


'''simple docstring'''
import os
from collections import deque

import torch
from torch.utils.data import Dataset
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=""    ,   UpperCamelCase_="train"							):

							'''simple docstring'''


							assert os.path.isdir(UpperCamelCase_							)

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




		def __len__(		self							):

							'''simple docstring'''


							return len(self.documents							)




		def __getitem__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


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



def 			a      (			__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :str												= list(filter(lambda __a   : len(__a   ) != 0 ,					[line.strip() for line in raw_story.split('''\n'''   )]   )   )

					# for some unknown reason some lines miss a period, add it
					UpperCamelCase__   :Optional[Any]												= [_add_missing_period(__a   ) for line in nonempty_lines]

					# gather article lines
					UpperCamelCase__   :Dict												= []
					UpperCamelCase__   :Optional[Any]												= deque(__a   )
					while True:
										try:
															UpperCamelCase__   :List[Any]												= lines.popleft()
															if element.startswith('''@highlight'''   ):
																				break
															story_lines.append(__a   )
										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__   :Tuple												= list(filter(lambda __a   : not t.startswith('''@highlight'''   ) ,					__a   )   )

					return story_lines, summary_lines


def 			a      (			__a   )  ->	Optional[Any]:



					'''simple docstring'''






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


def 			a      (			__a ,					__a ,					__a   )  ->	Optional[int]:



					'''simple docstring'''






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


def 			a      (			__a ,					__a   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= torch.ones_like(__a   )
					UpperCamelCase__   :Tuple												= sequence == pad_token_id
					UpperCamelCase__   :List[Any]												= 0
					return mask


def 			a      (			__a ,					__a ,					__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :Any												= [tokenizer.encode(__a   ) for line in story_lines]
					UpperCamelCase__   :List[Any]												= [token for sentence in story_lines_token_ids for token in sentence]
					UpperCamelCase__   :int												= [tokenizer.encode(__a   ) for line in summary_lines]
					UpperCamelCase__   :Tuple												= [token for sentence in summary_lines_token_ids for token in sentence]

					return story_token_ids, summary_token_ids







def 			a      (			__a ,					__a   )  ->	Dict:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= []
					for sequence in batch:
										UpperCamelCase__   :Dict												= -1
										UpperCamelCase__   :List[str]												= []
										for s in sequence:
															if s == separator_token_id:
																				sentence_num += 1
															embeddings.append(sentence_num % 2   )
										batch_embeddings.append(__a   )
					return torch.tensor(__a   )



'''simple docstring'''
from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__snake_case							      =     {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__snake_case							      =     {
    '''facebook/blenderbot_small-90M''': 512,
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				VOCAB_FILES_NAMES
		_a     =				PRETRAINED_VOCAB_FILES_MAP
		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     =				BlenderbotSmallTokenizer




		def __init__(		self    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_=False    ,   UpperCamelCase_=True    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=UpperCamelCase_    ,   merges=UpperCamelCase_    ,   add_prefix_space=UpperCamelCase_    ,   trim_offsets=UpperCamelCase_    ,   )    ,   bos_token=UpperCamelCase_    ,   eos_token=UpperCamelCase_    ,   unk_token=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Union[str, Any]												= add_prefix_space




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= [self.sep_token_id]
							UpperCamelCase__   :Any												= [self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]