



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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a__    =	logging.get_logger(__name__)







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		["""pixel_values"""]

   def __init__(					self							:			Union[str, Any]				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Optional[Dict[str, int]] = None				,		lowerCAmelCase							:			PILImageResampling = PILImageResampling.BILINEAR				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Union[int, float] = 1 / 255				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		**lowerCAmelCase							:			int				,		)				->				None:

    """simple docstring"""







    super().__init__(**lowerCAmelCase)
    _snake_case    :  Any					=					size if size is not None else {"""shortest_edge""": 256}
    _snake_case    :  List[Any]					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
    _snake_case    :  int					=					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    _snake_case    :  int					=					get_size_dict(lowerCAmelCase)
    _snake_case    :  Any					=					do_resize
    _snake_case    :  str					=					size
    _snake_case    :  Optional[Any]					=					resample
    _snake_case    :  Optional[Any]					=					do_center_crop
    _snake_case    :  Optional[Any]					=					crop_size
    _snake_case    :  Optional[int]					=					do_rescale
    _snake_case    :  Tuple					=					rescale_factor
    _snake_case    :  Optional[Any]					=					do_normalize
    _snake_case    :  Optional[Any]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _snake_case    :  Optional[Any]					=					image_std if image_std is not None else IMAGENET_STANDARD_STD

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Dict[str, int]				,		lowerCAmelCase							:			PILImageResampling = PILImageResampling.BICUBIC				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			Optional[Any]				,		)				->				np.ndarray:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
    if "shortest_edge" not in size:
     raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
    _snake_case    :  Optional[int]					=					get_resize_output_image_size(lowerCAmelCase				,		size=size["""shortest_edge"""]				,		default_to_square=lowerCAmelCase)
    return resize(lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Dict[str, int]				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			Any				,		)				->				np.ndarray:

    """simple docstring"""







    _snake_case    :  str					=					get_size_dict(lowerCAmelCase)
    return center_crop(lowerCAmelCase				,		size=(size["""height"""], size["""width"""])				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			Optional[Any])				->				np.ndarray:

    """simple docstring"""







    return rescale(lowerCAmelCase				,		scale=lowerCAmelCase				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Union[float, List[float]]				,		lowerCAmelCase							:			Union[float, List[float]]				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			str				,		)				->				np.ndarray:

    """simple docstring"""







    return normalize(lowerCAmelCase				,		mean=lowerCAmelCase				,		std=lowerCAmelCase				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)


   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			ImageInput				,		lowerCAmelCase							:			Optional[bool] = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			Optional[bool] = None				,		lowerCAmelCase							:			Optional[float] = None				,		lowerCAmelCase							:			Optional[bool] = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[str, TensorType]] = None				,		lowerCAmelCase							:			Union[str, ChannelDimension] = ChannelDimension.FIRST				,		**lowerCAmelCase							:			Any				,		)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Any					=					do_resize if do_resize is not None else self.do_resize
    _snake_case    :  str					=					size if size is not None else self.size
    _snake_case    :  Union[str, Any]					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					resample if resample is not None else self.resample
    _snake_case    :  Any					=					do_center_crop if do_center_crop is not None else self.do_center_crop
    _snake_case    :  Optional[Any]					=					crop_size if crop_size is not None else self.crop_size
    _snake_case    :  int					=					get_size_dict(lowerCAmelCase)
    _snake_case    :  Any					=					do_rescale if do_rescale is not None else self.do_rescale
    _snake_case    :  Optional[int]					=					rescale_factor if rescale_factor is not None else self.rescale_factor
    _snake_case    :  Optional[Any]					=					do_normalize if do_normalize is not None else self.do_normalize
    _snake_case    :  str					=					image_mean if image_mean is not None else self.image_mean
    _snake_case    :  Union[str, Any]					=					image_std if image_std is not None else self.image_std

    _snake_case    :  List[Any]					=					make_list_of_images(lowerCAmelCase)

    if not valid_images(lowerCAmelCase):
     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.""")

    # All transformations expect numpy arrays.
    _snake_case    :  Optional[Any]					=					[to_numpy_array(lowerCAmelCase) for image in images]

    if do_resize:
     _snake_case    :  str					=					[self.resize(image=lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase) for image in images]

    if do_center_crop:
     _snake_case    :  int					=					[self.center_crop(image=lowerCAmelCase				,		size=lowerCAmelCase) for image in images]

    if do_rescale:
     _snake_case    :  Dict					=					[self.rescale(image=lowerCAmelCase				,		scale=lowerCAmelCase) for image in images]

    if do_normalize:
     _snake_case    :  Tuple					=					[self.normalize(image=lowerCAmelCase				,		mean=lowerCAmelCase				,		std=lowerCAmelCase) for image in images]

    _snake_case    :  Union[str, Any]					=					[to_channel_dimension_format(lowerCAmelCase				,		lowerCAmelCase) for image in images]

    _snake_case    :  List[Any]					=					{"""pixel_values""": images}
    return BatchFeature(data=lowerCAmelCase				,		tensor_type=lowerCAmelCase)





from math import ceil, sqrt



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000_000				)  ->     int:
 _snake_case    :  Tuple					=					0

 for outer_width in range(3				,       (limit // 4) + 2				):
  if outer_width**2 > limit:
   _snake_case    :  int					=					max(ceil(sqrt(outer_width**2 - limit				)				)				,       1				)
  else:
   _snake_case    :  Any					=					1
  if (outer_width - hole_width_lower_bound) % 2:
   hole_width_lower_bound += 1

  answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

 return answer


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