






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,
)


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_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'''),
    ]
)

SCREAMING_SNAKE_CASE__				     =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def        UpperCAmelCase__		(						lowerCamelCase_       :       str				):
			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									__a						:  Tuple									=     model_type_to_module_name(lowerCAmelCase_				)

									__a						:  Union[str, Any]									=     importlib.import_module(f'''.{module_name}'''   ,  'transformers.models'				)
									try:
												return getattr(lowerCAmelCase_   ,  lowerCAmelCase_				)
									except AttributeError:
												continue

			for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
						if getattr(lowerCAmelCase_   ,  '__name__'   ,  lowerCAmelCase_				) == 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						:  List[Any]									=     importlib.import_module('transformers'				)
			if hasattr(lowerCAmelCase_   ,  lowerCAmelCase_				):
						return getattr(lowerCAmelCase_   ,  lowerCAmelCase_				)

			return None

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Optional[Any] = None   ,  lowerCamelCase_       :       Tuple = False   ,  lowerCamelCase_       :       Union[str, Any] = False   ,  lowerCamelCase_       :       int = None   ,  lowerCamelCase_       :       Union[str, Any] = None   ,  lowerCamelCase_       :       Tuple = None   ,  lowerCamelCase_       :       Any = False   ,  **lowerCamelCase_       :       List[Any]   ,  ):
			__a						:  Dict									=     get_file_from_repo(
			    lowerCAmelCase_   ,  lowerCAmelCase_   ,  cache_dir=lowerCAmelCase_   ,  force_download=lowerCAmelCase_   ,  resume_download=lowerCAmelCase_   ,  proxies=lowerCAmelCase_   ,  use_auth_token=lowerCAmelCase_   ,  revision=lowerCAmelCase_   ,  local_files_only=lowerCAmelCase_   ,  )
			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(lowerCAmelCase_   ,  encoding='utf-8'				) as reader:
						return json.load(lowerCAmelCase_				)






class 			_UpperCamelCase:

		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    __lowerCAmelCase  (					cls		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       **SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     kwargs.pop('config'    ,       __lowerCamelCase    )
					__a						:  Optional[Any]									=     kwargs.pop('trust_remote_code'    ,       __lowerCamelCase    )
					__a						:  Optional[Any]									=     True

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

					if image_processor_class is not None:
								__a						:  int									=     image_processor_class_from_name(__lowerCamelCase    )

					__a						:  Any									=     image_processor_auto_map is not None
					__a						:  Dict									=     image_processor_class is not None or type(__lowerCamelCase    ) in IMAGE_PROCESSOR_MAPPING
					__a						:  int									=     resolve_trust_remote_code(
					    __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    )

					if has_remote_code and trust_remote_code:
								__a						:  Tuple									=     get_class_from_dynamic_module(
								    __lowerCamelCase    ,       __lowerCamelCase    ,       **__lowerCamelCase    )
								__a						:  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    __lowerCAmelCase  (					SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase    ,       __lowerCamelCase    )










"""simple docstring"""






from math import sqrt




def 			snake_case		(		lowerCAmelCase_ = 1000000	)						->				int:
  _snake_case   	=      0
  _snake_case   	=      0
  _snake_case   	=      42

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2  ,						2 * max_cuboid_size + 1	):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2	).is_integer():
        num_cuboids += (
            min(lowerCAmelCase_  ,						sum_shortest_sides // 2	)
            - max(1  ,						sum_shortest_sides - max_cuboid_size	)
            + 1
        )

  return max_cuboid_size


if __name__ == "__main__":
     print(F"{solution() = }")





