

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		:       List[Any]						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       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"""),
    ]
)

_UpperCAmelCase		:       Optional[Any]						    =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)





def  __lowerCamelCase     (			UpperCamelCase__				):







       '''simple docstring'''






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

                     snake_case_          =   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.
       snake_case_          =   importlib.import_module('transformers'				)
       if hasattr(__a       ,							__a				):
              return getattr(__a       ,							__a				)

       return None





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = False       ,							UpperCamelCase__ = False       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = False       ,							**UpperCamelCase__       ,							):







       '''simple docstring'''






       snake_case_          =   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 			lowercase    :




    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(_a	)
    def    a							( cls				,       snake_case				,       **snake_case	):
           snake_case_          =   kwargs.pop('config'				,       _a	)
           snake_case_          =   kwargs.pop('trust_remote_code'				,       _a	)
           snake_case_          =   True

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

           if image_processor_class is not None:
                  snake_case_          =   image_processor_class_from_name(_a	)

           snake_case_          =   image_processor_auto_map is not None
           snake_case_          =   image_processor_class is not None or type(_a	) in IMAGE_PROCESSOR_MAPPING
           snake_case_          =   resolve_trust_remote_code(
               _a				,       _a				,       _a				,       _a	)

           if has_remote_code and trust_remote_code:
                  snake_case_          =   get_class_from_dynamic_module(
                      _a				,       _a				,       **_a	)
                  snake_case_          =   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:
                  snake_case_          =   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							( snake_case				,       snake_case	):
           IMAGE_PROCESSOR_MAPPING.register(_a				,       _a	)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase		:       Dict						    =	{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Optional[int]						    =	["""BlipImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Any						    =	[
					    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BlipModel""",
					    """BlipPreTrainedModel""",
					    """BlipForConditionalGeneration""",
					    """BlipForQuestionAnswering""",
					    """BlipVisionModel""",
					    """BlipTextModel""",
					    """BlipForImageTextRetrieval""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Any						    =	[
					    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFBlipModel""",
					    """TFBlipPreTrainedModel""",
					    """TFBlipForConditionalGeneration""",
					    """TFBlipForQuestionAnswering""",
					    """TFBlipVisionModel""",
					    """TFBlipTextModel""",
					    """TFBlipForImageTextRetrieval""",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[int]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




