"""
Utilities for working with the local dataset cache. This file is adapted from the AllenNLP library at
https://github.com/allenai/allennlp Copyright by the AllenNLP authors.
"""

import fnmatch
import io
import json
import os
import re
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from dataclasses import fields
from functools import partial, wraps
from hashlib import sha256
from pathlib import Path
from typing import Any, BinaryIO, Dict, Optional, Tuple, Union
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import numpy as np
from tqdm.auto import tqdm

import requests
from filelock import FileLock

from . import __version__
from .utils import logging


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

ENV_VARS_TRUE_VALUES = {"1", "ON", "YES"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})

try:
    USE_TF = os.environ.get("USE_TF", "AUTO").upper()
    USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
    if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
        import torch

        _torch_available = True  # pylint: disable=invalid-name
        logger.info("PyTorch version {} available.".format(torch.__version__))
    else:
        logger.info("Disabling PyTorch because USE_TF is set")
        _torch_available = False
except ImportError:
    _torch_available = False  # pylint: disable=invalid-name

try:
    USE_TF = os.environ.get("USE_TF", "AUTO").upper()
    USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()

    if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
        import tensorflow as tf

        assert hasattr(tf, "__version__") and int(tf.__version__[0]) >= 2
        _tf_available = True  # pylint: disable=invalid-name
        logger.info("TensorFlow version {} available.".format(tf.__version__))
    else:
        logger.info("Disabling Tensorflow because USE_TORCH is set")
        _tf_available = False
except (ImportError, AssertionError):
    _tf_available = False  # pylint: disable=invalid-name


try:
    USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()

    if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
        import flax
        import jax

        logger.info("JAX version {}, Flax: available".format(jax.__version__))
        logger.info("Flax available: {}".format(flax))
        _flax_available = True
    else:
        _flax_available = False
except ImportError:
    _flax_available = False  # pylint: disable=invalid-name


try:
    import datasets  # noqa: F401

    # Check we're not importing a "datasets" directory somewhere
    _datasets_available = hasattr(datasets, "__version__") and hasattr(datasets, "load_dataset")
    if _datasets_available:
        logger.debug(f"Successfully imported datasets version {datasets.__version__}")
    else:
        logger.debug("Imported a datasets object but this doesn't seem to be the 🤗 datasets library.")

except ImportError:
    _datasets_available = False

try:
    from torch.hub import _get_torch_home

    torch_cache_home = _get_torch_home()
except ImportError:
    torch_cache_home = os.path.expanduser(
        os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
    )


try:
    import torch_xla.core.xla_model as xm  # noqa: F401

    if _torch_available:
        _torch_tpu_available = True  # pylint: disable=
    else:
        _torch_tpu_available = False
except ImportError:
    _torch_tpu_available = False


try:
    import psutil  # noqa: F401

    _psutil_available = True

except ImportError:
    _psutil_available = False


try:
    import py3nvml  # noqa: F401

    _py3nvml_available = True

except ImportError:
    _py3nvml_available = False


try:
    from apex import amp  # noqa: F401

    _has_apex = True
except ImportError:
    _has_apex = False


try:
    import faiss  # noqa: F401

    _faiss_available = True
    logger.debug(f"Successfully imported faiss version {faiss.__version__}")
except ImportError:
    _faiss_available = False

try:
    import sklearn.metrics  # noqa: F401

    import scipy.stats  # noqa: F401

    _has_sklearn = True
except (AttributeError, ImportError):
    _has_sklearn = False

try:
    # Test copied from tqdm.autonotebook: https://github.com/tqdm/tqdm/blob/master/tqdm/autonotebook.py
    get_ipython = sys.modules["IPython"].get_ipython
    if "IPKernelApp" not in get_ipython().config:
        raise ImportError("console")
    if "VSCODE_PID" in os.environ:
        raise ImportError("vscode")

    import IPython  # noqa: F401

    _in_notebook = True
except (AttributeError, ImportError, KeyError):
    _in_notebook = False


try:
    import sentencepiece  # noqa: F401

    _sentencepiece_available = True

except ImportError:
    _sentencepiece_available = False


try:
    import google.protobuf  # noqa: F401

    _protobuf_available = True

except ImportError:
    _protobuf_available = False


try:
    import tokenizers  # noqa: F401

    _tokenizers_available = True

except ImportError:
    _tokenizers_available = False


old_default_cache_path = os.path.join(torch_cache_home, "transformers")
# New default cache, shared with the Datasets library
hf_cache_home = os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
default_cache_path = os.path.join(hf_cache_home, "transformers")

# Onetime move from the old location to the new one if no ENV variable has been set.
if (
    os.path.isdir(old_default_cache_path)
    and "PYTORCH_PRETRAINED_BERT_CACHE" not in os.environ
    and "PYTORCH_TRANSFORMERS_CACHE" not in os.environ
    and "TRANSFORMERS_CACHE" not in os.environ
):
    logger.warn(
        "In Transformers v4.0.0, the default path to cache downloaded models changed from "
        "'~/.cache/torch/transformers' to '~/.cache/huggingface/transformers'. Since you don't seem to have overridden "
        "and '~/.cache/torch/transformers' is a directory that exists, we're moving it to "
        "'~/.cache/huggingface/transformers' to avoid redownloading models you have already in the cache. You should "
        "only see this message once."
    )
    shutil.move(old_default_cache_path, default_cache_path)

PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)

WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = "tf_model.h5"
TF_WEIGHTS_NAME = "model.ckpt"
CONFIG_NAME = "config.json"
MODEL_CARD_NAME = "modelcard.json"

SENTENCEPIECE_UNDERLINE = "▁"
SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

MULTIPLE_CHOICE_DUMMY_INPUTS = [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
HUGGINGFACE_CO_PREFIX = "https://huggingface.co/{model_id}/resolve/{revision}/{filename}"

PRESET_MIRROR_DICT = {
    "tuna": "https://mirrors.tuna.tsinghua.edu.cn/hugging-face-models",
    "bfsu": "https://mirrors.bfsu.edu.cn/hugging-face-models",
}


def is_torch_available():
    return _torch_available


def is_tf_available():
    return _tf_available


def is_flax_available():
    return _flax_available


def is_torch_tpu_available():
    return _torch_tpu_available


def is_datasets_available():
    return _datasets_available


def is_psutil_available():
    return _psutil_available


def is_py3nvml_available():
    return _py3nvml_available


def is_apex_available():
    return _has_apex


def is_faiss_available():
    return _faiss_available


def is_sklearn_available():
    return _has_sklearn


def is_sentencepiece_available():
    return _sentencepiece_available


def is_protobuf_available():
    return _protobuf_available


def is_tokenizers_available():
    return _tokenizers_available


def is_in_notebook():
    return _in_notebook


def torch_only_method(fn):
    def wrapper(*args, **kwargs):
        if not _torch_available:
            raise ImportError(
                "You need to install pytorch to use this method or class, "
                "or activate it with environment variables USE_TORCH=1 and USE_TF=0."
            )
        else:
            return fn(*args, **kwargs)

    return wrapper


# docstyle-ignore
DATASETS_IMPORT_ERROR = """
{0} requires the 🤗 Datasets library but it was not found in your environment. You can install it with:
```
pip install datasets
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install datasets
```
then restarting your kernel.

Note that if you have a local folder named `datasets` or a local python file named `datasets.py` in your current
working directory, python may try to import this instead of the 🤗 Datasets library. You should rename this folder or
that python file if that's the case.
"""


# docstyle-ignore
TOKENIZERS_IMPORT_ERROR = """
{0} requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with:
```
pip install tokenizers
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install tokenizers
```
"""


# docstyle-ignore
SENTENCEPIECE_IMPORT_ERROR = """
{0} requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
that match your environment.
"""


# docstyle-ignore
PROTOBUF_IMPORT_ERROR = """
{0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones
that match your environment.
"""


# docstyle-ignore
FAISS_IMPORT_ERROR = """
{0} requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
that match your environment.
"""


# docstyle-ignore
PYTORCH_IMPORT_ERROR = """
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
"""


# docstyle-ignore
SKLEARN_IMPORT_ERROR = """
{0} requires the scikit-learn library but it was not found in your environment. You can install it with:
```
pip install -U scikit-learn
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install -U scikit-learn
```
"""


# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = """
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
"""


# docstyle-ignore
FLAX_IMPORT_ERROR = """
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
"""


def requires_datasets(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_datasets_available():
        raise ImportError(DATASETS_IMPORT_ERROR.format(name))


def requires_faiss(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_faiss_available():
        raise ImportError(FAISS_IMPORT_ERROR.format(name))


def requires_pytorch(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_torch_available():
        raise ImportError(PYTORCH_IMPORT_ERROR.format(name))


def requires_sklearn(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_sklearn_available():
        raise ImportError(SKLEARN_IMPORT_ERROR.format(name))


def requires_tf(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_tf_available():
        raise ImportError(TENSORFLOW_IMPORT_ERROR.format(name))


def requires_flax(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_flax_available():
        raise ImportError(FLAX_IMPORT_ERROR.format(name))


def requires_tokenizers(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_tokenizers_available():
        raise ImportError(TOKENIZERS_IMPORT_ERROR.format(name))


def requires_sentencepiece(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_sentencepiece_available():
        raise ImportError(SENTENCEPIECE_IMPORT_ERROR.format(name))


def requires_protobuf(obj):
    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    if not is_protobuf_available():
        raise ImportError(PROTOBUF_IMPORT_ERROR.format(name))


def add_start_docstrings(*docstr):
    def docstring_decorator(fn):
        fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
        return fn

    return docstring_decorator


def add_start_docstrings_to_model_forward(*docstr):
    def docstring_decorator(fn):
        class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
        intro = "   The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
        note = r"""

    .. note::
        Although the recipe for forward pass needs to be defined within this function, one should call the
        :class:`Module` instance afterwards instead of this since the former takes care of running the pre and post
        processing steps while the latter silently ignores them.
        """
        fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
        return fn

    return docstring_decorator


def add_end_docstrings(*docstr):
    def docstring_decorator(fn):
        fn.__doc__ = fn.__doc__ + "".join(docstr)
        return fn

    return docstring_decorator


PT_RETURN_INTRODUCTION = r"""
    Returns:
        :class:`~{full_output_type}` or :obj:`tuple(torch.FloatTensor)`: A :class:`~{full_output_type}` (if
        ``return_dict=True`` is passed or when ``config.return_dict=True``) or a tuple of :obj:`torch.FloatTensor`
        comprising various elements depending on the configuration (:class:`~transformers.{config_class}`) and inputs.

"""


TF_RETURN_INTRODUCTION = r"""
    Returns:
        :class:`~{full_output_type}` or :obj:`tuple(tf.Tensor)`: A :class:`~{full_output_type}` (if
        ``return_dict=True`` is passed or when ``config.return_dict=True``) or a tuple of :obj:`tf.Tensor` comprising
        various elements depending on the configuration (:class:`~transformers.{config_class}`) and inputs.

"""


def _get_indent(t):
    """Returns the indentation in the first line of t"""
    search = re.search(r"^(\s*)\S", t)
    return "" if search is None else search.groups()[0]


def _convert_output_args_doc(output_args_doc):
    """Convert output_args_doc to display properly."""
    # Split output_arg_doc in blocks argument/description
    indent = _get_indent(output_args_doc)
    blocks = []
    current_block = ""
    for line in output_args_doc.split("\n"):
        # If the indent is the same as the beginning, the line is the name of new arg.
        if _get_indent(line) == indent:
            if len(current_block) > 0:
                blocks.append(current_block[:-1])
            current_block = f"{line}\n"
        else:
            # Otherwise it's part of the description of the current arg.
            # We need to remove 2 spaces to the indentation.
            current_block += f"{line[2:]}\n"
    blocks.append(current_block[:-1])

    # Format each block for proper rendering
    for i in range(len(blocks)):
        blocks[i] = re.sub(r"^(\s+)(\S+)(\s+)", r"\1- **\2**\3", blocks[i])
        blocks[i] = re.sub(r":\s*\n\s*(\S)", r" -- \1", blocks[i])

    return "\n".join(blocks)


def _prepare_output_docstrings(output_type, config_class):
    """
    Prepares the return part of the docstring using `output_type`.
    """
    docstrings = output_type.__doc__

    # Remove the head of the docstring to keep the list of args only
    lines = docstrings.split("\n")
    i = 0
    while i < len(lines) and re.search(r"^\s*(Args|Parameters):\s*$", lines[i]) is None:
        i += 1
    if i < len(lines):
        docstrings = "\n".join(lines[(i + 1) :])
        docstrings = _convert_output_args_doc(docstrings)

    # Add the return introduction
    full_output_type = f"{output_type.__module__}.{output_type.__name__}"
    intro = TF_RETURN_INTRODUCTION if output_type.__name__.startswith("TF") else PT_RETURN_INTRODUCTION
    intro = intro.format(full_output_type=full_output_type, config_class=config_class)
    return intro + docstrings


PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import torch

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0)  # Batch size 1

        >>> outputs = model(**inputs, labels=labels)
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

PT_QUESTION_ANSWERING_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import torch

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
        >>> inputs = tokenizer(question, text, return_tensors='pt')
        >>> start_positions = torch.tensor([1])
        >>> end_positions = torch.tensor([3])

        >>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
        >>> loss = outputs.loss
        >>> start_scores = outputs.start_logits
        >>> end_scores = outputs.end_logits
"""

PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import torch

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
        >>> outputs = model(**inputs, labels=labels)
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

PT_MASKED_LM_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import torch

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
        >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]

        >>> outputs = model(**inputs, labels=labels)
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

PT_BASE_MODEL_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import torch

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
"""

PT_MULTIPLE_CHOICE_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import torch

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
        >>> choice0 = "It is eaten with a fork and a knife."
        >>> choice1 = "It is eaten while held in the hand."
        >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

        >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
        >>> outputs = model(**{{k: v.unsqueeze(0) for k,v in encoding.items()}}, labels=labels)  # batch size is 1

        >>> # the linear classifier still needs to be trained
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

PT_CAUSAL_LM_SAMPLE = r"""
    Example::

        >>> import torch
        >>> from transformers import {tokenizer_class}, {model_class}

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint})

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs, labels=inputs["input_ids"])
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
        >>> input_ids = inputs["input_ids"]
        >>> inputs["labels"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))) # Batch size 1

        >>> outputs = model(inputs)
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

TF_QUESTION_ANSWERING_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
        >>> input_dict = tokenizer(question, text, return_tensors='tf')
        >>> outputs = model(input_dict)
        >>> start_logits = outputs.start_logits
        >>> end_logits = outputs.end_logits

        >>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
        >>> answer = ' '.join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0]+1])
"""

TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
        >>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1

        >>> outputs = model(inputs)
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

TF_MASKED_LM_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
        >>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]

        >>> outputs = model(inputs)
        >>> loss = outputs.loss
        >>> logits = outputs.logits
"""

TF_BASE_MODEL_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
        >>> outputs = model(inputs)

        >>> last_hidden_states = outputs.last_hidden_states
"""

TF_MULTIPLE_CHOICE_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
        >>> choice0 = "It is eaten with a fork and a knife."
        >>> choice1 = "It is eaten while held in the hand."

        >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='tf', padding=True)
        >>> inputs = {{k: tf.expand_dims(v, 0) for k, v in encoding.items()}}
        >>> outputs = model(inputs)  # batch size is 1

        >>> # the linear classifier still needs to be trained
        >>> logits = outputs.logits
"""

TF_CAUSAL_LM_SAMPLE = r"""
    Example::

        >>> from transformers import {tokenizer_class}, {model_class}
        >>> import tensorflow as tf

        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
        >>> model = {model_class}.from_pretrained('{checkpoint}')

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
        >>> outputs = model(inputs)
        >>> logits = outputs.logits
"""


def add_code_sample_docstrings(
    *docstr, tokenizer_class=None, checkpoint=None, output_type=None, config_class=None, mask=None
):
    def docstring_decorator(fn):
        model_class = fn.__qualname__.split(".")[0]
        is_tf_class = model_class[:2] == "TF"
        doc_kwargs = dict(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)

        if "SequenceClassification" in model_class:
            code_sample = TF_SEQUENCE_CLASSIFICATION_SAMPLE if is_tf_class else PT_SEQUENCE_CLASSIFICATION_SAMPLE
        elif "QuestionAnswering" in model_class:
            code_sample = TF_QUESTION_ANSWERING_SAMPLE if is_tf_class else PT_QUESTION_ANSWERING_SAMPLE
        elif "TokenClassification" in model_class:
            code_sample = TF_TOKEN_CLASSIFICATION_SAMPLE if is_tf_class else PT_TOKEN_CLASSIFICATION_SAMPLE
        elif "MultipleChoice" in model_class:
            code_sample = TF_MULTIPLE_CHOICE_SAMPLE if is_tf_class else PT_MULTIPLE_CHOICE_SAMPLE
        elif "MaskedLM" in model_class or model_class in ["FlaubertWithLMHeadModel", "XLMWithLMHeadModel"]:
            doc_kwargs["mask"] = "[MASK]" if mask is None else mask
            code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE
        elif "LMHead" in model_class:
            code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE
        elif "Model" in model_class or "Encoder" in model_class:
            code_sample = TF_BASE_MODEL_SAMPLE if is_tf_class else PT_BASE_MODEL_SAMPLE
        else:
            raise ValueError(f"Docstring can't be built for model {model_class}")

        output_doc = _prepare_output_docstrings(output_type, config_class) if output_type is not None else ""
        built_doc = code_sample.format(**doc_kwargs)
        fn.__doc__ = (fn.__doc__ or "") + "".join(docstr) + output_doc + built_doc
        return fn

    return docstring_decorator


def replace_return_docstrings(output_type=None, config_class=None):
    def docstring_decorator(fn):
        docstrings = fn.__doc__
        lines = docstrings.split("\n")
        i = 0
        while i < len(lines) and re.search(r"^\s*Returns?:\s*$", lines[i]) is None:
            i += 1
        if i < len(lines):
            lines[i] = _prepare_output_docstrings(output_type, config_class)
            docstrings = "\n".join(lines)
        else:
            raise ValueError(
                f"The function {fn} should have an empty 'Return:' or 'Returns:' in its docstring as placeholder, current docstring is:\n{docstrings}"
            )
        fn.__doc__ = docstrings
        return fn

    return docstring_decorator


def is_remote_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https")


def hf_bucket_url(
    model_id: str, filename: str, subfolder: Optional[str] = None, revision: Optional[str] = None, mirror=None
) -> str:
    """
    Resolve a model identifier, a file name, and an optional revision id, to a huggingface.co-hosted url, redirecting
    to Cloudfront (a Content Delivery Network, or CDN) for large files.

    Cloudfront is replicated over the globe so downloads are way faster for the end user (and it also lowers our
    bandwidth costs).

    Cloudfront aggressively caches files by default (default TTL is 24 hours), however this is not an issue here
    because we migrated to a git-based versioning system on huggingface.co, so we now store the files on S3/Cloudfront
    in a content-addressable way (i.e., the file name is its hash). Using content-addressable filenames means cache
    can't ever be stale.

    In terms of client-side caching from this library, we base our caching on the objects' ETag. An object' ETag is:
    its sha1 if stored in git, or its sha256 if stored in git-lfs. Files cached locally from transformers before v3.5.0
    are not shared with those new files, because the cached file's name contains a hash of the url (which changed).
    """
    if subfolder is not None:
        filename = f"{subfolder}/{filename}"

    if mirror:
        endpoint = PRESET_MIRROR_DICT.get(mirror, mirror)
        legacy_format = "/" not in model_id
        if legacy_format:
            return f"{endpoint}/{model_id}-{filename}"
        else:
            return f"{endpoint}/{model_id}/{filename}"

    if revision is None:
        revision = "main"
    return HUGGINGFACE_CO_PREFIX.format(model_id=model_id, revision=revision, filename=filename)


def url_to_filename(url: str, etag: Optional[str] = None) -> str:
    """
    Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's,
    delimited by a period. If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name so that TF 2.0 can
    identify it as a HDF5 file (see
    https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
    """
    url_bytes = url.encode("utf-8")
    filename = sha256(url_bytes).hexdigest()

    if etag:
        etag_bytes = etag.encode("utf-8")
        filename += "." + sha256(etag_bytes).hexdigest()

    if url.endswith(".h5"):
        filename += ".h5"

    return filename


def filename_to_url(filename, cache_dir=None):
    """
    Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or
    its stored metadata do not exist.
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    cache_path = os.path.join(cache_dir, filename)
    if not os.path.exists(cache_path):
        raise EnvironmentError("file {} not found".format(cache_path))

    meta_path = cache_path + ".json"
    if not os.path.exists(meta_path):
        raise EnvironmentError("file {} not found".format(meta_path))

    with open(meta_path, encoding="utf-8") as meta_file:
        metadata = json.load(meta_file)
    url = metadata["url"]
    etag = metadata["etag"]

    return url, etag


def cached_path(
    url_or_filename,
    cache_dir=None,
    force_download=False,
    proxies=None,
    resume_download=False,
    user_agent: Union[Dict, str, None] = None,
    extract_compressed_file=False,
    force_extract=False,
    local_files_only=False,
) -> Optional[str]:
    """
    Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file
    and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and
    then return the path

    Args:
        cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
        force_download: if True, re-download the file even if it's already cached in the cache dir.
        resume_download: if True, resume the download if incompletely received file is found.
        user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
        extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
            file in a folder along the archive.
        force_extract: if True when extract_compressed_file is True and the archive was already extracted,
            re-extract the archive and override the folder where it was extracted.

    Return:
        Local path (string) of file or if networking is off, last version of file cached on disk.

    Raises:
        In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(url_or_filename, Path):
        url_or_filename = str(url_or_filename)
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    if is_remote_url(url_or_filename):
        # URL, so get it from the cache (downloading if necessary)
        output_path = get_from_cache(
            url_or_filename,
            cache_dir=cache_dir,
            force_download=force_download,
            proxies=proxies,
            resume_download=resume_download,
            user_agent=user_agent,
            local_files_only=local_files_only,
        )
    elif os.path.exists(url_or_filename):
        # File, and it exists.
        output_path = url_or_filename
    elif urlparse(url_or_filename).scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError("file {} not found".format(url_or_filename))
    else:
        # Something unknown
        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))

    if extract_compressed_file:
        if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
            return output_path

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        output_dir, output_file = os.path.split(output_path)
        output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
        output_path_extracted = os.path.join(output_dir, output_extract_dir_name)

        if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
            return output_path_extracted

        # Prevent parallel extractions
        lock_path = output_path + ".lock"
        with FileLock(lock_path):
            shutil.rmtree(output_path_extracted, ignore_errors=True)
            os.makedirs(output_path_extracted)
            if is_zipfile(output_path):
                with ZipFile(output_path, "r") as zip_file:
                    zip_file.extractall(output_path_extracted)
                    zip_file.close()
            elif tarfile.is_tarfile(output_path):
                tar_file = tarfile.open(output_path)
                tar_file.extractall(output_path_extracted)
                tar_file.close()
            else:
                raise EnvironmentError("Archive format of {} could not be identified".format(output_path))

        return output_path_extracted

    return output_path


def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
    """
    Formats a user-agent string with basic info about a request.
    """
    ua = "transformers/{}; python/{}".format(__version__, sys.version.split()[0])
    if is_torch_available():
        ua += "; torch/{}".format(torch.__version__)
    if is_tf_available():
        ua += "; tensorflow/{}".format(tf.__version__)
    if isinstance(user_agent, dict):
        ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
    elif isinstance(user_agent, str):
        ua += "; " + user_agent
    return ua


def http_get(url: str, temp_file: BinaryIO, proxies=None, resume_size=0, user_agent: Union[Dict, str, None] = None):
    """
    Donwload remote file. Do not gobble up errors.
    """
    headers = {"user-agent": http_user_agent(user_agent)}
    if resume_size > 0:
        headers["Range"] = "bytes=%d-" % (resume_size,)
    r = requests.get(url, stream=True, proxies=proxies, headers=headers)
    r.raise_for_status()
    content_length = r.headers.get("Content-Length")
    total = resume_size + int(content_length) if content_length is not None else None
    progress = tqdm(
        unit="B",
        unit_scale=True,
        total=total,
        initial=resume_size,
        desc="Downloading",
        disable=bool(logging.get_verbosity() == logging.NOTSET),
    )
    for chunk in r.iter_content(chunk_size=1024):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(chunk))
            temp_file.write(chunk)
    progress.close()


def get_from_cache(
    url: str,
    cache_dir=None,
    force_download=False,
    proxies=None,
    etag_timeout=10,
    resume_download=False,
    user_agent: Union[Dict, str, None] = None,
    local_files_only=False,
) -> Optional[str]:
    """
    Given a URL, look for the corresponding file in the local cache. If it's not there, download it. Then return the
    path to the cached file.

    Return:
        Local path (string) of file or if networking is off, last version of file cached on disk.

    Raises:
        In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    os.makedirs(cache_dir, exist_ok=True)

    url_to_download = url
    etag = None
    if not local_files_only:
        try:
            headers = {"user-agent": http_user_agent(user_agent)}
            r = requests.head(url, headers=headers, allow_redirects=False, proxies=proxies, timeout=etag_timeout)
            r.raise_for_status()
            etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")
            # We favor a custom header indicating the etag of the linked resource, and
            # we fallback to the regular etag header.
            # If we don't have any of those, raise an error.
            if etag is None:
                raise OSError(
                    "Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility."
                )
            # In case of a redirect,
            # save an extra redirect on the request.get call,
            # and ensure we download the exact atomic version even if it changed
            # between the HEAD and the GET (unlikely, but hey).
            if 300 <= r.status_code <= 399:
                url_to_download = r.headers["Location"]
        except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
            # etag is already None
            pass

    filename = url_to_filename(url, etag)

    # get cache path to put the file
    cache_path = os.path.join(cache_dir, filename)

    # etag is None == we don't have a connection or we passed local_files_only.
    # try to get the last downloaded one
    if etag is None:
        if os.path.exists(cache_path):
            return cache_path
        else:
            matching_files = [
                file
                for file in fnmatch.filter(os.listdir(cache_dir), filename.split(".")[0] + ".*")
                if not file.endswith(".json") and not file.endswith(".lock")
            ]
            if len(matching_files) > 0:
                return os.path.join(cache_dir, matching_files[-1])
            else:
                # If files cannot be found and local_files_only=True,
                # the models might've been found if local_files_only=False
                # Notify the user about that
                if local_files_only:
                    raise ValueError(
                        "Cannot find the requested files in the cached path and outgoing traffic has been"
                        " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
                        " to False."
                    )
                else:
                    raise ValueError(
                        "Connection error, and we cannot find the requested files in the cached path."
                        " Please try again or make sure your Internet connection is on."
                    )

    # From now on, etag is not None.
    if os.path.exists(cache_path) and not force_download:
        return cache_path

    # Prevent parallel downloads of the same file with a lock.
    lock_path = cache_path + ".lock"
    with FileLock(lock_path):

        # If the download just completed while the lock was activated.
        if os.path.exists(cache_path) and not force_download:
            # Even if returning early like here, the lock will be released.
            return cache_path

        if resume_download:
            incomplete_path = cache_path + ".incomplete"

            @contextmanager
            def _resumable_file_manager() -> "io.BufferedWriter":
                with open(incomplete_path, "ab") as f:
                    yield f

            temp_file_manager = _resumable_file_manager
            if os.path.exists(incomplete_path):
                resume_size = os.stat(incomplete_path).st_size
            else:
                resume_size = 0
        else:
            temp_file_manager = partial(tempfile.NamedTemporaryFile, mode="wb", dir=cache_dir, delete=False)
            resume_size = 0

        # Download to temporary file, then copy to cache dir once finished.
        # Otherwise you get corrupt cache entries if the download gets interrupted.
        with temp_file_manager() as temp_file:
            logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)

            http_get(url_to_download, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)

        logger.info("storing %s in cache at %s", url, cache_path)
        os.replace(temp_file.name, cache_path)

        logger.info("creating metadata file for %s", cache_path)
        meta = {"url": url, "etag": etag}
        meta_path = cache_path + ".json"
        with open(meta_path, "w") as meta_file:
            json.dump(meta, meta_file)

    return cache_path


class cached_property(property):
    """
    Descriptor that mimics @property but caches output in member variable.

    From tensorflow_datasets

    Built-in in functools from Python 3.8.
    """

    def __get__(self, obj, objtype=None):
        # See docs.python.org/3/howto/descriptor.html#properties
        if obj is None:
            return self
        if self.fget is None:
            raise AttributeError("unreadable attribute")
        attr = "__cached_" + self.fget.__name__
        cached = getattr(obj, attr, None)
        if cached is None:
            cached = self.fget(obj)
            setattr(obj, attr, cached)
        return cached


def torch_required(func):
    # Chose a different decorator name than in tests so it's clear they are not the same.
    @wraps(func)
    def wrapper(*args, **kwargs):
        if is_torch_available():
            return func(*args, **kwargs)
        else:
            raise ImportError(f"Method `{func.__name__}` requires PyTorch.")

    return wrapper


def tf_required(func):
    # Chose a different decorator name than in tests so it's clear they are not the same.
    @wraps(func)
    def wrapper(*args, **kwargs):
        if is_tf_available():
            return func(*args, **kwargs)
        else:
            raise ImportError(f"Method `{func.__name__}` requires TF.")

    return wrapper


def is_tensor(x):
    """ Tests if ``x`` is a :obj:`torch.Tensor`, :obj:`tf.Tensor` or :obj:`np.ndarray`. """
    if is_torch_available():
        import torch

        if isinstance(x, torch.Tensor):
            return True
    if is_tf_available():
        import tensorflow as tf

        if isinstance(x, tf.Tensor):
            return True
    return isinstance(x, np.ndarray)


class ModelOutput(OrderedDict):
    """
    Base class for all model outputs as dataclass. Has a ``__getitem__`` that allows indexing by integer or slice (like
    a tuple) or strings (like a dictionary) that will ignore the ``None`` attributes. Otherwise behaves like a regular
    python dictionary.

    .. warning::
        You can't unpack a :obj:`ModelOutput` directly. Use the :meth:`~transformers.file_utils.ModelOutput.to_tuple`
        method to convert it to a tuple before.
    """

    def __post_init__(self):
        class_fields = fields(self)

        # Safety and consistency checks
        assert len(class_fields), f"{self.__class__.__name__} has no fields."
        assert all(
            field.default is None for field in class_fields[1:]
        ), f"{self.__class__.__name__} should not have more than one required field."

        first_field = getattr(self, class_fields[0].name)
        other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])

        if other_fields_are_none and not is_tensor(first_field):
            try:
                iterator = iter(first_field)
                first_field_iterator = True
            except TypeError:
                first_field_iterator = False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
            if first_field_iterator:
                for element in iterator:
                    if (
                        not isinstance(element, (list, tuple))
                        or not len(element) == 2
                        or not isinstance(element[0], str)
                    ):
                        break
                    setattr(self, element[0], element[1])
                    if element[1] is not None:
                        self[element[0]] = element[1]
            elif first_field is not None:
                self[class_fields[0].name] = first_field
        else:
            for field in class_fields:
                v = getattr(self, field.name)
                if v is not None:
                    self[field.name] = v

    def __delitem__(self, *args, **kwargs):
        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")

    def setdefault(self, *args, **kwargs):
        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")

    def pop(self, *args, **kwargs):
        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")

    def update(self, *args, **kwargs):
        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")

    def __getitem__(self, k):
        if isinstance(k, str):
            inner_dict = {k: v for (k, v) in self.items()}
            return inner_dict[k]
        else:
            return self.to_tuple()[k]

    def __setattr__(self, name, value):
        if name in self.keys() and value is not None:
            # Don't call self.__setitem__ to avoid recursion errors
            super().__setitem__(name, value)
        super().__setattr__(name, value)

    def __setitem__(self, key, value):
        # Will raise a KeyException if needed
        super().__setitem__(key, value)
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(key, value)

    def to_tuple(self) -> Tuple[Any]:
        """
        Convert self to a tuple containing all the attributes/keys that are not ``None``.
        """
        return tuple(self[k] for k in self.keys())
