




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_		      = logging.get_logger(__name__)

lowerCAmelCase_		      = 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_		      = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def 					snake_case(				__magic_name__    ) ->							Any:





     '''simple docstring'''


     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               lowercase					:   Union[str, Any]								= model_type_to_module_name(_snake_case    )

               lowercase					:   List[Any]								= 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.
     lowercase					:   Dict								= importlib.import_module('''transformers'''    )
     if hasattr(_snake_case ,		_snake_case    ):
          return getattr(_snake_case ,		_snake_case    )

     return None






def 					snake_case(				__magic_name__ ,		__magic_name__ = None ,		__magic_name__ = False ,		__magic_name__ = False ,		__magic_name__ = None ,		__magic_name__ = None ,		__magic_name__ = None ,		__magic_name__ = False ,		**__magic_name__ ,		) ->							int:





     '''simple docstring'''


     lowercase					:   str								= 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 						_A					:






  def __init__(  self		:	Any      )		->    Any:






       """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(UpperCamelCase_      )
  def 					__a			(  cls		:	Union[str, Any]      ,						_A		:	List[Any]      ,						**_A		:	str      )		->    Optional[int]:






       """simple docstring"""







       lowercase					:   Tuple								= kwargs.pop('''config'''      ,						UpperCamelCase_      )
       lowercase					:   Dict								= kwargs.pop('''trust_remote_code'''      ,						UpperCamelCase_      )
       lowercase					:   Optional[int]								= True

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

       if image_processor_class is not None:
            lowercase					:   List[str]								= image_processor_class_from_name(UpperCamelCase_      )

       lowercase					:   int								= image_processor_auto_map is not None
       lowercase					:   List[str]								= image_processor_class is not None or type(UpperCamelCase_      ) in IMAGE_PROCESSOR_MAPPING
       lowercase					:   Tuple								= resolve_trust_remote_code(
           UpperCamelCase_      ,						UpperCamelCase_      ,						UpperCamelCase_      ,						UpperCamelCase_      )

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

       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		:	Any      )		->    Tuple:






       """simple docstring"""







       IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase_      ,						UpperCamelCase_      )



"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


