


"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a=""    ,   __a="train"	):
										assert os.path.isdir(__a	)

										__lowerCAmelCase				    =			[]
										__lowerCAmelCase				    =			os.listdir(__a	)
										for story_filename in story_filenames_list:
													if "summary" in story_filename:
																continue
													__lowerCAmelCase				    =			os.path.join(__a    ,   __a	)
													if not os.path.isfile(__a	):
																continue
													self.documents.append(__a	)

							def __len__(     self	):
										return len(self.documents	)



							def __getitem__(     self    ,   __a	):
										__lowerCAmelCase				    =			self.documents[idx]
										__lowerCAmelCase				    =			document_path.split("/"	)[-1]
										with open(__a    ,   encoding="utf-8"	) as source:
													__lowerCAmelCase				    =			source.read()
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			process_story(__a	)
										return document_name, story_lines, summary_lines


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			list(filter(lambda _UpperCamelCase      : len(_UpperCamelCase      ) != 0    , [line.strip() for line in raw_story.split("\n"      )]      )      )

			# for some unknown reason some lines miss a period, add it
			__lowerCAmelCase				    =			[_add_missing_period(_UpperCamelCase      ) for line in nonempty_lines]

			# gather article lines
			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			deque(_UpperCamelCase      )
			while True:
						try:
									__lowerCAmelCase				    =			lines.popleft()
									if element.startswith("@highlight"      ):
												break
									story_lines.append(_UpperCamelCase      )
						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
			__lowerCAmelCase				    =			list(filter(lambda _UpperCamelCase      : not t.startswith("@highlight"      )    , _UpperCamelCase      )      )

			return story_lines, summary_lines


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
			if line.startswith("@highlight"      ):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if len(_UpperCamelCase      ) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(_UpperCamelCase      ))      )
						return sequence


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.ones_like(_UpperCamelCase      )
			__lowerCAmelCase				    =			sequence == pad_token_id
			__lowerCAmelCase				    =			0
			return mask


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[tokenizer.encode(_UpperCamelCase      ) for line in story_lines]
			__lowerCAmelCase				    =			[token for sentence in story_lines_token_ids for token in sentence]
			__lowerCAmelCase				    =			[tokenizer.encode(_UpperCamelCase      ) for line in summary_lines]
			__lowerCAmelCase				    =			[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]
			for sequence in batch:
						__lowerCAmelCase				    =			-1
						__lowerCAmelCase				    =			[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2      )
						batch_embeddings.append(_UpperCamelCase      )
			return torch.tensor(_UpperCamelCase      )











import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



