import argparse
import numpy as np
import torch
import torch.nn.functional as F
import pytest
import json
import random
import time
import copy
from torch import nn
from modelingpreln import BertEncoder as BertEncoderPreln
from modeling import BertEncoder as BertEncoderPostln
from modeling import BertConfig, BertLayerNorm
from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig

import sys


def check_equal(first, second, atol=1e-2, verbose=False):
    diction_x = {}
    diction_y = {}

    for i, (x, y) in enumerate(zip(first, second)):
        print(x[1], y[1])

    for i, (x, y) in enumerate(zip(first, second)):
        k = 0
        while (diction_x.get((k, x[1])) is not None):
            k = k + 1
        diction_x[k, x[1]] = x[0]
        k = 0
        while (diction_y.get((k, y[1])) is not None):
            k = k + 1
        diction_y[k, y[1]] = y[0]
    if verbose:
        print()
    for i, (x, y) in enumerate(zip(diction_x, diction_y)):
        print(x, y)

    for i, (x, y) in enumerate(zip(diction_x, diction_y)):
        if (x[0] == 1): continue
        print("checking ", x[1], ":")
        y = diction_y[x[0], x[1]]
        x = diction_x[x[0], x[1]]
        x = x.cpu().detach().numpy()
        y = y.cpu().detach().numpy()
        print(x)
        print(y)

        avgx = np.sum(abs(x), dtype=float)
        countx = x.shape[0]
        for i in range(len(x.shape) - 1):
            countx *= x.shape[i + 1]
            avgx = np.sum(avgx)
        tollerance = 1
        if avgx != float('inf') and avgx != -float('inf'):
            avgx = avgx / countx
            tollerance = avgx * atol
        print("tollerance is ", tollerance)
        if verbose:
            print("x = {}".format(x.flatten()))
            print("y = {}".format(y.flatten()))
            print('-' * 80)
        np.testing.assert_allclose(x, y, err_msg="Index: {}".format(i), atol=tollerance)


def zero_grad(variables):
    for variable in variables:
        variable.grad.zero_()


device = torch.device("cuda")
kwargs_fp32 = {'dtype': torch.float, 'device': device, 'requires_grad': True}
kwargs_fp16 = {'dtype': torch.half, 'device': device, 'requires_grad': True}


class DSEncoder(nn.Module):
    def __init__(self, config, weights, biases):
        super(DSEncoder, self).__init__()
        self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
        self.layer = nn.ModuleList([
            copy.deepcopy(DeepSpeedTransformerLayer(i,
                                                    config,
                                                    weights,
                                                    biases))
            for i in range(config.num_hidden_layers)
        ])
        self.grads = []
        self.pre_or_post = config.pre_layer_norm

    def forward(self,
                hidden_states,
                attention_mask,
                output_all_encoded_layers=True,
                checkpoint_activations=False):
        all_encoder_layers = []

        def custom(start, end):
            def custom_forward(*inputs):
                layers = self.layer[start:end]
                x_ = inputs[0]
                for layer in layers:
                    x_ = layer(x_, inputs[1])
                return x_

            return custom_forward

        if checkpoint_activations:
            l = 0
            num_layers = len(self.layer)
            chunk_length = math.ceil(math.sqrt(num_layers))
            while l < num_layers:
                hidden_states = checkpoint.checkpoint(custom(l,
                                                             l + chunk_length),
                                                      hidden_states,
                                                      attention_mask * 1)
                l += chunk_length
            # decoder layers
        else:
            for i, layer_module in enumerate(self.layer):
                hidden_states = layer_module(hidden_states, attention_mask, self.grads)
                hidden_states.register_hook(
                    lambda x,
                    self=self: self.grads.append([x,
                                                  "hidden_state"]))

                if output_all_encoded_layers:
                    all_encoder_layers.append(hidden_states)

        if not output_all_encoded_layers or checkpoint_activations:
            if (self.pre_or_post):
                hidden_states = self.FinalLayerNorm(hidden_states)
            all_encoder_layers.append(hidden_states)
        return all_encoder_layers

    def get_grads(self):
        return self.grads


def create_models(ds_config):
    bert_config = BertConfig(vocab_size_or_config_json_file=119547,
                             hidden_size=ds_config.hidden_size,
                             num_hidden_layers=ds_config.num_hidden_layers,
                             num_attention_heads=ds_config.heads,
                             intermediate_size=4 * ds_config.hidden_size,
                             hidden_act="gelu",
                             hidden_dropout_prob=ds_config.hidden_dropout_ratio,
                             attention_probs_dropout_prob=ds_config.attn_dropout_ratio,
                             max_position_embeddings=ds_config.max_seq_length,
                             type_vocab_size=2,
                             initializer_range=ds_config.initializer_range)

    weights = []
    biases = []

    for i in range(4):
        weights.append(
            nn.Parameter(torch.Tensor(ds_config.hidden_size,
                                      ds_config.hidden_size)))
        weights[i].data.normal_(mean=0.0, std=ds_config.initializer_range)

    weights.append(nn.Parameter(torch.Tensor(ds_config.hidden_size)))
    weights[4].data.fill_(1.0)
    weights.append(
        nn.Parameter(torch.Tensor(4 * ds_config.hidden_size,
                                  ds_config.hidden_size)))
    weights[5].data.normal_(mean=0.0, std=ds_config.initializer_range)
    weights.append(
        nn.Parameter(torch.Tensor(ds_config.hidden_size,
                                  4 * ds_config.hidden_size)))
    weights[6].data.normal_(mean=0.0, std=ds_config.initializer_range)
    weights.append(nn.Parameter(torch.Tensor(ds_config.hidden_size)))
    weights[7].data.fill_(1.0)

    biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size)))
    biases[0].data.zero_()
    for i in range(4):
        biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size)))
        biases[i + 1].data.zero_()
    biases.append(nn.Parameter(torch.Tensor(4 * ds_config.hidden_size)))
    biases[5].data.zero_()
    biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size)))
    biases[6].data.zero_()
    biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size)))
    biases[7].data.zero_()

    if (ds_config.pre_layer_norm):
        bert_encoder = BertEncoderPreln(bert_config, weights, biases)
    else:
        bert_encoder = BertEncoderPostln(bert_config, weights, biases)
    ds_encoder = DSEncoder(ds_config, weights, biases)

    if ds_config.fp16:
        bert_encoder.half()
        ds_encoder.half()

    bert_encoder.cuda()
    ds_encoder.cuda()

    return bert_encoder, ds_encoder


def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


def run_backward(ds_config, atol=1e-2, verbose=False):
    set_seed(123)
    bert_encoder, ds_encoder = create_models(ds_config)

    # prepare test data
    kwargs = kwargs_fp16 if ds_config.fp16 else kwargs_fp32
    hidden_states = torch.randn(ds_config.batch_size,
                                ds_config.max_seq_length,
                                ds_config.hidden_size,
                                **kwargs)
    input_mask = torch.randn(ds_config.batch_size,
                             1,
                             1,
                             ds_config.max_seq_length,
                             **kwargs)
    Y = torch.randn(ds_config.batch_size,
                    ds_config.max_seq_length,
                    ds_config.hidden_size,
                    **kwargs)

    # run baseline
    base_results = bert_encoder(hidden_states,
                                input_mask,
                                output_all_encoded_layers=False,
                                checkpoint_activations=False)

    loss = (Y - base_results[0]).pow(2).sum()
    loss.backward()
    base_grads = bert_encoder.get_grads()

    # run ds
    ds_results = ds_encoder(hidden_states,
                            input_mask,
                            output_all_encoded_layers=False,
                            checkpoint_activations=False)

    loss = (Y - ds_results[0]).pow(2).sum()
    loss.backward()
    ds_grads = ds_encoder.get_grads()

    # check grads
    check_equal(base_grads, ds_grads, atol=atol, verbose=verbose)


@pytest.mark.parametrize('batch_size, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16, atol',
                         [
                             (3,1024,128,16,24,True,False, 0.05),
                             (3,1024,128,16,24,True,True, 0.05),
                             (3,1024,128,16,24,False,False, 0.1),
                             (3,1024,128,16,24,False,True, 0.2),
                         ]) # yapf: disable
def test_backward(batch_size,
                  hidden_size,
                  seq_len,
                  heads,
                  num_layers,
                  is_preln,
                  use_fp16,
                  atol):
    # Only run fp16 test cases on devices with 7+ capability.
    major, _ = torch.cuda.get_device_capability()
    if major < 7 and (use_fp16 is True or is_preln is False):
        return

    ds_config = DeepSpeedTransformerConfig()
    ds_config.layer_id = None
    ds_config.batch_size = batch_size
    ds_config.hidden_size = hidden_size
    ds_config.max_seq_length = seq_len
    ds_config.heads = heads
    ds_config.attn_dropout_ratio = 0.0
    ds_config.hidden_dropout_ratio = 0.0
    ds_config.num_hidden_layers = num_layers
    ds_config.pre_layer_norm = is_preln
    ds_config.initializer_range = 0.02
    ds_config.fp16 = use_fp16

    run_backward(ds_config, atol=atol)


#@pytest.mark.parametrize('batch_size, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16, atol',
#                         [
#                             (3,1024,128,16,24,True,False, 0.07),
#                             (3,1024,128,16,24,True,True, 0.05),
#                             (3,1024,128,16,24,False,False, 0.1),
#                             (3,1024,128,16,24,False,True, 0.2),
#                         ]) # yapf: disable
#def test_backward_stochastic(batch_size,
#                             hidden_size,
#                             seq_len,
#                             heads,
#                             num_layers,
#                             is_preln,
#                             use_fp16,
#                             atol):
#    # Only run fp16 test cases on devices with 7+ capability.
#    major, _ = torch.cuda.get_device_capability()
#    if major < 7 and (use_fp16 is True or is_preln is False):
#        return
#
#    ds_config = DeepSpeedTransformerConfig()
#    ds_config.layer_id = None
#    ds_config.batch_size = batch_size
#    ds_config.hidden_size = hidden_size
#    ds_config.max_seq_length = seq_len
#    ds_config.heads = heads
#    ds_config.attn_dropout_ratio = 0.0
#    ds_config.hidden_dropout_ratio = 0.0
#    ds_config.num_hidden_layers = num_layers
#    ds_config.pre_layer_norm = is_preln
#    ds_config.initializer_range = 0.02
#    ds_config.fp16 = use_fp16
#    ds_config.stochastic_mode = True
#
#    run_backward(ds_config, atol=atol)
