

'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase="",     _lowercase="train"       )     ->							int:
											assert os.path.isdir(_lowercase       )

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



				def __len__(    self       )     ->							str:
											return len(self.documents       )





				def __getitem__(    self,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				self.documents[idx]
											SCREAMING_SNAKE_CASE_				=				document_path.split('/'       )[-1]
											with open(_lowercase,     encoding='utf-8'       ) as source:
																		SCREAMING_SNAKE_CASE_				=				source.read()
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				process_story(_lowercase       )
											return document_name, story_lines, summary_lines




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[Any]   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				list(filter(lambda lowerCAmelCase__   : len(lowerCAmelCase__   ) != 0		,[line.strip() for line in raw_story.split('\n'   )]   )   )

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

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

							return story_lines, summary_lines




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict   )      -> str:
							SCREAMING_SNAKE_CASE_				=				['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
							if line.startswith('@highlight'   ):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."




def 							_UpperCamelCase				(   lowerCAmelCase__:					Any		,lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Optional[int]   )      -> Optional[Any]:
							if len(lowerCAmelCase__   ) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase__   ))   )
														return sequence




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					Optional[Any]   )      -> Union[str, Any]:
							SCREAMING_SNAKE_CASE_				=				torch.ones_like(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				sequence == pad_token_id
							SCREAMING_SNAKE_CASE_				=				0
							return mask




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Optional[Any]   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				[tokenizer.encode(lowerCAmelCase__   ) for line in story_lines]
							SCREAMING_SNAKE_CASE_				=				[token for sentence in story_lines_token_ids for token in sentence]
							SCREAMING_SNAKE_CASE_				=				[tokenizer.encode(lowerCAmelCase__   ) for line in summary_lines]
							SCREAMING_SNAKE_CASE_				=				[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Optional[Any]   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				[]
							for sequence in batch:
														SCREAMING_SNAKE_CASE_				=				-1
														SCREAMING_SNAKE_CASE_				=				[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2   )
														batch_embeddings.append(lowerCAmelCase__   )
							return torch.tensor(lowerCAmelCase__   )








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				hidden_size
											SCREAMING_SNAKE_CASE_				=				num_hidden_layers
											SCREAMING_SNAKE_CASE_				=				num_attention_heads
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				intermediate_size
											SCREAMING_SNAKE_CASE_				=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE_				=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_				=				max_position_embeddings
											SCREAMING_SNAKE_CASE_				=				type_vocab_size
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps
											SCREAMING_SNAKE_CASE_				=				position_embedding_type
											SCREAMING_SNAKE_CASE_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





