




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,
)


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

lowerCamelCase							:						int					       =  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"),
    ]
)

lowerCamelCase							:						Optional[int]					       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str	):


      '''simple docstring'''



      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  lowerCamelCase_							=       model_type_to_module_name(lowercase	)

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

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

      return None

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, os.PathLike]      ,				lowercase      :			Optional[Union[str, os.PathLike]] = None      ,				lowercase      :			bool = False      ,				lowercase      :			bool = False      ,				lowercase      :			Optional[Dict[str, str]] = None      ,				lowercase      :			Optional[Union[bool, str]] = None      ,				lowercase      :			Optional[str] = None      ,				lowercase      :			bool = False      ,				**lowercase      :			str      ,				):


      '''simple docstring'''



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







class     A:



 '''simple docstring'''


 def __init__(							self   :    Optional[Any]    )  ->      Dict:

       """simple docstring"""






       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   :    List[Any]		,				A_   :    str		,				**A_   :    str    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       kwargs.pop('config'		,				A_    )
       lowerCamelCase_							=       kwargs.pop('trust_remote_code'		,				A_    )
       lowerCamelCase_							=       True

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

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

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

       if has_remote_code and trust_remote_code:
             lowerCamelCase_							=       get_class_from_dynamic_module(
                 A_		,				A_		,				**A_    )
             lowerCamelCase_							=       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:
             lowerCamelCase_							=       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_   :    Any		,				A_   :    List[str]    )  ->      List[str]:

       """simple docstring"""






       IMAGE_PROCESSOR_MAPPING.register(A_		,				A_    )







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase__  			=				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

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



