
'''simple docstring'''
from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase												=	logging.get_logger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







       '''simple docstring'''

       lowerCAmelCase_ 						=						["pixel_values"]

       def __init__(self  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  UpperCAmelCase = PILImageResampling.BICUBIC  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  UpperCAmelCase = True  ,  UpperCAmelCase = 1 / 255  ,  UpperCAmelCase = True  ,  UpperCAmelCase = IMAGENET_DEFAULT_MEAN  ,  UpperCAmelCase = IMAGENET_DEFAULT_STD  ,  **UpperCAmelCase  ,  )					->       None:
              super().__init__(**lowerCamelCase_  )
              _snake_case       =  size if size is not None else {"""shortest_edge""": 224}
              _snake_case       =  get_size_dict(lowerCamelCase_  ,  default_to_square=lowerCamelCase_  )
              _snake_case       =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
              _snake_case       =  get_size_dict(lowerCamelCase_  ,  param_name="""crop_size"""  )

              _snake_case       =  do_resize
              _snake_case       =  size
              _snake_case       =  resample
              _snake_case       =  do_center_crop
              _snake_case       =  crop_size
              _snake_case       =  do_rescale
              _snake_case       =  rescale_factor
              _snake_case       =  do_normalize
              _snake_case       =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
              _snake_case       =  image_std if image_std is not None else IMAGENET_DEFAULT_STD

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = PILImageResampling.BICUBIC  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       np.ndarray:
              _snake_case       =  get_size_dict(lowerCamelCase_  ,  default_to_square=lowerCamelCase_  )
              # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
              if "shortest_edge" in size:
                     _snake_case       =  int((256 / 224) * size["""shortest_edge"""]  )
                     _snake_case       =  get_resize_output_image_size(lowerCamelCase_  ,  size=lowerCamelCase_  ,  default_to_square=lowerCamelCase_  )
                     _snake_case       =  {"""height""": output_size[0], """width""": output_size[1]}
              if "height" not in size_dict or "width" not in size_dict:
                     raise ValueError(
                         f"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"""  )
              return resize(
                  lowerCamelCase_  ,  size=(size_dict["""height"""], size_dict["""width"""])  ,  resample=lowerCamelCase_  ,  data_format=lowerCamelCase_  ,  **lowerCamelCase_  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       np.ndarray:
              _snake_case       =  get_size_dict(lowerCamelCase_  )
              if "height" not in size or "width" not in size:
                     raise ValueError(f"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"""  )
              return center_crop(lowerCamelCase_  ,  size=(size["""height"""], size["""width"""])  ,  data_format=lowerCamelCase_  ,  **lowerCamelCase_  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       np.ndarray:
              return rescale(lowerCamelCase_  ,  scale=lowerCamelCase_  ,  data_format=lowerCamelCase_  ,  **lowerCamelCase_  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       np.ndarray:
              return normalize(lowerCamelCase_  ,  mean=lowerCamelCase_  ,  std=lowerCamelCase_  ,  data_format=lowerCamelCase_  ,  **lowerCamelCase_  )





       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = ChannelDimension.FIRST  ,  **UpperCAmelCase  ,  )					->       BatchFeature:
              _snake_case       =  do_resize if do_resize is not None else self.do_resize
              _snake_case       =  resample if resample is not None else self.resample
              _snake_case       =  do_center_crop if do_center_crop is not None else self.do_center_crop
              _snake_case       =  do_rescale if do_rescale is not None else self.do_rescale
              _snake_case       =  rescale_factor if rescale_factor is not None else self.rescale_factor
              _snake_case       =  do_normalize if do_normalize is not None else self.do_normalize
              _snake_case       =  image_mean if image_mean is not None else self.image_mean
              _snake_case       =  image_std if image_std is not None else self.image_std

              _snake_case       =  size if size is not None else self.size
              _snake_case       =  get_size_dict(lowerCamelCase_  ,  default_to_square=lowerCamelCase_  )
              _snake_case       =  crop_size if crop_size is not None else self.crop_size
              _snake_case       =  get_size_dict(lowerCamelCase_  ,  param_name="""crop_size"""  )

              _snake_case       =  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       =  [to_numpy_array(lowerCamelCase_  ) for image in images]

              if do_resize:
                     _snake_case       =  [self.resize(lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_  ) for image in images]

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

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

              if do_normalize:
                     _snake_case       =  [self.normalize(lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_  ) for image in images]

              _snake_case       =  [to_channel_dimension_format(lowerCamelCase_  ,  lowerCamelCase_  ) for image in images]

              _snake_case       =  {"""pixel_values""": images}
              return BatchFeature(data=lowerCamelCase_  ,  tensor_type=lowerCamelCase_  )

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

import typing
from collections import Counter






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  Counter()
							for base in range(1							, max_perimeter + 1				):
														for perpendicular in range(_SCREAMING_SNAKE_CASE							, max_perimeter + 1				):
																					_snake_case       =  (base * base + perpendicular * perpendicular) ** 0.5
																					if hypotenuse == int(_SCREAMING_SNAKE_CASE				):
																												_snake_case       =  int(base + perpendicular + hypotenuse				)
																												if perimeter > max_perimeter:
																																			continue
																												triplets[perimeter] += 1
							return triplets






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE = 1000				):
							_snake_case       =  pythagorean_triple(_SCREAMING_SNAKE_CASE				)
							return triplets.most_common(1				)[0][0]


if __name__ == "__main__":
				print(f'''Perimeter {solution()} has maximum solutions''')