



import os
from collections import deque

import torch
from torch.utils.data import Dataset

class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  str=""      ,				snake_case__  :  str="train" ):
								"""simple docstring"""







								assert os.path.isdir(snake_case__ )

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







							def __len__(						self  :  int ):
								"""simple docstring"""







								return len(self.documents )







							def __getitem__(						self  :  List[Any]      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







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






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]  ):






	"""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 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]  ):






	"""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 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Optional[Any]  ):






	"""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  )








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						str   							=  'distilbert'
							lowercase_		:						Any   							=  {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  int=30_522      ,				snake_case__  :  str=512      ,				snake_case__  :  Tuple=False      ,				snake_case__  :  Tuple=6      ,				snake_case__  :  Any=12      ,				snake_case__  :  Dict=768      ,				snake_case__  :  Any=4 * 768      ,				snake_case__  :  Union[str, Any]=0.1      ,				snake_case__  :  str=0.1      ,				snake_case__  :  Tuple="gelu"      ,				snake_case__  :  str=0.0_2      ,				snake_case__  :  Optional[Any]=0.1      ,				snake_case__  :  Optional[Any]=0.2      ,				snake_case__  :  str=0      ,				**snake_case__  :  Dict      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							sinusoidal_pos_embds
								__lowerCAmelCase         =							n_layers
								__lowerCAmelCase         =							n_heads
								__lowerCAmelCase         =							dim
								__lowerCAmelCase         =							hidden_dim
								__lowerCAmelCase         =							dropout
								__lowerCAmelCase         =							attention_dropout
								__lowerCAmelCase         =							activation
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							qa_dropout
								__lowerCAmelCase         =							seq_classif_dropout
								super().__init__(**snake_case__      ,				pad_token_id=snake_case__ )

class       a		(   __UpperCAmelCase					):







							@property
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								if self.task == "multiple-choice":
									__lowerCAmelCase         =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
									__lowerCAmelCase         =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ] )






