



import os
from collections import deque

import torch
from torch.utils.data import Dataset




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Any			,		_A      :     str=""			,		_A      :     Union[str, Any]="train"):
											assert os.path.isdir(_A)

											A__	:							Optional[Any]						    = []
											A__	:							Dict						    = os.listdir(_A)
											for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																							continue
																	A__	:							Dict						    = os.path.join(_A			,		_A)
																	if not os.path.isfile(_A):
																							continue
																	self.documents.append(_A)







					def __len__(       self      :     List[Any]):
											return len(self.documents)







					def __getitem__(       self      :     Dict			,		_A      :     List[Any]):
											A__	:							int						    = self.documents[idx]
											A__	:							str						    = document_path.split("/")[-1]
											with open(_A			,		encoding="utf-8") as source:
																	A__	:							Dict						    = source.read()
																	A__    ,       A__	:							Optional[int]						    = process_story(_A)
											return document_name, story_lines, summary_lines




def 	snake_case__       (					__lowercase       )		->		Optional[Any]:







						"""simple docstring"""

						A__	:							List[str]						    = list(filter(lambda __lowercase       : len(__lowercase       ) != 0							,     [line.strip() for line in raw_story.split("\n"       )]       )       )

						# for some unknown reason some lines miss a period, add it
						A__	:							List[str]						    = [_add_missing_period(__lowercase       ) for line in nonempty_lines]

						# gather article lines
						A__	:							Dict						    = []
						A__	:							List[str]						    = deque(__lowercase       )
						while True:
												try:
																		A__	:							List[str]						    = lines.popleft()
																		if element.startswith("@highlight"       ):
																								break
																		story_lines.append(__lowercase       )
												except IndexError:
																		# if "@highlight" is absent from the file we pop
																		# all elements until there is None, raising an exception.
																		return story_lines, []

    # gather summary lines
						A__	:							List[str]						    = list(filter(lambda __lowercase       : not t.startswith("@highlight"       )							,     __lowercase       )       )

						return story_lines, summary_lines
def 	snake_case__       (					__lowercase       )		->		Any:







						"""simple docstring"""

						A__	:							Optional[Any]						    = [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
						if line.startswith("@highlight"       ):
												return line
						if line[-1] in END_TOKENS:
												return line
						return line + "."
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						if len(__lowercase       ) > block_size:
												return sequence[:block_size]
						else:
												sequence.extend([pad_token_id] * (block_size - len(__lowercase       ))       )
												return sequence
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		List[str]:







						"""simple docstring"""

						A__	:							Any						    = torch.ones_like(__lowercase       )
						A__	:							str						    = sequence == pad_token_id
						A__	:							Any						    = 0
						return mask
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Any						    = [tokenizer.encode(__lowercase       ) for line in story_lines]
						A__	:							str						    = [token for sentence in story_lines_token_ids for token in sentence]
						A__	:							Union[str, Any]						    = [tokenizer.encode(__lowercase       ) for line in summary_lines]
						A__	:							Dict						    = [token for sentence in summary_lines_token_ids for token in sentence]

						return story_token_ids, summary_token_ids




def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							int						    = []
						for sequence in batch:
												A__	:							Optional[int]						    = -1
												A__	:							Union[str, Any]						    = []
												for s in sequence:
																		if s == separator_token_id:
																								sentence_num += 1
																		embeddings.append(sentence_num % 2       )
												batch_embeddings.append(__lowercase       )
						return torch.tensor(__lowercase       )




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)