






import os
from collections import deque

import torch
from torch.utils.data import Dataset

class _lowercase			(       _lowercase	):




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]=""    ,							UpperCamelCase__:       Tuple="train"  ):
														assert os.path.isdir(UpperCamelCase__  )

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




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




							def __getitem__(							self:       Dict    ,							UpperCamelCase__:       List[str]  ):
														lowerCamelCase__      :			Optional[int] 						= self.documents[idx]
														lowerCamelCase__      :			Optional[int] 						= document_path.split("""/"""  )[-1]
														with open(UpperCamelCase__    ,							encoding="""utf-8"""  ) as source:
																					lowerCamelCase__      :			List[str] 						= source.read()
																					lowerCamelCase__      :			List[str] 						= process_story(UpperCamelCase__  )
														return document_name, story_lines, summary_lines





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							lowerCamelCase__      :			List[str] 						= 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__      :			Union[str, Any] 						= [_add_missing_period(UpperCamelCase   ) for line in nonempty_lines]

							# gather article lines
							lowerCamelCase__      :			Dict 						= []
							lowerCamelCase__      :			List[str] 						= deque(UpperCamelCase   )
							while True:
														try:
																					lowerCamelCase__      :			str 						= 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__      :			Optional[Any] 						= list(filter(lambda UpperCamelCase   : not t.startswith("""@highlight"""   )       ,    UpperCamelCase   )   )

							return story_lines, summary_lines





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Tuple:
							lowerCamelCase__      :			Optional[Any] 						= [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""   ):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."





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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			Dict 						= torch.ones_like(UpperCamelCase   )
							lowerCamelCase__      :			Any 						= sequence == pad_token_id
							lowerCamelCase__      :			List[str] 						= 0
							return mask





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

							return story_token_ids, summary_token_ids





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Optional[int] 						= []
							for sequence in batch:
														lowerCamelCase__      :			Optional[Any] 						= -1
														lowerCamelCase__      :			Optional[int] 						= []
														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   )









'''simple docstring'''







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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Dict					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    Tuple					  ={
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""    ,							UpperCamelCase__  ) != add_prefix_space:
																					lowerCamelCase__      :			Any 						= getattr(UpperCamelCase__    ,							pre_tok_state.pop("""type"""  )  )
																					lowerCamelCase__      :			Dict 						= add_prefix_space
																					lowerCamelCase__      :			Optional[int] 						= pre_tok_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			str 						= []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

														if len(UpperCamelCase__  ) > self.model_max_length:
																					lowerCamelCase__      :			int 						= input_ids[-self.model_max_length :]
														return input_ids

