


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_				:			Any   =	logging.get_logger(__name__)


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

UpperCAmelCase_				:			Union[str, Any]   =	{
    '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'
        )
    },
}

UpperCAmelCase_				:			str   =	{'facebook/blenderbot_small-90M': 512}







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

       """simple docstring"""

       a_ :		Tuple							=  set()
       a_ :		Any							=  word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)    )
              a_ :		Optional[Any]							=  char

       a_ :		Optional[Any]							=  set(__A    )
       return pairs
class 			_a  (    lowercase__      ):
      snake_case__    :					str					       =						VOCAB_FILES_NAMES
      snake_case__    :					Dict					       =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :					str					       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :					Any					       =						['''input_ids''', '''attention_mask''']
      def __init__(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Tuple="__start__"					,							SCREAMING_SNAKE_CASE__    :	List[Any]="__end__"					,							SCREAMING_SNAKE_CASE__    :	str="__unk__"					,							SCREAMING_SNAKE_CASE__    :	Dict="__null__"					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							)		->      int:
             super().__init__(unk_token=SCREAMING_SNAKE_CASE__					,							bos_token=SCREAMING_SNAKE_CASE__					,							eos_token=SCREAMING_SNAKE_CASE__					,							pad_token=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as vocab_handle:
                    a_ :		Dict							=  json.load(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  {v: k for k, v in self.encoder.items()}
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as merges_handle:
                    a_ :		str							=  merges_handle.read().split('\n'					)[1:-1]
             a_ :		Union[str, Any]							=  [tuple(merge.split()					) for merge in merges]
             a_ :		Tuple							=  dict(zip(SCREAMING_SNAKE_CASE__					,							range(len(SCREAMING_SNAKE_CASE__					)					)					)					)
             a_ :		Tuple							=  {}
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      int:
             return len(self.encoder					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Dict:
             return dict(self.encoder					,							**self.added_tokens_encoder					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             if token in self.cache:
                    return self.cache[token]
             a_ :		Any							=  re.sub('([.,!?()])'					,							r' \1'					,							SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  re.sub('(\')'					,							r' \1 '					,							SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[Any]							=  re.sub(r'\s{2,}'					,							' '					,							SCREAMING_SNAKE_CASE__					)
             if "\n" in token:
                    a_ :		str							=  token.replace('\n'					,							' __newln__'					)

             a_ :		Tuple							=  token.split(' '					)
             a_ :		Any							=  []
             for token in tokens:
                    if not len(SCREAMING_SNAKE_CASE__					):
                           continue

                    a_ :		Union[str, Any]							=  token.lower()
                    a_ :		Dict							=  tuple(SCREAMING_SNAKE_CASE__					)
                    a_ :		Any							=  tuple(list(word[:-1]					) + [word[-1] + '</w>']					)
                    a_ :		List[Any]							=  get_pairs(SCREAMING_SNAKE_CASE__					)

                    if not pairs:
                           words.append(SCREAMING_SNAKE_CASE__					)
                           continue

                    while True:
                           a_ :		Any							=  min(SCREAMING_SNAKE_CASE__					,							key=lambda SCREAMING_SNAKE_CASE__					: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__					,							float('inf'					)					)					)
                           if bigram not in self.bpe_ranks:
                                  break
                           a_ :		Union[str, Any]							=  bigram
                           a_ :		Dict							=  []
                           a_ :		Dict							=  0

                           while i < len(SCREAMING_SNAKE_CASE__					):
                                  try:
                                         a_ :		Optional[Any]							=  word.index(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                                         new_word.extend(word[i:j]					)
                                         a_ :		Any							=  j
                                  except ValueError:
                                         new_word.extend(word[i:]					)
                                         break

                                  if word[i] == first and i < len(SCREAMING_SNAKE_CASE__					) - 1 and word[i + 1] == second:
                                         new_word.append(first + second					)
                                         i += 2
                                  else:
                                         new_word.append(word[i]					)
                                         i += 1
                           a_ :		Any							=  tuple(SCREAMING_SNAKE_CASE__					)
                           a_ :		List[str]							=  new_word
                           if len(SCREAMING_SNAKE_CASE__					) == 1:
                                  break
                           else:
                                  a_ :		int							=  get_pairs(SCREAMING_SNAKE_CASE__					)
                    a_ :		str							=  '@@ '.join(SCREAMING_SNAKE_CASE__					)
                    a_ :		Tuple							=  word[:-4]

                    a_ :		List[Any]							=  word
                    words.append(SCREAMING_SNAKE_CASE__					)
             return " ".join(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      List[str]:
             a_ :		List[Any]							=  []

             a_ :		int							=  re.findall(r'\S+\n?'					,							SCREAMING_SNAKE_CASE__					)

             for token in words:
                    split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE__					).split(' '					)					)					)
             return split_tokens
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      int:
             a_ :		Optional[int]							=  token.lower()
             return self.encoder.get(SCREAMING_SNAKE_CASE__					,							self.encoder.get(self.unk_token					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	int					)		->      str:
             return self.decoder.get(SCREAMING_SNAKE_CASE__					,							self.unk_token					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      str:
             a_ :		int							=  ' '.join(SCREAMING_SNAKE_CASE__					).replace('@@ '					,							''					).strip()
             return out_string

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[str] = None					)		->      Tuple[str]:
             if not os.path.isdir(SCREAMING_SNAKE_CASE__					):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
                    return
             a_ :		str							=  os.path.join(
                 SCREAMING_SNAKE_CASE__					,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
             a_ :		Dict							=  os.path.join(
                 SCREAMING_SNAKE_CASE__					,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']					)

             with open(SCREAMING_SNAKE_CASE__					,							'w'					,							encoding='utf-8'					) as f:
                    f.write(json.dumps(self.encoder					,							indent=2					,							sort_keys=SCREAMING_SNAKE_CASE__					,							ensure_ascii=SCREAMING_SNAKE_CASE__					) + '\n'					)

             a_ :		Tuple							=  0
             with open(SCREAMING_SNAKE_CASE__					,							'w'					,							encoding='utf-8'					) as writer:
                    writer.write('#version: 0.2\n'					)
                    for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,							key=lambda SCREAMING_SNAKE_CASE__					: kv[1]					):
                           if index != token_index:
                                  logger.warning(
                                      F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                      ' Please check that the tokenizer is not corrupted!'					)
                                  a_ :		Optional[int]							=  token_index
                           writer.write(' '.join(SCREAMING_SNAKE_CASE__					) + '\n'					)
                           index += 1

             return vocab_file, merge_file






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    )


