


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,
)


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							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'''),
    ]
)

UpperCAmelCase__   	=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
  if class_name in extractors:
   _lowercase							      =model_type_to_module_name(__a						)

   _lowercase							      =importlib.import_module(F".{module_name}"     ,						'''transformers.models'''						)
   try:
    return getattr(__a     ,						__a						)
   except AttributeError:
    continue

 for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
  if getattr(__a     ,						'''__name__'''     ,						__a						) == 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.
 _lowercase							      =importlib.import_module('''transformers'''						)
 if hasattr(__a     ,						__a						):
  return getattr(__a     ,						__a						)

 return None
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case = None     ,						__snake_case = False     ,						__snake_case = False     ,						__snake_case = None     ,						__snake_case = None     ,						__snake_case = None     ,						__snake_case = False     ,						**__snake_case     ,						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =get_file_from_repo(
     __a     ,						__a     ,						cache_dir=__a     ,						force_download=__a     ,						resume_download=__a     ,						proxies=__a     ,						use_auth_token=__a     ,						revision=__a     ,						local_files_only=__a     ,						)
 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(__a     ,						encoding='''utf-8'''						) as reader:
  return json.load(__a						)







class       lowerCamelCase__   :





     def __init__(self       )     ->   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      __A					(cls		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Any:
      _lowercase							      =kwargs.pop('''config'''		,     __lowerCAmelCase       )
      _lowercase							      =kwargs.pop('''trust_remote_code'''		,     __lowerCAmelCase       )
      _lowercase							      =True

      _lowercase      ,    _lowercase							      =ImageProcessingMixin.get_image_processor_dict(__lowerCAmelCase		,     **__lowerCAmelCase       )
      _lowercase							      =config_dict.get('''image_processor_type'''		,     __lowerCAmelCase       )
      _lowercase							      =None
      if "AutoImageProcessor" in config_dict.get('''auto_map'''		,     {}       ):
       _lowercase							      =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:
       _lowercase							      =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.'''       )
        _lowercase							      =feature_extractor_class.replace('''FeatureExtractor'''		,     '''ImageProcessor'''       )
       if "AutoFeatureExtractor" in config_dict.get('''auto_map'''		,     {}       ):
        _lowercase							      =config_dict['''auto_map''']['''AutoFeatureExtractor''']
        _lowercase							      =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       ):
        _lowercase							      =AutoConfig.from_pretrained(__lowerCAmelCase		,     **__lowerCAmelCase       )
       # It could be in `config.image_processor_type``
       _lowercase							      =getattr(__lowerCAmelCase		,     '''image_processor_type'''		,     __lowerCAmelCase       )
       if hasattr(__lowerCAmelCase		,     '''auto_map'''       ) and "AutoImageProcessor" in config.auto_map:
        _lowercase							      =config.auto_map['''AutoImageProcessor''']

      if image_processor_class is not None:
       _lowercase							      =image_processor_class_from_name(__lowerCAmelCase       )

      _lowercase							      =image_processor_auto_map is not None
      _lowercase							      =image_processor_class is not None or type(__lowerCAmelCase       ) in IMAGE_PROCESSOR_MAPPING
      _lowercase							      =resolve_trust_remote_code(
          __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase       )

      if has_remote_code and trust_remote_code:
       _lowercase							      =get_class_from_dynamic_module(
           __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase       )
       _lowercase							      =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:
       _lowercase							      =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      __A					(UpperCAmelCase		,     UpperCAmelCase       )     ->   Dict:
      IMAGE_PROCESSOR_MAPPING.register(__lowerCAmelCase		,     __lowerCAmelCase       )








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   hidden_act
					A__        =   intermediate_size
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   initializer_range
					A__        =   layer_norm_eps
					A__        =   position_embedding_type
					A__        =   use_cache
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__        =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )



