


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a__				:							List[str]						=     logging.get_logger(__name__)


if is_vision_available():
		import PIL




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Optional[Any]    					=			["pixel_values"]




	def __init__( self			:				Optional[Any]			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				PILImageResampling = PILImageResampling.BICUBIC			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Union[int, float] = 1 / 2_5_5			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Optional[Union[float, List[float]]] = None			,			UpperCAmelCase__			:				Optional[Union[float, List[float]]] = None			,			UpperCAmelCase__			:				bool = True			,			**UpperCAmelCase__			:				List[str]			,			)       ->	None:
						super().__init__(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__			,			param_name="crop_size"   )

						__SCREAMING_SNAKE_CASE						   =				do_resize
						__SCREAMING_SNAKE_CASE						   =				size
						__SCREAMING_SNAKE_CASE						   =				resample
						__SCREAMING_SNAKE_CASE						   =				do_center_crop
						__SCREAMING_SNAKE_CASE						   =				crop_size
						__SCREAMING_SNAKE_CASE						   =				do_rescale
						__SCREAMING_SNAKE_CASE						   =				rescale_factor
						__SCREAMING_SNAKE_CASE						   =				do_normalize
						__SCREAMING_SNAKE_CASE						   =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						__SCREAMING_SNAKE_CASE						   =				image_std if image_std is not None else OPENAI_CLIP_STD
						__SCREAMING_SNAKE_CASE						   =				do_convert_rgb




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Dict[str, int]			,			UpperCAmelCase__			:				PILImageResampling = PILImageResampling.BICUBIC			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Dict			,			)       ->	np.ndarray:
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )
						if "shortest_edge" not in size:
											raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
						__SCREAMING_SNAKE_CASE						   =				get_resize_output_image_size(UpperCAmelCase__			,			size=size["shortest_edge"]			,			default_to_square=UpperCAmelCase__   )
						return resize(UpperCAmelCase__			,			size=UpperCAmelCase__			,			resample=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Dict[str, int]			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Dict			,			)       ->	np.ndarray:
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__   )
						if "height" not in size or "width" not in size:
											raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""   )
						return center_crop(UpperCAmelCase__			,			size=(size["height"], size["width"])			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Union[int, float]			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Union[str, Any]			,			)       ->	Dict:
						return rescale(UpperCAmelCase__			,			scale=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Union[float, List[float]]			,			UpperCAmelCase__			:				Union[float, List[float]]			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				List[Any]			,			)       ->	np.ndarray:
						return normalize(UpperCAmelCase__			,			mean=UpperCAmelCase__			,			std=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				ImageInput			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				PILImageResampling = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				int = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				float = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Optional[Union[float, List[float]]] = None			,			UpperCAmelCase__			:				Optional[Union[float, List[float]]] = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Optional[Union[str, TensorType]] = None			,			UpperCAmelCase__			:				Optional[ChannelDimension] = ChannelDimension.FIRST			,			**UpperCAmelCase__			:				Union[str, Any]			,			)       ->	PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						   =				do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						   =				size if size is not None else self.size
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			param_name="size"			,			default_to_square=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						   =				do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE						   =				crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			param_name="crop_size"			,			default_to_square=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						   =				rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE						   =				do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE						   =				image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE						   =				image_std if image_std is not None else self.image_std
						__SCREAMING_SNAKE_CASE						   =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						__SCREAMING_SNAKE_CASE						   =				make_list_of_images(UpperCAmelCase__   )

						if not valid_images(UpperCAmelCase__   ):
											raise ValueError(
											    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
											    "torch.Tensor, tf.Tensor or jax.ndarray."   )

						if do_resize and size is None:
											raise ValueError("Size must be specified if do_resize is True."   )

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop is True."   )

						if do_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."   )

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError("Image mean and std must be specified if do_normalize is True."   )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
											__SCREAMING_SNAKE_CASE						   =				[convert_to_rgb(UpperCAmelCase__   ) for image in images]

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE						   =				[to_numpy_array(UpperCAmelCase__   ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE						   =				[self.resize(image=UpperCAmelCase__			,			size=UpperCAmelCase__			,			resample=UpperCAmelCase__   ) for image in images]

						if do_center_crop:
											__SCREAMING_SNAKE_CASE						   =				[self.center_crop(image=UpperCAmelCase__			,			size=UpperCAmelCase__   ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE						   =				[self.rescale(image=UpperCAmelCase__			,			scale=UpperCAmelCase__   ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE						   =				[self.normalize(image=UpperCAmelCase__			,			mean=UpperCAmelCase__			,			std=UpperCAmelCase__   ) for image in images]

						__SCREAMING_SNAKE_CASE						   =				[to_channel_dimension_format(UpperCAmelCase__			,			UpperCAmelCase__   ) for image in images]

						__SCREAMING_SNAKE_CASE						   =				{"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__			,			tensor_type=UpperCAmelCase__   )




"""simple docstring"""







from collections import defaultdict
from math import ceil, sqrt




def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000  ,						lowerCAmelCase_ = 10   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				defaultdict(lowerCAmelCase_   )

					for outer_width in range(3  ,						(t_limit // 4) + 2   ):
										if outer_width * outer_width > t_limit:
															__SCREAMING_SNAKE_CASE						   =				max(
															    ceil(sqrt(outer_width * outer_width - t_limit   )   )  ,						1   )
										else:
															__SCREAMING_SNAKE_CASE						   =				1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(lowerCAmelCase_  ,						outer_width - 1  ,						2   ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10   )


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