try:
        f = doctools.SectionedFile(from_string=myfile)
        assert False, "Expected a BuildFailure"
    except BuildFailure:
        e = sys.exc_info()[1]
        assert str(e) == """section 'foo' redefined
(in file 'None', first section at line 2, second at line 6)""", \
        "error was: %s" % (str(e))

import sys

from unittest import SkipTest
from six import print_

from paver.easy import *
from paver import doctools, tasks, options

def test_sections_from_file():
    simpletext = """# [[[section foo]]]
"""
    f = doctools.SectionedFile(from_string=simpletext)
    assert len(f) == 1, "Sections found: %s" % f
    assert f['foo'] == "#Foo!\n", "Foo section contained: '%s'" % f['foo']

def display(msg, *args):
    print_(msg % args)

doctools.debug = display

def test_nested_sections():
    myfile = """
[[[section bar]]]
    Hi there.
    [[[section baz]]]
    Yo.
    [[[endsection]]]
[[[endsection]]]
"""
    f = doctools.SectionedFile(from_string=myfile)
    assert len(f) == 2
    assert f['bar'] == """    Hi there.
    Yo.
""", "Bar was: '%s'" % (f['bar'])
    assert f['bar.baz'] == """    Yo.
"""

def test_section_doesnt_end():
    myfile = """
[[[section bar]]]
Yo.
"""
    try:
        f = doctools.SectionedFile(from_string=myfile)
        assert False, "Expected a BuildFailure"
    except BuildFailure:
        e = sys.exc_info()[1]
        assert str(e) == """No end marker for section 'bar'
(in file 'None', starts at line 2)""", "error was: %s" % (str(e))

def test_section_already_defined():
    myfile = """
[[[section foo]]]
First one.
[[[endsection]]]

[[[section foo]]]
Second one.
[[[endsection]]]
"""

def test_endmarker_without_start():
    myfile = """
[[[section foo]]]
This is a good section.
[[[endsection]]]

[[[endsection]]]
"""
    try:
        f = doctools.SectionedFile(from_string=myfile)
        assert False, "Expected a BuildFailure"
    except BuildFailure:
        e = sys.exc_info()[1]
        assert str(e) == """End section marker with no starting marker
(in file 'None', at line 6)""", \
        "error was: %s" % (str(e))

def test_whole_file():
    myfile = """
[[[section bar]]]
    Hi there.
    [[[section baz]]]
    Yo.
    [[[endsection]]]
[[[endsection]]]
"""
    f = doctools.SectionedFile(from_string=myfile)
    assert f.all == """
    Hi there.
    Yo.
""", "All was: %s" % (f.all)

def test_bad_section():
    f = doctools.SectionedFile(from_string="")
    try:
        f['foo']
        assert False, "Should have a BuildFailure"
    except BuildFailure:
        e = sys.exc_info()[1]
        assert str(e) == "No section 'foo' in file 'None'", \
               "Error: '%s'" % (str(e))

def test_include_lookup():
    basedir = path(__file__).dirname() / "data"
    include = doctools.Includer(basedir, include_markers={})
    everything = include("t1.py")
    assert everything == """# file 't1.py'
print "Hi!"

print "More"
""", "Everything was: '%s'" % (everything)
    first = include("t1.py", "first")
    assert first == """# section 'first' in file 't1.py'
print "Hi!"
""", "First was '%s'" % (first)
    second = include("t2.py", "second.inner")
    assert second == """# section 'second.inner' in file 't2.py'
print sys.path
""", "Second was '%s'" % (second)

def test_cogging():
    if not paver.doctools.has_cog:
        raise SkipTest("Cog must be installed for this test")
    env = tasks.Environment(doctools)
    tasks.environment = env
    opt = env.options
    opt.cog = options.Bunch()
    basedir = path(__file__).dirname()
    opt.cog.basedir = basedir
    opt.cog.pattern = "*.rst"
    opt.cog.includedir = basedir / "data"
    env.options = opt
    doctools.cog()
    textfile = basedir / "data/textfile.rst"
    with open(textfile) as f:
        data = f.read()
    print_(data)
    assert "print sys.path" in data
    doctools.uncog()
    with open(textfile) as f:
        data = f.read()
    assert "print sys.path" not in data

def test_cogging_with_markers_removed():
    if not paver.doctools.has_cog:
        raise SkipTest("Cog must be installed for this test")
    env = tasks.Environment(doctools)
    tasks.environment = env
    opt = env.options
    opt.cog = Bunch()
    basedir = path(__file__).dirname()
    opt.cog.basedir = basedir
    opt.cog.pattern = "*.rst"
    opt.cog.includedir = basedir / "data"
    opt.cog.delete_code = True
    env.options = opt
    textfile = basedir / "data/textfile.rst"
    with open(textfile) as f:
        original_data = f.read()
    try:
        doctools.cog()
        with open(textfile) as f:
            data = f.read()
        print_(data)
        assert "[[[cog" not in data
    finally:
        with open(textfile, "w") as f:
            f.write(original_data)


from abapy.mesh import RegularQuadMesh
from abapy.indentation import IndentationMesh
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib.collections as collections
import mpl_toolkits.mplot3d as a3
import numpy as np
from matplotlib import cm
from scipy import interpolate

"""
  Vector function used to produced the displacement field.
  """
  r0 = 1.
  theta = .5 * np.pi * x
  r = y + r0
  ux = -x + r * np.cos(theta**2)
  uy = -y + r * np.sin(theta**2)
  uz = 0. * z
  return ux, uy, uz

def scalar_function(x, y, z, labels):
  """
  Scalar function used to produced the plotted field.
  """
  return x**2 + y**2
#MESH GENERATION
N1, N2 = 30, 30
l1, l2 = .75, 1.
m = RegularQuadMesh(N1 = N1, N2 = N2, l1 = l1, l2 = l2)
#FIELDS GENERATION
u = m.nodes.eval_vectorFunction(vector_function)
m.add_field(u, "u")
f = m.nodes.eval_function(scalar_function)
m.add_field(f, "f")
#PLOTS
fig = plt.figure(0)
plt.clf()
ax = fig.add_subplot(1,1,1)
m.draw(ax,
    disp_func  = lambda fields : fields["u"],
    field_func = lambda fields : fields["f"],
    cmap = cm.jet,
    cbar_orientation = "vertical",
    contour = False,
    contour_colors = "black",
    alpha = 1.,
    cmap_levels = 10,
    edge_width = .1)
ax.set_aspect("equal")
plt.grid()
plt.xlabel("$x$ position")
plt.ylabel("$y$ position")
plt.show()


"""
  Vector function used to produced the displacement field.
  """
  r0 = 1.
  theta = .5 * np.pi * x
  r = y + r0
  ux = -x + r * np.cos(theta**2)
  uy = -y + r * np.sin(theta**2)
  uz = 0. * z
  return ux, uy, uz

from abapy.mesh import RegularQuadMesh
from abapy.indentation import IndentationMesh
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib.collections as collections
import mpl_toolkits.mplot3d as a3
import numpy as np
from matplotlib import cm
from scipy import interpolate

def vector_function(x, y, z, labels):

def scalar_function(x, y, z, labels):
  """
  Scalar function used to produced the plotted field.
  """
  return x**2 + y**2
#MESH GENERATION
N1, N2 = 30, 30
l1, l2 = .75, 1.
m = RegularQuadMesh(N1 = N1, N2 = N2, l1 = l1, l2 = l2)
#FIELDS GENERATION
u = m.nodes.eval_vectorFunction(vector_function)
m.add_field(u, "u")
f = m.nodes.eval_function(scalar_function)
m.add_field(f, "f")
#PLOTS
fig = plt.figure(0)
plt.clf()
ax = fig.add_subplot(1,1,1)
m.draw(ax,
    disp_func  = lambda fields : fields["u"],
    field_func = lambda fields : fields["f"],
    cmap = cm.jet,
    cbar_orientation = "vertical",
    contour = False,
    contour_colors = "black",
    alpha = 1.,
    cmap_levels = 10,
    edge_width = .1)
ax.set_aspect("equal")
plt.grid()
plt.xlabel("$x$ position")
plt.ylabel("$y$ position")
plt.show()


from abapy.mesh import RegularQuadMesh
from abapy.indentation import IndentationMesh
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib.collections as collections
import mpl_toolkits.mplot3d as a3
import numpy as np
from matplotlib import cm
from scipy import interpolate

def vector_function(x, y, z, labels):
  """
  Vector function used to produced the displacement field.
  """
  r0 = 1.
  theta = .5 * np.pi * x
  r = y + r0
  u

  uy = -y + r * np.sin(theta**2)
  uz = 0. * z
  return ux, uy, uz

def scalar_function(x, y, z, labels):
  """
  Scalar function used to produced the plotted field.
  """
  return x**2 + y**2
#MESH GENERATION
N1, N2 = 30, 30
l1, l2 = .75, 1.
m = RegularQuadMesh(N1 = N1, N2 = N2, l1 = l1, l2 = l2)
#FIELDS GENERATION
u = m.nodes.eval_vectorFunction(vector_function)
m.add_field(u, "u")
f = m.nodes.eval_function(scalar_function)
m.add_field(f, "f")
#PLOTS
fig = plt.figure(0)
plt.clf()
ax = fig.add_subplot(1,1,1)
m.draw(ax,
    disp_func  = lambda fields : fields["u"],
    field_func = lambda fields : fields["f"],
    cmap = cm.jet,
    cbar_orientation = "vertical",
    contour = False,
    contour_colors = "black",
    alpha = 1.,
    cmap_levels = 10,
    edge_width = .1)
ax.set_aspect("equal")
plt.grid()
plt.xlabel("$x$ position")
plt.ylabel("$y$ position")
plt.show()


from abapy.mesh import RegularQuadMesh
from abapy.indentation import IndentationMesh
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib.collections as collections
import mpl_toolkits.mplot3d as a3
import numpy as np
from matplotlib import cm
from scipy import interpolate

def vector_function(x, y, z, labels):
  """
  Vector function used to produced the displacement field.
  """
  r0 = 1.
  theta = .5 * np.pi * x
  r = y + r0
  ux = -x + r * np.cos(theta**2)
  uy = -y + r * np.sin(theta**2)
  uz = 0. * z
  return ux, uy, uz

def scalar_function(x, y, z, labels):
  """
  Scalar function used to produced the plotted field.
  """

#MESH GENERATION
N1, N2 = 30, 30
l1, l2 = .75, 1.
m = RegularQuadMesh(N1 = N1, N2 = N2, l1 = l1, l2 = l2)
#FIELDS GENERATION
u = m.nodes.eval_vectorFunction(vector_function)
m.add_field(u, "u")
f = m.nodes.eval_function(scalar_function)
m.add_field(f, "f")
#PLOTS
fig = plt.figure(0)
plt.clf()
ax = fig.add_subplot(1,1,1)
m.draw(ax,
    disp_func  = lambda fields : fields["u"],
    field_func = lambda fields : fields["f"],
    cmap = cm.jet,
    cbar_orientation = "vertical",
    contour = False,
    contour_colors = "black",
    alpha = 1.,
    cmap_levels = 10,
    edge_width = .1)
ax.set_aspect("equal")
plt.grid()
plt.xlabel("$x$ position")
plt.ylabel("$y$ position")
plt.show()


import json
import os

from cachetools import LRUCache
from cachetools import cached

PROJECT_BASE = None

def get_project_base_directory():
    global PROJECT_BASE
    if PROJECT_BASE is None:
        PROJECT_BASE = os.path.abspath(
            os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, os.pardir))
    return PROJECT_BASE

@cached(cache=LRUCache(maxsize=10))

if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
    try:
        with open(json_conf_path) as f:
            return json.load(f)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

def dump_json_conf(config_data, conf_path):
    if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
    try:
        with open(json_conf_path, "w") as f:
            json.dump(config_data, f, indent=4)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

if __name__ == "__main__":
    print(get_project_base_directory())
    print(load_json_conf('conf/transfer_conf.json'))


if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
    try:
        with open(json_conf_path) as f:
            return json.load(f)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

import json
import os

from cachetools import LRUCache
from cachetools import cached

PROJECT_BASE = None

def get_project_base_directory():
    global PROJECT_BASE
    if PROJECT_BASE is None:
        PROJECT_BASE = os.path.abspath(
            os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, os.pardir))
    return PROJECT_BASE

@cached(cache=LRUCache(maxsize=10))
def load_json_conf(conf_path):

def dump_json_conf(config_data, conf_path):
    if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
    try:
        with open(json_conf_path, "w") as f:
            json.dump(config_data, f, indent=4)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

if __name__ == "__main__":
    print(get_project_base_directory())
    print(load_json_conf('conf/transfer_conf.json'))


import json
import os

from cachetools import LRUCache
from cachetools import cached

PROJECT_BASE = None

def get_project_base_directory():
    global PROJECT_BASE
    if PROJECT_BASE is None:
        PROJECT_BASE = os.path.abspath(
            os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, os.pardir))
    return PROJECT_BAS

@cached(cache=LRUCache(maxsize=10))
def load_json_conf(conf_path):
    if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
    try:
        with open(json_conf_path) as f:
            return json.load(f)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

def dump_json_conf(config_data, conf_path):
    if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)
    try:
        with open(json_conf_path, "w") as f:
            json.dump(config_data, f, indent=4)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

if __name__ == "__main__":
    print(get_project_base_directory())
    print(load_json_conf('conf/transfer_conf.json'))


try:
        with open(json_conf_path) as f:
            return json.load(f)
    except:
        raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path))

import json
import os

from cachetools import LRUCache
from cachetools import cached

PROJECT_BASE = None

def get_project_base_directory():
    global PROJECT_BASE
    if PROJECT_BASE is None:
        PROJECT_BASE = os.path.abspath(
            os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir, os.pardir))
    return PROJECT_BASE

@cached(cache=LRUCache(maxsize=10))
def load_json_conf(conf_path):
    if os.path.isabs(conf_path):
        json_conf_path = conf_path
    else:
        json_conf_path = os.path.join(get_project_base_directory(), conf_path)


# day8 异常处理
# 陈浩 学号 1901100030

sample_text = '''
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense. Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambxiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
'''
cn_text = '''
14天python训练营欢迎您!
锦瑟无端五十弦
一弦一柱思华年
沧海月明珠有泪
蓝田日暖玉生烟
'''
# 定义字母统计函数，返回降序排列数组
def stats_text_en(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
    elements = text.split()
    # 定义新存储变量
    words = []
    # 定义待剔除的符号
    symbols = ",.*-!"
    # 遍历所有元素，剔除特殊符号
    for element in elements:
        for symbol in symbols:
            element = element.replace(symbol,"")
        if len(element) and element.isascii():
            words.append(element)
    # 初始化字典
    counter = {}
    # 定义集合，剔除重复元素，降低遍历次数
    words_set = set(words)
    #根据单词进行遍历、统计
    for word in words_set:
        counter[word] = words.count(word)
    # 按照出现次数，进行排序，并输出
    return sorted(counter.items(), key=lambda x:x[1], reverse=True )

# 统计参数中每个汉字出现的次数
def stats_text_cn(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
    # 定义新存储变量
    cn_characters = []
    #遍历字符串，取出所有汉字
    for character in text:
        if "\u4e00" <= character <= "\u9efff":
            cn_characters.append(character)
    # 初始化字典
    counter = {}
    # 定义集合，剔除重复元素，降低遍历次数
    characters_set = set(cn_characters)
    #根据单词进行遍历、统计
    for character in characters_set:
        counter[character] = cn_characters.count(character)
    # 按照出现次数，进行排序，并输出
    return sorted(counter.items(), key=lambda x:x[1], reverse=True )

# 添加stats_text函数，调用stats_text_en和stats_text_cn函数，合并输出统计结果

if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
#   返回合并输出结果
    return stats_text_cn(text) + stats_text_en(text)

#测试程序


if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
#   返回合并输出结果
    return stats_text_cn(text) + stats_text_en(text)

# day8 异常处理
# 陈浩 学号 1901100030

sample_text = '''
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense. Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambxiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
'''
cn_text = '''
14天python训练营欢迎您!
锦瑟无端五十弦
一弦一柱思华年
沧海月明珠有泪
蓝田日暖玉生烟
'''
# 定义字母统计函数，返回降序排列数组
def stats_text_en(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
    elements = text.split()
    # 定义新存储变量
    words = []
    # 定义待剔除的符号
    symbols = ",.*-!"
    # 遍历所有元素，剔除特殊符号
    for element in elements:
        for symbol in symbols:
            element = element.replace(symbol,"")
        if len(element) and element.isascii():
            words.append(element)
    # 初始化字典
    counter = {}
    # 定义集合，剔除重复元素，降低遍历次数
    words_set = set(words)
    #根据单词进行遍历、统计
    for word in words_set:
        counter[word] = words.count(word)
    # 按照出现次数，进行排序，并输出
    return sorted(counter.items(), key=lambda x:x[1], reverse=True )

# 统计参数中每个汉字出现的次数
def stats_text_cn(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
    # 定义新存储变量
    cn_characters = []
    #遍历字符串，取出所有汉字
    for character in text:
        if "\u4e00" <= character <= "\u9efff":
            cn_characters.append(character)
    # 初始化字典
    counter = {}
    # 定义集合，剔除重复元素，降低遍历次数
    characters_set = set(cn_characters)
    #根据单词进行遍历、统计
    for character in characters_set:
        counter[character] = cn_characters.count(character)
    # 按照出现次数，进行排序，并输出
    return sorted(counter.items(), key=lambda x:x[1], reverse=True )

# 添加stats_text函数，调用stats_text_en和stats_text_cn函数，合并输出统计结果
def stats_text(text):


# day8 异常处理
# 陈浩 学号 1901100030

sample_text = '''
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense. Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambxiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
'''
cn_text = '''
14天python训练营欢迎您!
锦瑟无端五十弦
一弦一柱思华年
沧海月明珠有泪
蓝田日暖玉生烟
'''
# 定义字母统计函数，返回降序排列数组
def stats_text_en(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
    elements = text.split()
    # 定义新存储变量
    words = []
    # 定义待剔除的符号
    symbols = ",.*-!"
    # 遍历所有元素，剔除特殊符号
    for element in elements:
        for symbol in symbols:
            element = element.replace(symbol,"")
        if len(element) and element.isascii():
            words.append(element)
    # 初始化字典
    counter = {}
    # 定义集合，剔除重复元素，降低遍历次数
    words_set = set(words)
    #根据单词进行遍历、统计
    for word in words_set:
        counter[word] = words.count(word)
    # 按照出现次数，进行排序，并输出
    return sorted(counter.items(), key=lambda x:x[1], reverse=True )

# 统计参数中每个汉字出现的次数
def stats_text_cn(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
    # 定义新存储变量
    cn_characters = []
    #遍历字符串，取出所有汉字
    for character in text:
        if "\u4e00" <= character <= "\u9efff":
            cn_characters.append(character)
    # 初始化字典
    counter = {

    # 定义集合，剔除重复元素，降低遍历次数
    characters_set = set(cn_characters)
    #根据单词进行遍历、统计
    for character in characters_set:
        counter[character] = cn_characters.count(character)
    # 按照出现次数，进行排序，并输出
    return sorted(counter.items(), key=lambda x:x[1], reverse=True )

# 添加stats_text函数，调用stats_text_en和stats_text_cn函数，合并输出统计结果
def stats_text(text):
    if not isinstance(text, str):
        raise ValueError("参数必须是str类型，输入类型 %s" %type(text))
#   返回合并输出结果
    return stats_text_cn(text) + stats_text_en(text)

#测试程序


def __init__(self,
                 unique_id,
                 example_index,
                 doc_span_index,
                 tokens,
                 token_to_orig_map,
                 token_is_max_context,
                 input_ids,
                 input_mask,
                 segment_ids,
                 start_position=None,
                 end_position=None,
                 history_answer_marker=None,
                 metadata=None):

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import json
import math
import os
import modeling
import optimization
import tokenization
import six
import tensorflow as tf
from copy import deepcopy

from cqa_flags import FLAGS
from cqa_supports import *
from cqa_model import *
from cqa_gen_batches import *

class CQAExample(object):
    """A single training/test example."""

    def __init__(self,
                 qas_id,
                 question_text,
                 doc_tokens,
                 orig_answer_text=None,
                 start_position=None,
                 end_position=None,
                 history_answer_marker=None,
                 metadata=None):
        self.qas_id = qas_id
        self.question_text = question_text
        self.doc_tokens = doc_tokens
        self.orig_answer_text = orig_answer_text
        self.start_position = start_position
        self.end_position = end_position
        self.history_answer_marker = history_answer_marker
        self.metadata = metadata

    def __str__(self):
        return self.__repr__()

    def __repr__(self):
        s = ""
        s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
        s += ", question_text: %s" % (
            tokenization.printable_text(self.question_text))
        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
        if self.start_position:
            s += ", start_position: %d" % (self.start_position)
        if self.start_position:
            s += ", end_position: %d" % (self.end_position)
        if self.history_answer_marker:
            s += ', history_answer_marker: {}'.format(json.dumps(self.history_answer_marker))
        if self.metadata:
            s += ', metadata: ' + json.dumps(self.metadata)
        return s

class InputFeatures(object):
    """A single set of features of data."""

self.unique_id = unique_id
        self.example_index = example_index
        self.doc_span_index = doc_span_index
        self.tokens = tokens
        self.token_to_orig_map = token_to_orig_map
        self.token_is_max_context = token_is_max_context
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.start_position = start_position
        self.end_position = end_position
        self.history_answer_marker = history_answer_marker
        self.metadata = metadata

def read_quac_examples(input_file, is_training):
    """Read a QuAC json file into a list of CQAExample."""
    with tf.gfile.Open(input_file, "r") as reader:
        input_data = json.load(reader)["data"]

    def is_whitespace(c):
        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
            return True
        return False

    examples = []
    if FLAGS.load_small_portion:
        input_data = input_data[:10]
        tf.logging.warning('<<<<<<<<<< load_small_portion is on! >>>>>>>>>>')
    for entry in input_data:
        # An additional "CANNOTANSWER" has been added in QuAC data, so no need to append one.
        entry = entry['paragraphs'][0]
        paragraph_text = entry["context"]
        doc_tokens = []
        char_to_word_offset = []
        prev_is_whitespace = True
        for c in paragraph_text:
            if is_whitespace(c):
                prev_is_whitespace = True
            else:
                if prev_is_whitespace:
                    doc_tokens.append(c)
                else:
                    doc_tokens[-1] += c
                prev_is_whitespace = False
            char_to_word_offset.append(len(doc_tokens) - 1)

        # convert the convasational QAs to squad format, with history

        questions = [(item['question'], item['id']) for item in entry['qas']] # [(question, question_id), ()]
        answers = [(item['orig_answer']['text'], item['orig_answer']['answer_start']) for item in entry['qas']]
        followups = [item['followup'] for item in entry['qas']]
        yesnos = [item['yesno'] for item in entry['qas']]

        qas = []
        for i, (question, answer, followup, yesno) in enumerate(zip(questions, answers, followups, yesnos)):
            metadata = {'turn': i + 1, 'history_turns': [], 'tok_history_answer_markers':[],
                        'followup': followup, 'yesno': yesno, 'history_turns_text': []}

            end_index = i
            question_with_histories = ''

            history_answer_marker = None

            start_index = 0 # we read all the histories no matter we use RL or not. we will make approporiate selections afterwards
            history_answer_marker = []
            for history_turn, (each_answer, each_question) in enumerate(
                zip(answers[start_index: end_index], questions[start_index: end_index])):

                each_marker = [each_answer[1], each_answer[1] + len(each_answer[0]), each_answer[0]]
                history_answer_marker.append(each_marker)
                metadata['history_turns'].append(history_turn + start_index + 1)
                metadata['history_turns_text'].append((each_question[0], each_answer[0])) #[(q1, a1), (q2, a2), ...]

            # add the current question
            question_with_histories += question[0]
            qas.append({'id': question[1], 'question': question_with_histories, 'answers': [{'answer_start': answer[1], 'text': answer[0]}],
                        'history_answer_marker': history_answer_marker, 'metadata': metadata})

        for qa in qas:
            qas_id = qa["id"]
            question_text = qa["question"]
            start_position = None
            end_position = None
            orig_answer_text = None

            # we read in the groundtruth answer bothing druing training and predicting, because we need to compute acc and f1 at predicting time.
            if len(qa["answers"]) != 1:
                raise ValueError(
                    "For training, each question should have exactly 1 answer.")
            answer = qa["answers"][0]
            orig_answer_text = answer["text"]
            answer_offset = answer["answer_start"]
            answer_length = len(orig_answer_text)
            start_position = char_to_word_offset[answer_offset]
            end_position = char_to_word_offset[answer_offset + answer_length - 1]
            # Only add answers where the text can be exactly recovered from the
            # document. If this CAN'T happen it's likely due to weird Unicode
            # stuff so we will just skip the example.
            # Note that this means for training mode, every example is NOT
            # guaranteed to be preserved.
            actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
            cleaned_answer_text = " ".join(
                tokenization.whitespace_tokenize(orig_answer_text))

            if is_training and actual_text.find(cleaned_answer_text) == -1:
                tf.logging.warning("Could not find answer: '%s' vs. '%s'",
                                   actual_text, cleaned_answer_text)
                continue

            # we construct a tok_history_answer_marker to store the aggregated history answer markers for a question.
            # we also construct each_tok_history_answer_marker to store a single history answer marker.
            tok_history_answer_marker = [0] * len(doc_tokens)

            for marker_index, marker in enumerate(qa['history_answer_marker']):
                each_tok_history_answer_marker = [0] * len(doc_tokens)
                history_orig_answer_text = marker[2]
                history_answer_offset = marker[0]
                history_answer_length = len(history_orig_answer_text)
                history_start_position = char_to_word_offset[history_answer_offset]
                history_end_position = char_to_word_offset[history_answer_offset + history_answer_length - 1]
                history_actual_text = " ".join(doc_tokens[history_start_position:(history_end_position + 1)])
                history_cleaned_answer_text = " ".join(tokenization.whitespace_tokenize(history_orig_answer_text))
                if history_actual_text.find(history_cleaned_answer_text) != -1:
                    tok_history_answer_marker = tok_history_answer_marker[: history_start_position] + \
                                        [1] * (history_end_position - history_start_position + 1) + \
                                        tok_history_answer_marker[history_end_position + 1 :]
                    each_tok_history_answer_marker = each_tok_history_answer_marker[: history_start_position] + \
                                        [1] * (history_end_position - history_start_position + 1) + \
                                        each_tok_history_answer_marker[history_end_position + 1 :]
                    assert len(tok_history_answer_marker) == len(doc_tokens)
                    assert len(each_tok_history_answer_marker) == len(doc_tokens)
                    qa['metadata']['tok_history_answer_markers'].append(each_tok_history_answer_marker)
                else:
                    tf.logging.warning("Could not find history answer: '%s' vs. '%s'", history_actual_text, history_cleaned_answer_text)

            example = CQAExample(
                qas_id=qas_id,
                question_text=question_text,
                doc_tokens=doc_tokens,
                orig_answer_text=orig_answer_text,
                start_position=start_position,
                end_position=end_position,
                history_answer_marker=tok_history_answer_marker,
                metadata=qa['metadata'])
            examples.append(example)
    return examples

def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                 doc_stride, max_query_length, is_training):
    """Loads a data file into a list of `InputBatch`s."""

    unique_id = 1000000000

    features = []
    for (example_index, example) in enumerate(examples):
        metadata = example.metadata
        query_tokens = tokenizer.tokenize(example.question_text)

        if len(query_tokens) > max_query_length:
            query_tokens = query_tokens[0:max_query_length]

        history_answer_marker = example.history_answer_marker
        tok_to_orig_index = []
        orig_to_tok_index = []
        all_doc_tokens = []
        all_history_answer_marker = []
        for (i, token) in enumerate(example.doc_tokens):
            orig_to_tok_index.append(len(all_doc_tokens))
            sub_tokens = tokenizer.tokenize(token)
            for sub_token in sub_tokens:
                tok_to_orig_index.append(i)
                all_doc_tokens.append(sub_token)
                all_history_answer_marker.append(history_answer_marker[i])

        tok_start_position = None
        tok_end_position = None

        # we do this for both training and predicting, because we need also start/end position at testing time to compute acc and f1
        tok_start_position = orig_to_tok_index[example.start_position]
        if example.end_position < len(example.doc_tokens) - 1:
            tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
        else:
            tok_end_position = len(all_doc_tokens) - 1
        (tok_start_position, tok_end_position) = _improve_answer_span(
            all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
            example.orig_answer_text)

        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3

        # We can have documents that are longer than the maximum sequence length.
        # To deal with this we do a sliding window approach, where we take chunks
        # of the up to our max length with a stride of `doc_stride`.
        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
            "DocSpan", ["start", "length"])
        doc_spans = []
        start_offset = 0
        while start_offset < len(all_doc_tokens):
            length = len(all_doc_tokens) - start_offset
            if length > max_tokens_for_doc:
                length = max_tokens_for_doc
            doc_spans.append(_DocSpan(start=start_offset, length=length))
            if start_offset + length == len(all_doc_tokens):
                break
            start_offset += min(length, doc_stride)

        for (doc_span_index, doc_span) in enumerate(doc_spans):
            marker = []
            tokens = []
            token_to_orig_map = {}
            token_is_max_context = {}
            segment_ids = []
            tokens.append("[CLS]")
            marker.append(0)
            segment_ids.append(0)
            for token in query_tokens:
                tokens.append(token)
                marker.append(0)
                segment_ids.append(0)
            tokens.append("[SEP]")
            marker.append(0)
            segment_ids.append(0)

            for i in range(doc_span.length):
                split_token_index = doc_span.start + i
                token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]

                is_max_context = _check_is_max_context(doc_spans, doc_span_index,
                                                       split_token_index)
                token_is_max_context[len(tokens)] = is_max_context
                tokens.append(all_doc_tokens[split_token_index])
                marker.append(all_history_answer_marker[split_token_index])
                segment_ids.append(1)
            tokens.append("[SEP]")
            marker.append(0)
            segment_ids.append(1)

            input_ids = tokenizer.convert_tokens_to_ids(tokens)

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            input_mask = [1] * len(input_ids)

            # Zero-pad up to the sequence length.
            while len(input_ids) < max_seq_length:
                input_ids.append(0)
                input_mask.append(0)
                segment_ids.append(0)
                marker.append(0)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length
            assert len(marker) == max_seq_length

            start_position = None
            end_position = None
            if is_training:
                # For training, if our document chunk does not contain an annotation
                # we throw it out, since there is nothing to predict.
                doc_start = doc_span.start
                doc_end = doc_span.start + doc_span.length - 1
                if (example.start_position < doc_start or
                    example.end_position < doc_start or
                    example.start_position > doc_end or example.end_position > doc_end):
                    continue

                doc_offset = len(query_tokens) + 2
                start_position = tok_start_position - doc_start + doc_offset
                end_position = tok_end_position - doc_start + doc_offset
            else:
                # when predicting, we donot throw out any doc span to prevent label leaking
                doc_start = doc_span.start
                doc_end = doc_span.start + doc_span.length - 1
                doc_offset = len(query_tokens) + 2
                start_position = tok_start_position - doc_start + doc_offset
                end_position = tok_end_position - doc_start + doc_offset

            features.append(
                InputFeatures(
                    unique_id=unique_id,
                    example_index=example_index,
                    doc_span_index=doc_span_index,
                    tokens=tokens,
                    token_to_orig_map=token_to_orig_map,
                    token_is_max_context=token_is_max_context,
                    input_ids=input_ids,
                    input_mask=input_mask,
                    segment_ids=segment_ids,
                    start_position=start_position,
                    end_position=end_position,
                    history_answer_marker=marker,
                    metadata=metadata))
            unique_id += 1

    return features

def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
                         orig_answer_text):
    """Returns tokenized answer spans that better match the annotated answer."""

    # The SQuAD annotations are character based. We first project them to
    # whitespace-tokenized words. But then after WordPiece tokenization, we can
    # often find a "better match". For example:
    #   Question: What year was John Smith born?
    #   Context: The leader was John Smith (1895-1943).
    # The original whitespace-tokenized answer will be "(1895-1943).". However
    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
    # the exact answer, 1895.
    # However, this is not always possible. Consider the following:
    #   Question: What country is the top exporter of electornics?
    #   Context: The Japanese electronics industry is the lagest in the world.
    #   Answer: Japan
    # In this case, the annotator chose "Japan" as a character sub-span of
    # the word "Japanese". Since our WordPiece tokenizer does not split
    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
    # in SQuAD, but does happen.
    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))

    for new_start in range(input_start, input_end + 1):
        for new_end in range(input_end, new_start - 1, -1):
            text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
            if text_span == tok_answer_text:
                return (new_start, new_end)

    return (input_start, input_end)

def _check_is_max_context(doc_spans, cur_span_index, position):
    """Check if this is the 'max context' doc span for the token."""

    # Because of the sliding window approach taken to scoring documents, a single
    # token can appear in multiple documents. E.g.
    #  Doc: the man went to the store and bought a gallon of milk
    #  Span A: the man went to the
    #  Span B: to the store and bought
    #  Span C: and bought a gallon of
    # Now the word 'bought' will have two scores from spans B and C. We only
    # want to consider the score with "maximum context", which we define as
    # the *minimum* of its left and right context (the *sum* of left and
    # right context will always be the same, of course).
    # In the example the maximum context for 'bought' would be span C since
    # it has 1 left context and 3 right context, while span B has 4 left context
    best_score = None
    best_span_index = None
    for (span_index, doc_span) in enumerate(doc_spans):
        end = doc_span.start + doc_span.length - 1
        if position < doc_span.start:
            continue
        if position > end:
            continue
        num_left_context = position - doc_span.start
        num_right_context = end - position
        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
        if best_score is None or score > best_score:
            best_score = score
            best_span_index = span_index

    return cur_span_index == best_span_index

def write_predictions(all_examples, all_features, all_results, n_best_size,
                      max_answer_length, do_lower_case, output_prediction_file,
                      output_nbest_file, for_reward=False):
    """Write final predictions to the json file."""
    tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
    tf.logging.info("Writing nbest to: %s" % (output_nbest_file))

    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
        example_index_to_features[feature.example_index].append(feature)

    unique_id_to_result = {}
    for result in all_results:
        unique_id_to_result[result.unique_id] = result

    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
        "PrelimPrediction",
        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])

    all_predictions = collections.OrderedDict()
    all_nbest_json = collections.OrderedDict()
    for (example_index, example) in enumerate(all_examples):
        features = example_index_to_features[example_index]

        prelim_predictions = []
        for (feature_index, feature) in enumerate(features):
            result = unique_id_to_result[feature.unique_id]

            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
            for start_index in start_indexes:
                for end_index in end_indexes:
                    # We could hypothetically create invalid predictions, e.g., predict
                    # that the start of the span is in the question. We throw out all
                    if start_index >= len(feature.tokens):
                        continue
                    if end_index >= len(feature.tokens):
                        continue
                    if start_index not in feature.token_to_orig_map:
                        continue
                    if end_index not in feature.token_to_orig_map:
                        continue
                    if not feature.token_is_max_context.get(start_index, False):
                        continue
                    if end_index < start_index:
                        continue
                    length = end_index - start_index + 1
                    if length > max_answer_length:
                        continue
                    prelim_predictions.append(
                        _PrelimPrediction(
                            feature_index=feature_index,
                            start_index=start_index,
                            end_index=end_index,
                            start_logit=result.start_logits[start_index],
                            end_logit=result.end_logits[end_index]))

        prelim_predictions = sorted(
            prelim_predictions,
            key=lambda x: (x.start_logit + x.end_logit),
            reverse=True)

        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
            "NbestPrediction", ["text", "start_logit", "end_logit"])

        seen_predictions = {}
        nbest = []
        for pred in prelim_predictions:
            if len(nbest) >= n_best_size:
                break
            feature = features[pred.feature_index]

            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
            orig_doc_start = feature.token_to_orig_map[pred.start_index]
            orig_doc_end = feature.token_to_orig_map[pred.end_index]
            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
            tok_text = " ".join(tok_tokens)

            # De-tokenize WordPieces that have been split off.
            tok_text = tok_text.replace(" ##", "")
            tok_text = tok_text.replace("##", "")

            # Clean whitespace
            tok_text = tok_text.strip()
            tok_text = " ".join(tok_text.split())
            orig_text = " ".join(orig_tokens)

            final_text = get_final_text(tok_text, orig_text, do_lower_case)
            if final_text in seen_predictions:
                continue

            seen_predictions[final_text] = True
            nbest.append(
                _NbestPrediction(
                    text=final_text,
                    start_logit=pred.start_logit,
                    end_logit=pred.end_logit))

        # In very rare edge cases we could have no valid predictions. So we
        # just create a nonce prediction in this case to avoid failure.
        if not nbest:
            if for_reward:
                continue
            if FLAGS.dataset.lower() == 'coqa':
                nbest.append(_NbestPrediction(text="unknown", start_logit=0.0, end_logit=0.0))
            elif FLAGS.dataset.lower() == 'quac':
                nbest.append(_NbestPrediction(text="invalid", start_logit=0.0, end_logit=0.0))

        assert len(nbest) >= 1

        total_scores = []
        for entry in nbest:
            total_scores.append(entry.start_logit + entry.end_logit)

        probs = _compute_softmax(total_scores)

        nbest_json = []
        for (i, entry) in enumerate(nbest):
            output = collections.OrderedDict()
            output["text"] = entry.text
            output["probability"] = probs[i]
            output["start_logit"] = entry.start_logit
            output["end_logit"] = entry.end_logit
            nbest_json.append(output)

        assert len(nbest_json) >= 1

        all_predictions[example.qas_id] = nbest_json[0]["text"]
        all_nbest_json[example.qas_id] = nbest_json

    with tf.gfile.GFile(output_prediction_file, "w") as writer:
        # convert to official evaluation format
        if FLAGS.dataset.lower() == 'coqa':
            converted = []
            for key, value in all_predictions.items():
                converted.append({'id': key[:30], 'turn_id': int(key[30:]), 'answer': value})
                writer.write(json.dumps(converted, indent=4) + "\n")
        elif FLAGS.dataset.lower() == 'quac':
            converted = {}
            for key, value in all_predictions.items():
                dialog_id = key[:36]
                if dialog_id not in converted:
                    converted[dialog_id] = {'best_span_str': [], 'qid': [], 'followup': [], 'yesno': []}
                converted[dialog_id]['best_span_str'].append(value)
                converted[dialog_id]['qid'].append(key)
                converted[dialog_id]['followup'].append('x')
                converted[dialog_id]['yesno'].append('y')
            for key, value in converted.items():
                writer.write(json.dumps(value) + '\n')

    with tf.gfile.GFile(output_nbest_file, "w") as writer:
        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")

def get_final_text(pred_text, orig_text, do_lower_case):
    """Project the tokenized prediction back to the original text."""

    # When we created the data, we kept track of the alignment between original
    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
    # now `orig_text` contains the span of our original text corresponding to the
    # span that we predicted.
    # However, `orig_text` may contain extra characters that we don't want in
    # our prediction.
    # For example, let's say:
    #   pred_text = steve smith
    #   orig_text = Steve Smith's
    # We don't want to return `orig_text` because it contains the extra "'s".
    # We don't want to return `pred_text` because it's already been normalized
    # (the SQuAD eval script also does punctuation stripping/lower casing but
    # our tokenizer does additional normalization like stripping accent
    # What we really want to return is "Steve Smith".
    # Therefore, we have to apply a semi-complicated alignment heruistic between
    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
    # can fail in certain cases in which case we just return `orig_text`.

    def _strip_spaces(text):
        ns_chars = []
        ns_to_s_map = collections.OrderedDict()
        for (i, c) in enumerate(text):
            if c == " ":
                continue
            ns_to_s_map[len(ns_chars)] = i
            ns_chars.append(c)
        ns_text = "".join(ns_chars)
        return (ns_text, ns_to_s_map)

    # We first tokenize `orig_text`, strip whitespace from the result
    # and `pred_text`, and check if they are the same length. If they are
    # NOT the same length, the heuristic has failed. If they are the same
    # length, we assume the characters are one-to-one aligned.
    tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)

    tok_text = " ".join(tokenizer.tokenize(orig_text))

    start_position = tok_text.find(pred_text)
    if start_position == -1:
        if FLAGS.verbose_logging:
            tf.logging.info(
                "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
        return orig_text
    end_position = start_position + len(pred_text) - 1

    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)

    if len(orig_ns_text) != len(tok_ns_text):
        if FLAGS.verbose_logging:
            tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
                            orig_ns_text, tok_ns_text)
        return orig_text

    # We then project the characters in `pred_text` back to `orig_text` using
    # the character-to-character alignment.
    tok_s_to_ns_map = {}
    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
        tok_s_to_ns_map[tok_index] = i

    orig_start_position = None
    if start_position in tok_s_to_ns_map:
        ns_start_position = tok_s_to_ns_map[start_position]
        if ns_start_position in orig_ns_to_s_map:
            orig_start_position = orig_ns_to_s_map[ns_start_position]

    if orig_start_position is None:
        if FLAGS.verbose_logging:
            tf.logging.info("Couldn't map start position")
        return orig_text

    orig_end_position = None
    if end_position in tok_s_to_ns_map:
        ns_end_position = tok_s_to_ns_map[end_position]
        if ns_end_position in orig_ns_to_s_map:
            orig_end_position = orig_ns_to_s_map[ns_end_position]

    if orig_end_position is None:
        if FLAGS.verbose_logging:
            tf.logging.info("Couldn't map end position")
        return orig_text

    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
    return output_text

def _get_best_indexes(logits, n_best_size):
    """Get the n-best logits from a list."""
    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)

    best_indexes = []
    for i in range(len(index_and_score)):
        if i >= n_best_size:
            break
        best_indexes.append(index_and_score[i][0])
    return best_indexes

def _compute_softmax(scores):
    """Compute softmax probability over raw logits."""
    if not scores:
        return []

    max_score = None
    for score in scores:
        if max_score is None or score > max_score:
            max_score = score

    exp_scores = []
    total_sum = 0.0
    for score in scores:
        x = math.exp(score - max_score)
        exp_scores.append(x)
        total_sum += x

    probs = []
    for score in exp_scores:
        probs.append(score / total_sum)
    return probs

def convert_features_to_feed_dict(features):
    batch_unique_ids, batch_input_ids, batch_input_mask = [], [], []
    batch_segment_ids, batch_start_positions, batch_end_positions, batch_history_answer_marker = [], [], [], []

    for feature in features:
        batch_unique_ids.append(feature.unique_id)
        batch_input_ids.append(feature.input_ids)
        batch_input_mask.append(feature.input_mask)
        batch_segment_ids.append(feature.segment_ids)
        batch_start_positions.append(feature.start_position)
        batch_end_positions.append(feature.end_position)
        batch_history_answer_marker.append(feature.history_answer_marker)

    feed_dict = {'unique_ids': batch_unique_ids, 'input_ids': batch_input_ids,
              'input_mask': batch_input_mask, 'segment_ids': batch_segment_ids,
              'start_positions': batch_start_positions, 'end_positions': batch_end_positions,
              'history_answer_marker': batch_history_answer_marker}
    return feed_dict

def convert_examples_to_example_variations(examples, max_considered_history_turns):
    # an example variation is "question + passage + markers (M3)"
    # meaning that we only have one marker for each example variation
    # because we want to make a binary choice for every example variation,
    # and combine all variations to form an example

    new_examples = []
    for example in examples:
        # if the example is the first question in the dialog, it does not contain history answers,
        # so we simply append it.
        if len(example.metadata['tok_history_answer_markers']) == 0:
            example.metadata['history_turns'] = []
            new_examples.append(example)
        else:
            for history_turn, marker, history_turn_text in zip(
                    example.metadata['history_turns'][- max_considered_history_turns:],
                    example.metadata['tok_history_answer_markers'][- max_considered_history_turns:],
                    example.metadata['history_turns_text'][- max_considered_history_turns:]):
                each_new_example = deepcopy(example)
                each_new_example.history_answer_marker = marker
                each_new_example.metadata['history_turns'] = [history_turn]
                each_new_example.metadata['tok_history_answer_markers'] = [marker]
                each_new_example.metadata['history_turns_text'] = [history_turn_text]
                new_examples.append(each_new_example)
    return new_examples

def convert_examples_to_variations_and_then_features(examples, tokenizer, max_seq_length,
                                doc_stride, max_query_length, max_considered_history_turns, is_training):
    # different from the "convert_examples_to_features" in cqa_supports.py, we return two masks with the feature (example/variaton trackers).
    # the first mask is the example index, and the second mask is the variation index. Wo do this to keep track of the features generated
    # by different examples and variations.

    all_features = []
    example_features_nums = [] # keep track of how many features are generated from the same example (regardless of example variations)
    example_tracker = []
    variation_tracker = []
    unique_id = 1000000000

    # when training, we shuffle the data for more stable training.
    # we shuffle here so that we do not need to shuffle when generating batches
    num_examples = len(examples)
    if is_training:
        np.random.seed(0)
        idx = np.random.permutation(num_examples)
        examples_shuffled = np.asarray(examples)[idx]
    else:
        examples_shuffled = np.asarray(examples)

    for example_index, example in enumerate(examples_shuffled):
        example_features_num = []
        variations = convert_examples_to_example_variations([example], max_considered_history_turns)
        for variation_index, variation in enumerate(variations):
            features = convert_examples_to_features([variation], tokenizer, max_seq_length, doc_stride, max_query_length, is_training)

            # the example_index and unique_id in features are wrong due to the generation of example variations.
            # we fix them here.
            for i in range(len(features)):
                features[i].example_index = example_index
                features[i].unique_id = unique_id
                unique_id += 1
            all_features.extend(features)
            variation_tracker.extend([variation_index] * len(features))
            example_tracker.extend([example_index] * len(features))
            example_features_num.append(len(features))
        # every variation of the same example should generate the same amount of features
        assert len(set(example_features_num)) == 1
        example_features_nums.append(example_features_num[0])
    assert len(all_features) == len(example_tracker)
    assert len(all_features) == len(variation_tracker)
    return all_features, example_tracker, variation_tracker, example_features_nums

def get_selected_example_features_without_actions(batch_features, batch_example_tracker, batch_variation_tracker):

    prev_e_tracker, prev_v_tracker = None, None
    relative_selected_pos = [] # the relative position of the selected history compared to the current turn
    handled_variations = {} # each example variation could have more than one features, but we only store the "relative_selected_pos" once
    f_tracker = 0 # feature tracker, denotes the feature index for each variation

    selected_example_features_dict = {}
    for feature, e_tracker, v_tracker in zip(batch_features, batch_example_tracker, batch_variation_tracker):
        # get the f_tracker
        if e_tracker == prev_e_tracker and v_tracker == prev_v_tracker:
            f_tracker += 1
        else:
            f_tracker = 0
        prev_e_tracker, prev_v_tracker = e_tracker, v_tracker

        # we append a feature that is without any history, so we can build upon this if other histories are chosen
        if e_tracker not in selected_example_features_dict:
            feature_without_marker = deepcopy(feature)
            feature_without_marker.history_answer_marker = np.zeros(len(feature.history_answer_marker))
            feature_without_marker.metadata['history_turns'] = []
            selected_example_features_dict[e_tracker] = [feature_without_marker]
        elif f_tracker >= len(selected_example_features_dict[e_tracker]):
            feature_without_marker = deepcopy(feature)
            feature_without_marker.history_answer_marker = np.zeros(len(feature.history_answer_marker))
            feature_without_marker.metadata['history_turns'] = []
            selected_example_features_dict[e_tracker].append(feature_without_marker)

        # add history answer markers, a turn is selected if it either in immediate previous j turns or have large tfidf score
        if len(feature.metadata['history_turns']) > 0 and \
             (feature.metadata['history_turns'][0] >= feature.metadata['turn'] - FLAGS.history):

            curr_feature = deepcopy(selected_example_features_dict[e_tracker][f_tracker])

            curr_feature.history_answer_marker = curr_feature.history_answer_marker + \
                                                 np.asarray(feature.history_answer_marker)
            # when the markers have overlaps, the corresponding bit would be 2 after added together. we need to set it to one.
            curr_feature.history_answer_marker = curr_feature.history_answer_marker != 0
            curr_feature.history_answer_marker = curr_feature.history_answer_marker.astype(int)

            curr_feature.metadata['history_turns'].append(feature.metadata['history_turns'][0])
            selected_example_features_dict[e_tracker][f_tracker] = curr_feature

            if (e_tracker, v_tracker) not in handled_variations:
                # we always choose the current turn
                relative_selected_pos.append(0)

                handled_variations[(e_tracker, v_tracker)] = 1
                if len(feature.metadata['history_turns']) > 0:
                    relative_selected_pos.append(feature.metadata['turn'] - feature.metadata['history_turns'][0])

    selected_example_features = []
    for features in selected_example_features_dict.values():
        selected_example_features.extend(features)

    return selected_example_features, relative_selected_pos