


'''simple docstring'''



import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 	UpperCamelCase__(       _a		):




				def __init__(		self      :			int				,   lowerCAmelCase      :			Optional[int]=""				,   lowerCAmelCase      :			Union[str, Any]="train"				)->					str:

										"""simple docstring"""




										assert os.path.isdir(snake_case_				)

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




				def __len__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




										return len(self.documents				)





				def __getitem__(		self      :			Any				,   lowerCAmelCase      :			Optional[Any]				)->					List[Any]:

										"""simple docstring"""




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







def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     list(filter(lambda A					: len(_lowerCAmelCase					) != 0				,       [line.strip() for line in raw_story.split('''\n'''					)]					)					)

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

						# gather article lines
						UpperCAmelCase					    =     []
						UpperCAmelCase					    =     deque(_lowerCAmelCase					)
						while True:
												try:
																		UpperCAmelCase					    =     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
						UpperCAmelCase					    =     list(filter(lambda A					: not t.startswith('''@highlight'''					)				,       _lowerCAmelCase					)					)

						return story_lines, summary_lines


def 				lowerCamelCase__				(	A       :      Union[str, Any]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      Any				,       A       :      int				,       A       :      Optional[int]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      str				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     torch.ones_like(_lowerCAmelCase					)
						UpperCAmelCase					    =     sequence == pad_token_id
						UpperCAmelCase					    =     0
						return mask


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[int]				,       A       :      Dict					):




						'''simple docstring'''






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

						return story_token_ids, summary_token_ids




def 				lowerCamelCase__				(	A       :      Tuple				,       A       :      List[str]					):




						'''simple docstring'''






						UpperCAmelCase					    =     []
						for sequence in batch:
												UpperCAmelCase					    =     -1
												UpperCAmelCase					    =     []
												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'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_lowercase      :      str            =					logging.get_logger(__name__)

_lowercase      :      Any            =					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Tuple            =					{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

_lowercase      :      Union[str, Any]            =					{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Dict  		=	VOCAB_FILES_NAMES
				__magic_name__    :				str  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				List[Any]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				List[Any]  		=	GPTaTokenizer




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Tuple="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Optional[int]=False				,   **lowerCAmelCase      :			Tuple				,   )->					int:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase				,   lowerCAmelCase				,   tokenizer_file=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   add_prefix_space=lowerCAmelCase				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     kwargs.pop('''add_bos_token'''				,   lowerCAmelCase				)

										UpperCAmelCase					    =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get('''add_prefix_space'''				,   lowerCAmelCase				) != add_prefix_space:
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   pre_tok_state.pop('''type'''				)				)
																UpperCAmelCase					    =     add_prefix_space
																UpperCAmelCase					    =     pre_tok_class(**lowerCAmelCase				)

										UpperCAmelCase					    =     add_prefix_space




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			Union[str, Any]				,   **lowerCAmelCase      :			Dict				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[str]				,   *lowerCAmelCase      :			Any				,   **lowerCAmelCase      :			Tuple				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				) + [self.eos_token_id]				)

										if len(lowerCAmelCase				) > self.model_max_length:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





