
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__		:     str     			=      logging.get_logger(__name__)

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






def   lowerCamelCase__							(					a      )      ->   int:
   for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
      if class_name in extractors:
         _A:  Tuple					=       model_type_to_module_name(a      )

         _A:  Tuple					=       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.
   _A:  Any					=       importlib.import_module('''transformers'''      )
   if hasattr(a		,		a      ):
      return getattr(a		,		a      )

   return None






def   lowerCamelCase__							(					a		,		a = None		,		a = False		,		a = False		,		a = None		,		a = None		,		a = None		,		a = False		,		**a		,		)      ->   Tuple:
   _A:  Optional[int]					=       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 UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							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(lowerCAmelCase_     )
   def  __magic_name__				(					cls     :							Dict			,			lowerCAmelCase_     :							Union[str, Any]			,			**lowerCAmelCase_     :							List[Any]     ):

      """simple docstring"""
      _A:  Optional[int]					=       kwargs.pop('''config'''			,			lowerCAmelCase_     )
      _A:  str					=       kwargs.pop('''trust_remote_code'''			,			lowerCAmelCase_     )
      _A:  List[str]					=       True

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

      if image_processor_class is not None:
         _A:  str					=       image_processor_class_from_name(lowerCAmelCase_     )

      _A:  List[Any]					=       image_processor_auto_map is not None
      _A:  Optional[Any]					=       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:  Optional[Any]					=       get_class_from_dynamic_module(
             lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_     )
         _A:  List[Any]					=       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:  Any					=       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  __magic_name__				(					lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_			,			lowerCAmelCase_     )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__		:     Dict     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''swin2sr'''

   __UpperCamelCase		:    str 				=    {
       '''hidden_size''': '''embed_dim''',
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int=6_4			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Dict=3			,			lowerCAmelCase_     :							Dict=1_8_0			,			lowerCAmelCase_     :							Union[str, Any]=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							Tuple=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							int=8			,			lowerCAmelCase_     :							Any=2.0			,			lowerCAmelCase_     :							Union[str, Any]=True			,			lowerCAmelCase_     :							Dict=0.0			,			lowerCAmelCase_     :							Optional[int]=0.0			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Any="gelu"			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							str=0.02			,			lowerCAmelCase_     :							Optional[Any]=1e-5			,			lowerCAmelCase_     :							int=2			,			lowerCAmelCase_     :							int=1.0			,			lowerCAmelCase_     :							Any="1conv"			,			lowerCAmelCase_     :							List[str]="pixelshuffle"			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[str]					=       image_size
      _A:  Any					=       patch_size
      _A:  Any					=       num_channels
      _A:  Union[str, Any]					=       embed_dim
      _A:  int					=       depths
      _A:  List[Any]					=       len(lowerCAmelCase_     )
      _A:  int					=       num_heads
      _A:  Any					=       window_size
      _A:  Optional[int]					=       mlp_ratio
      _A:  int					=       qkv_bias
      _A:  List[Any]					=       hidden_dropout_prob
      _A:  List[str]					=       attention_probs_dropout_prob
      _A:  List[Any]					=       drop_path_rate
      _A:  Any					=       hidden_act
      _A:  List[str]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  str					=       initializer_range
      _A:  int					=       upscale
      _A:  int					=       img_range
      _A:  Optional[Any]					=       resi_connection
      _A:  int					=       upsampler



