



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

a_						:     Optional[int]	     =		logging.get_logger(__name__)


a_						:     Optional[Any]	     =		{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_						:     Dict	     =		{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

a_						:     Optional[int]	     =		{'facebook/blenderbot-3B': 1_2_8}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		(
          list(range(ord("!" )   ,   ord("~" ) + 1 ) ) + list(range(ord("¡" )   ,   ord("¬" ) + 1 ) ) + list(range(ord("®" )   ,   ord("ÿ" ) + 1 ) )
      )
      lowerCamelCase								=		bs[:]
      lowerCamelCase								=		0
      for b in range(2**8 ):
            if b not in bs:
                  bs.append(UpperCAmelCase__ )
                  cs.append(2**8 + n )
                  n += 1
      lowerCamelCase								=		[chr(UpperCAmelCase__ ) for n in cs]
      return dict(zip(UpperCAmelCase__   ,   UpperCAmelCase__ ) )

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		set()
      lowerCamelCase								=		word[0]
      for char in word[1:]:
            pairs.add((prev_char, char) )
            lowerCamelCase								=		char
      return pairs






class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			['input_ids', 'attention_mask']







     def __init__(self				,			__a				,			__a				,			__a="replace"				,			__a="<s>"				,			__a="</s>"				,			__a="</s>"				,			__a="<s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<mask>"				,			__a=False				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else bos_token
           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else eos_token
           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else sep_token
           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else cls_token
           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else unk_token
           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else pad_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else mask_token

           super().__init__(
               errors=__a				,			bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			sep_token=__a				,			cls_token=__a				,			pad_token=__a				,			mask_token=__a				,			add_prefix_space=__a				,			**__a				,			)

           with open(__a				,			encoding="utf-8"    ) as vocab_handle:
                 lowerCamelCase								=		json.load(__a    )
           lowerCamelCase								=		{v: k for k, v in self.encoder.items()}
           lowerCamelCase								=		errors  # how to handle errors in decoding
           lowerCamelCase								=		bytes_to_unicode()
           lowerCamelCase								=		{v: k for k, v in self.byte_encoder.items()}
           with open(__a				,			encoding="utf-8"    ) as merges_handle:
                 lowerCamelCase								=		merges_handle.read().split("\n"    )[1:-1]
           lowerCamelCase								=		[tuple(merge.split()    ) for merge in bpe_merges]
           lowerCamelCase								=		dict(zip(__a				,			range(len(__a    )    )    )    )
           lowerCamelCase								=		{}
           lowerCamelCase								=		add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           lowerCamelCase								=		re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
     def 						_a      (self    ):






           '''simple docstring'''



           return len(self.encoder    )







     def 						_a      (self    ):






           '''simple docstring'''



           return dict(self.encoder				,			**self.added_tokens_encoder    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           if token in self.cache:
                 return self.cache[token]
           lowerCamelCase								=		tuple(__a    )
           lowerCamelCase								=		get_pairs(__a    )

           if not pairs:
                 return token

           while True:
                 lowerCamelCase								=		min(__a				,			key=lambda __a    : self.bpe_ranks.get(__a				,			float("inf"    )    )    )
                 if bigram not in self.bpe_ranks:
                       break
                 lowerCamelCase			,							lowerCamelCase								=		bigram
                 lowerCamelCase								=		[]
                 lowerCamelCase								=		0
                 while i < len(__a    ):
                       try:
                             lowerCamelCase								=		word.index(__a				,			__a    )
                       except ValueError:
                             new_word.extend(word[i:]    )
                             break
                       else:
                             new_word.extend(word[i:j]    )
                             lowerCamelCase								=		j

                       if word[i] == first and i < len(__a    ) - 1 and word[i + 1] == second:
                             new_word.append(first + second    )
                             i += 2
                       else:
                             new_word.append(word[i]    )
                             i += 1
                 lowerCamelCase								=		tuple(__a    )
                 lowerCamelCase								=		new_word
                 if len(__a    ) == 1:
                       break
                 else:
                       lowerCamelCase								=		get_pairs(__a    )
           lowerCamelCase								=		" ".join(__a    )
           lowerCamelCase								=		word
           return word







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           for token in re.findall(self.pat				,			__a    ):
                 lowerCamelCase								=		"".join(
                     self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__a    ).split(" "    )    )
           return bpe_tokens







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.encoder.get(__a				,			self.encoder.get(self.unk_token    )    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.decoder.get(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		"".join(__a    )
           lowerCamelCase								=		bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"				,			errors=self.errors    )
           return text







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

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

           lowerCamelCase								=		0
           with open(__a				,			"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 __a    : 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!"    )
                             lowerCamelCase								=		token_index
                       writer.write(" ".join(__a    ) + "\n"    )
                       index += 1

           return vocab_file, merge_file







     def 						_a      (self				,			__a				,			__a = None				,			__a = False    ):






           '''simple docstring'''



           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__a				,			token_ids_a=__a				,			already_has_special_tokens=__a    )

           if token_ids_a is None:
                 return [1] + ([0] * len(__a    )) + [1]
           return [1] + ([0] * len(__a    )) + [1, 1] + ([0] * len(__a    )) + [1]







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[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]







     def 						_a      (self				,			__a				,			__a=False				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		kwargs.pop("add_prefix_space"				,			self.add_prefix_space    )
           if (is_split_into_words or add_prefix_space) and (len(__a    ) > 0 and not text[0].isspace()):
                 lowerCamelCase								=		" " + text
           return (text, kwargs)







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           return token_ids_a + [self.eos_token_id]







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           for is_user, text in conversation.iter_texts():
                 if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(" " + text    )
                 else:
                       # Generated responses should contain them already.
                       inputs.append(__a    )

           lowerCamelCase								=		"  ".join(__a    )
           lowerCamelCase								=		self.encode(__a    )
           if len(__a    ) > self.model_max_length:
                 lowerCamelCase								=		input_ids[-self.model_max_length :]
                 logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
           return input_ids




import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a_						:     Tuple	     =		'src/transformers'
a_						:     Any	     =		'docs/source/en/tasks'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      with open(UpperCAmelCase__   ,   "r"   ,   encoding="utf-8"   ,   newline="\n" ) as f:
            lowerCamelCase								=		f.readlines()
      # Find the start prompt.
      lowerCamelCase								=		0
      while not lines[start_index].startswith(UpperCAmelCase__ ):
            start_index += 1
      start_index += 1

      lowerCamelCase								=		start_index
      while not lines[end_index].startswith(UpperCAmelCase__ ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index] ) <= 1:
            start_index += 1
      while len(lines[end_index] ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
a_						:     Union[str, Any]	     =		direct_transformers_import(TRANSFORMERS_PATH)

a_						:     List[Any]	     =		{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
a_						:     Union[str, Any]	     =		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase								=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__   ,   set() )
      lowerCamelCase								=		{
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		_find_text_in_file(
          filename=os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"   ,   end_prompt="<!--End of the generated tip-->"   ,   )

      lowerCamelCase								=		get_model_list_for_task(UpperCAmelCase__ )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   "w"   ,   encoding="utf-8"   ,   newline="\n" ) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
            else:
                  raise ValueError(
                      F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
                      " to fix this." )


if __name__ == "__main__":
     a_						:     str	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     str	     =		parser.parse_args()

     for task_guide in TASK_GUIDE_TO_MODELS.keys():
          check_model_list_for_task(task_guide, args.fix_and_overwrite)