





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,
)


snake_case								=							logging.get_logger(__name__)

snake_case								=							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"""),
    ]
)

snake_case								=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str  )   ->    Optional[int]:
  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _lowercase						    =			model_type_to_module_name(snake_case__  )

      _lowercase						    =			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						    =			importlib.import_module('transformers'  )
  if hasattr(snake_case__      ,				snake_case__  ):
    return getattr(snake_case__      ,				snake_case__  )

  return None

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Union[str, os.PathLike]      ,				snake_case__	:Optional[Union[str, os.PathLike]] = None      ,				snake_case__	:bool = False      ,				snake_case__	:bool = False      ,				snake_case__	:Optional[Dict[str, str]] = None      ,				snake_case__	:Optional[Union[bool, str]] = None      ,				snake_case__	:Optional[str] = None      ,				snake_case__	:bool = False      ,				**snake_case__	:Any      ,				)   ->    Tuple:
  _lowercase						    =			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_					:







       """simple docstring"""





       def __init__(     self						:						Any	)    ->       Tuple:
         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  __UpperCAmelCase			(     cls						:						str    ,__A						:						Union[str, Any]    ,**__A						:						List[Any]	)    ->       List[Any]:
         _lowercase						    =			kwargs.pop('config'    ,__A	)
         _lowercase						    =			kwargs.pop('trust_remote_code'    ,__A	)
         _lowercase						    =			True

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

         if image_processor_class is not None:
           _lowercase						    =			image_processor_class_from_name(__A	)

         _lowercase						    =			image_processor_auto_map is not None
         _lowercase						    =			image_processor_class is not None or type(__A	) in IMAGE_PROCESSOR_MAPPING
         _lowercase						    =			resolve_trust_remote_code(
             __A    ,__A    ,__A    ,__A	)

         if has_remote_code and trust_remote_code:
           _lowercase						    =			get_class_from_dynamic_module(
               __A    ,__A    ,**__A	)
           _lowercase						    =			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:
           _lowercase						    =			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  __UpperCAmelCase			(     __A						:						Any    ,__A						:						int	)    ->       Union[str, Any]:
         IMAGE_PROCESSOR_MAPPING.register(__A    ,__A	)



'''simple docstring'''
from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							42
		_a       					=							None
		_a       					=							None



__snake_case				 =	namedtuple('''CoinsDistribResult''', '''moves excess''')


def      a (     __a  )    ->      int:



					'''simple docstring'''






					if root is None:
										return 0

					# Validation
					def count_nodes(__a  ) -> int:
										if node is None:
															return 0

										return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

					def count_coins(__a  ) -> int:
										if node is None:
															return 0

										return count_coins(node.left  ) + count_coins(node.right  ) + node.data

					if count_nodes(__a  ) != count_coins(__a  ):
										raise ValueError('''The nodes number should be same as the number of coins'''  )

					# Main calculation
					def get_distrib(__a  ) -> CoinsDistribResult:

										if node is None:
															return CoinsDistribResult(0					,   1  )

										UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					get_distrib(node.left  )
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					get_distrib(node.right  )

										UpperCamelCase__  :Tuple				   =					1 - left_distrib_excess
										UpperCamelCase__  :Optional[int]				   =					1 - right_distrib_excess

										UpperCamelCase__  :int				   =					(
										    left_distrib_moves
										    + right_distrib_moves
										    + abs(__a  )
										    + abs(__a  )
										)
										UpperCamelCase__  :Optional[Any]				   =					node.data - coins_to_left - coins_to_right

										return CoinsDistribResult(__a					,   __a  )

					return get_distrib(__a  )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()