



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,
)


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

SCREAMING_SNAKE_CASE_:Optional[Any]					=						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"""),
    ]
)

SCREAMING_SNAKE_CASE_:List[str]					=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            A			:	Any										=       model_type_to_module_name(snake_case__		)

            A			:	str										=       importlib.import_module(f'''.{module_name}'''				,					"""transformers.models"""		)
            try:
                return getattr(snake_case__				,					snake_case__		)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(snake_case__				,					"""__name__"""				,					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.
    A			:	Tuple										=       importlib.import_module("""transformers"""		)
    if hasattr(snake_case__				,					snake_case__		):
        return getattr(snake_case__				,					snake_case__		)

    return None




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase = None				,					_lowerCAmelCase = False				,					_lowerCAmelCase = False				,					_lowerCAmelCase = None				,					_lowerCAmelCase = None				,					_lowerCAmelCase = None				,					_lowerCAmelCase = False				,					**_lowerCAmelCase				,					)	->  Any:



    """simple docstring"""






    A			:	Optional[int]										=       get_file_from_repo(
        snake_case__				,					snake_case__				,					cache_dir=snake_case__				,					force_download=snake_case__				,					resume_download=snake_case__				,					proxies=snake_case__				,					use_auth_token=snake_case__				,					revision=snake_case__				,					local_files_only=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(snake_case__				,					encoding="""utf-8"""		) as reader:
        return json.load(snake_case__		)






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self		):
        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       _lowerCAmelCase (	cls,		lowerCamelCase__,		**lowerCamelCase__		):
        A			:	int										=       kwargs.pop("""config""",		lowerCAmelCase_		)
        A			:	Optional[int]										=       kwargs.pop("""trust_remote_code""",		lowerCAmelCase_		)
        A			:	Dict										=       True

        A					,							A			:	int										=       ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase_,		**lowerCAmelCase_		)
        A			:	Union[str, Any]										=       config_dict.get("""image_processor_type""",		lowerCAmelCase_		)
        A			:	Dict										=       None
        if "AutoImageProcessor" in config_dict.get("""auto_map""",		{}		):
            A			:	List[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:
            A			:	Tuple										=       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."""		)
                A			:	List[str]										=       feature_extractor_class.replace("""FeatureExtractor""",		"""ImageProcessor"""		)
            if "AutoFeatureExtractor" in config_dict.get("""auto_map""",		{}		):
                A			:	str										=       config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
                A			:	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_		):
                A			:	List[str]										=       AutoConfig.from_pretrained(lowerCAmelCase_,		**lowerCAmelCase_		)
            # It could be in `config.image_processor_type``
            A			:	List[str]										=       getattr(lowerCAmelCase_,		"""image_processor_type""",		lowerCAmelCase_		)
            if hasattr(lowerCAmelCase_,		"""auto_map"""		) and "AutoImageProcessor" in config.auto_map:
                A			:	Any										=       config.auto_map["""AutoImageProcessor"""]

        if image_processor_class is not None:
            A			:	Dict										=       image_processor_class_from_name(lowerCAmelCase_		)

        A			:	Union[str, Any]										=       image_processor_auto_map is not None
        A			:	Dict										=       image_processor_class is not None or type(lowerCAmelCase_		) in IMAGE_PROCESSOR_MAPPING
        A			:	Any										=       resolve_trust_remote_code(
            lowerCAmelCase_,		lowerCAmelCase_,		lowerCAmelCase_,		lowerCAmelCase_		)

        if has_remote_code and trust_remote_code:
            A			:	Tuple										=       get_class_from_dynamic_module(
                lowerCAmelCase_,		lowerCAmelCase_,		**lowerCAmelCase_		)
            A			:	str										=       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:
            A			:	List[str]										=       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       _lowerCAmelCase (	lowerCamelCase__,		lowerCamelCase__		):
        IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_,		lowerCAmelCase_		)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


