


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


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

UpperCAmelCase_				:			List[str]   =	{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_				:			Tuple   =	{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

UpperCAmelCase_				:			Any   =	{"""mobilebert-uncased""": 512}


UpperCAmelCase_				:			int   =	{}
class 			_a  (    lowerCAmelCase_      ):
      snake_case__    :					Dict					       =						VOCAB_FILES_NAMES
      snake_case__    :					List[Any]					       =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :					Tuple					       =						PRETRAINED_INIT_CONFIGURATION
      snake_case__    :					Optional[Any]					       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :					Any					       =						MobileBertTokenizer
      def __init__(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=None					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Dict=True					,							SCREAMING_SNAKE_CASE__    :	int="[UNK]"					,							SCREAMING_SNAKE_CASE__    :	Dict="[SEP]"					,							SCREAMING_SNAKE_CASE__    :	Dict="[PAD]"					,							SCREAMING_SNAKE_CASE__    :	Dict="[CLS]"					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]="[MASK]"					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=True					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							)		->      Dict:
             super().__init__(
                 __SCREAMING_SNAKE_CASE					,							tokenizer_file=__SCREAMING_SNAKE_CASE					,							do_lower_case=__SCREAMING_SNAKE_CASE					,							unk_token=__SCREAMING_SNAKE_CASE					,							sep_token=__SCREAMING_SNAKE_CASE					,							pad_token=__SCREAMING_SNAKE_CASE					,							cls_token=__SCREAMING_SNAKE_CASE					,							mask_token=__SCREAMING_SNAKE_CASE					,							tokenize_chinese_chars=__SCREAMING_SNAKE_CASE					,							strip_accents=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)

             a_ :		Any							=  json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
             if (
                 normalizer_state.get('lowercase'					,							__SCREAMING_SNAKE_CASE					) != do_lower_case
                 or normalizer_state.get('strip_accents'					,							__SCREAMING_SNAKE_CASE					) != strip_accents
                 or normalizer_state.get('handle_chinese_chars'					,							__SCREAMING_SNAKE_CASE					) != tokenize_chinese_chars
             ):
                    a_ :		Dict							=  getattr(__SCREAMING_SNAKE_CASE					,							normalizer_state.pop('type'					)					)
                    a_ :		Dict							=  do_lower_case
                    a_ :		Any							=  strip_accents
                    a_ :		Tuple							=  tokenize_chinese_chars
                    a_ :		Optional[int]							=  normalizer_class(**__SCREAMING_SNAKE_CASE					)

             a_ :		Union[str, Any]							=  do_lower_case
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[Any]=None					)		->      Optional[Any]:
             a_ :		List[Any]							=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

             if token_ids_a:
                    output += token_ids_a + [self.sep_token_id]

             return output
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					)		->      Optional[Any]:
             a_ :		List[Any]							=  [self.sep_token_id]
             a_ :		int							=  [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					) * [0] + len(token_ids_a + sep					) * [1]

      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[str] = None					)		->      Tuple:
             a_ :		List[Any]							=  self._tokenizer.model.save(__SCREAMING_SNAKE_CASE					,							name=__SCREAMING_SNAKE_CASE					)
             return tuple(__SCREAMING_SNAKE_CASE					)






import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  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
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  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
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              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    )


