






'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


a_					=	logging.get_logger(__name__)

a_					=	OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

a_					=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)





def 	_a(					UpperCamelCase__			:  str							):




      '''simple docstring'''






      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=model_type_to_module_name(UpperCamelCase__							)

                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=importlib.import_module(f".{module_name}",		'''transformers.models'''							)
                  try:
                        return getattr(UpperCamelCase__,		UpperCamelCase__							)
                  except AttributeError:
                        continue

      for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
            if getattr(UpperCamelCase__,		'''__name__''',		UpperCamelCase__							) == class_name:
                  return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      SCREAMING_SNAKE_CASE__       :			List[str]													=importlib.import_module('''transformers'''							)
      if hasattr(UpperCamelCase__,		UpperCamelCase__							):
            return getattr(UpperCamelCase__,		UpperCamelCase__							)

      return None





def 	_a(					UpperCamelCase__			:  Union[str, os.PathLike],		UpperCamelCase__			:  Optional[Union[str, os.PathLike]] = None,		UpperCamelCase__			:  bool = False,		UpperCamelCase__			:  bool = False,		UpperCamelCase__			:  Optional[Dict[str, str]] = None,		UpperCamelCase__			:  Optional[Union[bool, str]] = None,		UpperCamelCase__			:  Optional[str] = None,		UpperCamelCase__			:  bool = False,		**UpperCamelCase__			:  Any,		):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=get_file_from_repo(
          UpperCamelCase__,		UpperCamelCase__,		cache_dir=UpperCamelCase__,		force_download=UpperCamelCase__,		resume_download=UpperCamelCase__,		proxies=UpperCamelCase__,		use_auth_token=UpperCamelCase__,		revision=UpperCamelCase__,		local_files_only=UpperCamelCase__,		)
      if resolved_config_file is None:
            logger.info(
                '''Could not locate the image processor configuration file, will try to use the model config instead.'''							)
            return {}

      with open(UpperCamelCase__,		encoding='''utf-8'''							) as reader:
            return json.load(UpperCamelCase__							)







class        __SCREAMING_SNAKE_CASE :




      def __init__( self		:							Any )		->  Dict:
            raise EnvironmentError(
                '''AutoImageProcessor is designed to be instantiated '''
                '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.''' )




      @classmethod
      @replace_list_option_in_docstrings(__lowerCamelCase )
      def        __magic_name__		( cls		:							List[str]   , __lowercase		:							List[Any]   , **__lowercase		:							int )		->  int:
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=kwargs.pop('''config'''   , __lowerCamelCase )
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=kwargs.pop('''trust_remote_code'''   , __lowerCamelCase )
            SCREAMING_SNAKE_CASE__       :			Any													=True

            SCREAMING_SNAKE_CASE__       :			int													=ImageProcessingMixin.get_image_processor_dict(__lowerCamelCase   , **__lowerCamelCase )
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=config_dict.get('''image_processor_type'''   , __lowerCamelCase )
            SCREAMING_SNAKE_CASE__       :			int													=None
            if "AutoImageProcessor" in config_dict.get('''auto_map'''   , {} ):
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=config_dict["""auto_map"""]["""AutoImageProcessor"""]

            # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
            # and if so, infer the image processor class from there.
            if image_processor_class is None and image_processor_auto_map is None:
                  SCREAMING_SNAKE_CASE__       :			Optional[int]													=config_dict.pop('''feature_extractor_type'''   , __lowerCamelCase )
                  if feature_extractor_class is not None:
                        logger.warning(
                            '''Could not find image processor class in the image processor config or the model config. Loading'''
                            ''' based on pattern matching with the model\'s feature extractor configuration.''' )
                        SCREAMING_SNAKE_CASE__       :			str													=feature_extractor_class.replace('''FeatureExtractor'''   , '''ImageProcessor''' )
                  if "AutoFeatureExtractor" in config_dict.get('''auto_map'''   , {} ):
                        SCREAMING_SNAKE_CASE__       :			Any													=config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
                        SCREAMING_SNAKE_CASE__       :			Dict													=feature_extractor_auto_map.replace('''FeatureExtractor'''   , '''ImageProcessor''' )
                        logger.warning(
                            '''Could not find image processor auto map in the image processor config or the model config.'''
                            ''' Loading based on pattern matching with the model\'s feature extractor configuration.''' )

        # If we don't find the image processor class in the image processor config, let's try the model config.
            if image_processor_class is None and image_processor_auto_map is None:
                  if not isinstance(__lowerCamelCase   , __lowerCamelCase ):
                        SCREAMING_SNAKE_CASE__       :			str													=AutoConfig.from_pretrained(__lowerCamelCase   , **__lowerCamelCase )
                  # It could be in `config.image_processor_type``
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=getattr(__lowerCamelCase   , '''image_processor_type'''   , __lowerCamelCase )
                  if hasattr(__lowerCamelCase   , '''auto_map''' ) and "AutoImageProcessor" in config.auto_map:
                        SCREAMING_SNAKE_CASE__       :			Any													=config.auto_map["""AutoImageProcessor"""]

            if image_processor_class is not None:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=image_processor_class_from_name(__lowerCamelCase )

            SCREAMING_SNAKE_CASE__       :			List[Any]													=image_processor_auto_map is not None
            SCREAMING_SNAKE_CASE__       :			Any													=image_processor_class is not None or type(__lowerCamelCase ) in IMAGE_PROCESSOR_MAPPING
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=resolve_trust_remote_code(
                __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase )

            if has_remote_code and trust_remote_code:
                  SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_class_from_dynamic_module(
                      __lowerCamelCase   , __lowerCamelCase   , **__lowerCamelCase )
                  SCREAMING_SNAKE_CASE__       :			int													=kwargs.pop('''code_revision'''   , __lowerCamelCase )
                  if os.path.isdir(__lowerCamelCase ):
                        image_processor_class.register_for_auto_class()
                  return image_processor_class.from_dict(__lowerCamelCase   , **__lowerCamelCase )
            elif image_processor_class is not None:
                  return image_processor_class.from_dict(__lowerCamelCase   , **__lowerCamelCase )
            # Last try: we use the IMAGE_PROCESSOR_MAPPING.
            elif type(__lowerCamelCase ) in IMAGE_PROCESSOR_MAPPING:
                  SCREAMING_SNAKE_CASE__       :			int													=IMAGE_PROCESSOR_MAPPING[type(__lowerCamelCase )]
                  return image_processor_class.from_dict(__lowerCamelCase   , **__lowerCamelCase )

            raise ValueError(
                F"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
                F"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
                F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}" )




      @staticmethod
      def        __magic_name__		( __lowercase		:							int   , __lowercase		:							Union[str, Any] )		->  Any:
            IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase   , __lowerCamelCase )







'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


a_					=	logging.get_logger(__name__)


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

a_					=	{
    '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'
        )
    },
}

a_					=	{'facebook/blenderbot_small-90M': 5_1_2}





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__       :			Any													=set(UpperCamelCase__							)
      return pairs







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							VOCAB_FILES_NAMES
 snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_												=							["""input_ids""", """attention_mask"""]




 def __init__( self		:							Tuple   , __lowercase		:							int   , __lowercase		:							Optional[int]   , __lowercase		:							List[str]="__start__"   , __lowercase		:							Union[str, Any]="__end__"   , __lowercase		:							str="__unk__"   , __lowercase		:							Union[str, Any]="__null__"   , **__lowercase		:							List[str]   , )		->  Optional[int]:
       super().__init__(unk_token=__lowercase   , bos_token=__lowercase   , eos_token=__lowercase   , pad_token=__lowercase   , **__lowercase )

       with open(__lowercase   , encoding='''utf-8''' ) as vocab_handle:
             SCREAMING_SNAKE_CASE__       :			Any													=json.load(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													={v: k for k, v in self.encoder.items()}
       with open(__lowercase   , encoding='''utf-8''' ) as merges_handle:
             SCREAMING_SNAKE_CASE__       :			int													=merges_handle.read().split('''\n''' )[1:-1]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=[tuple(merge.split() ) for merge in merges]
       SCREAMING_SNAKE_CASE__       :			int													=dict(zip(__lowercase   , range(len(__lowercase ) ) ) )
       SCREAMING_SNAKE_CASE__       :			Dict													={}




 @property
 def        __magic_name__		( self		:							Any )		->  int:
       return len(self.encoder )




 def        __magic_name__		( self		:							Tuple )		->  Dict:
       return dict(self.encoder   , **self.added_tokens_encoder )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							str )		->  str:
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=re.sub('''([.,!?()])'''   , r''' \1'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=re.sub('''(\')'''   , r''' \1 '''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=re.sub(r'''\s{2,}'''   , ''' '''   , __lowercase )
       if "\n" in token:
             SCREAMING_SNAKE_CASE__       :			List[str]													=token.replace('''\n'''   , ''' __newln__''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=token.split(''' ''' )
       SCREAMING_SNAKE_CASE__       :			Dict													=[]
       for token in tokens:
             if not len(__lowercase ):
                   continue

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

             if not pairs:
                   words.append(__lowercase )
                   continue

             while True:
                   SCREAMING_SNAKE_CASE__       :			int													=min(__lowercase   , key=lambda __lowercase : self.bpe_ranks.get(__lowercase   , float('''inf''' ) ) )
                   if bigram not in self.bpe_ranks:
                         break
                   SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=bigram
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
                   SCREAMING_SNAKE_CASE__       :			Tuple													=0

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

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

             SCREAMING_SNAKE_CASE__       :			str													=word
             words.append(__lowercase )
       return " ".join(__lowercase )




 def        __magic_name__		( self		:							str   , __lowercase		:							str )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=[]

       SCREAMING_SNAKE_CASE__       :			List[Any]													=re.findall(r'''\S+\n?'''   , __lowercase )

       for token in words:
             split_tokens.extend(list(self.bpe(__lowercase ).split(''' ''' ) ) )
       return split_tokens




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  int:
       SCREAMING_SNAKE_CASE__       :			int													=token.lower()
       return self.encoder.get(__lowercase   , self.encoder.get(self.unk_token ) )




 def        __magic_name__		( self		:							int   , __lowercase		:							int )		->  str:
       return self.decoder.get(__lowercase   , self.unk_token )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							List[str] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=''' '''.join(__lowercase ).replace('''@@ '''   , '''''' ).strip()
       return out_string




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

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

       SCREAMING_SNAKE_CASE__       :			List[str]													=0
       with open(__lowercase   , '''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 __lowercase : 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!''' )
                         SCREAMING_SNAKE_CASE__       :			Any													=token_index
                   writer.write(''' '''.join(__lowercase ) + '''\n''' )
                   index += 1

       return vocab_file, merge_file