


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str				):
     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               __UpperCamelCase    							=model_type_to_module_name(SCREAMING_SNAKE_CASE__				)

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

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

     return None

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, os.PathLike]			,     SCREAMING_SNAKE_CASE__			:					Optional[Union[str, os.PathLike]] = None			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     SCREAMING_SNAKE_CASE__			:					Optional[Dict[str, str]] = None			,     SCREAMING_SNAKE_CASE__			:					Optional[Union[bool, str]] = None			,     SCREAMING_SNAKE_CASE__			:					Optional[str] = None			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     **SCREAMING_SNAKE_CASE__			:					Dict			,     ):
     __UpperCamelCase    							=get_file_from_repo(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     cache_dir=SCREAMING_SNAKE_CASE__			,     force_download=SCREAMING_SNAKE_CASE__			,     resume_download=SCREAMING_SNAKE_CASE__			,     proxies=SCREAMING_SNAKE_CASE__			,     use_auth_token=SCREAMING_SNAKE_CASE__			,     revision=SCREAMING_SNAKE_CASE__			,     local_files_only=SCREAMING_SNAKE_CASE__			,     )
     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(SCREAMING_SNAKE_CASE__			,     encoding='utf-8'				) as reader:
          return json.load(SCREAMING_SNAKE_CASE__				)






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self    )  ->       Any:
         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(A_    )
    def _a					(			cls					,   A_					,   **A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=kwargs.pop('config'					,   A_    )
         __UpperCamelCase    							=kwargs.pop('trust_remote_code'					,   A_    )
         __UpperCamelCase    							=True

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

         if image_processor_class is not None:
              __UpperCamelCase    							=image_processor_class_from_name(A_    )

         __UpperCamelCase    							=image_processor_auto_map is not None
         __UpperCamelCase    							=image_processor_class is not None or type(A_    ) in IMAGE_PROCESSOR_MAPPING
         __UpperCamelCase    							=resolve_trust_remote_code(
             A_					,   A_					,   A_					,   A_    )

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

         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					(			A_					,   A_    )  ->       List[str]:
         IMAGE_PROCESSOR_MAPPING.register(A_					,   A_    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





