

'''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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:			str       =	logging.get_logger(__name__)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



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


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = size if size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE   ,				default_to_square=__SCREAMING_SNAKE_CASE   ,				param_name="""crop_size"""     )

      __SCREAMING_SNAKE_CASE      = do_resize
      __SCREAMING_SNAKE_CASE      = do_rescale
      __SCREAMING_SNAKE_CASE      = do_normalize
      __SCREAMING_SNAKE_CASE      = do_center_crop
      __SCREAMING_SNAKE_CASE      = crop_size
      __SCREAMING_SNAKE_CASE      = size
      __SCREAMING_SNAKE_CASE      = resample
      __SCREAMING_SNAKE_CASE      = rescale_factor
      __SCREAMING_SNAKE_CASE      = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      __SCREAMING_SNAKE_CASE      = image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int]   ,				__SCREAMING_SNAKE_CASE   :			PILImageResampling = PILImageResampling.BILINEAR   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			str   ,				)    ->					np.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE     )

      if "shortest_edge" in size:
           __SCREAMING_SNAKE_CASE      = get_resize_output_image_size(__SCREAMING_SNAKE_CASE   ,				size=size["""shortest_edge"""]   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
           __SCREAMING_SNAKE_CASE      = (size["""height"""], size["""width"""])
      else:
           raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'     )
      return resize(__SCREAMING_SNAKE_CASE   ,				size=__SCREAMING_SNAKE_CASE   ,				resample=__SCREAMING_SNAKE_CASE   ,				data_format=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple   ,				)    ->					np.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE     )
      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(__SCREAMING_SNAKE_CASE   ,				size=(size["""height"""], size["""width"""])   ,				data_format=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			float   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					np.ndarray:


      """simple docstring"""




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


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			ImageInput   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int] = None   ,				__SCREAMING_SNAKE_CASE   :			PILImageResampling = None   ,				__SCREAMING_SNAKE_CASE   :			bool = None   ,				__SCREAMING_SNAKE_CASE   :			int = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[float] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[float, List[float]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[float, List[float]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[str, ChannelDimension] = ChannelDimension.FIRST   ,				**__SCREAMING_SNAKE_CASE   :			Tuple   ,				)    ->					BatchFeature:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE      = do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE      = do_normalize if do_normalize is not None else self.do_normalize
      __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(__SCREAMING_SNAKE_CASE   ,				param_name="""crop_size"""   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE      = rescale_factor if rescale_factor is not None else self.rescale_factor
      __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      = size if size is not None else self.size
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE     )

      if not is_batched(__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = [images]

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

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

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

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

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

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

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

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









'''simple docstring'''



import math







def   a__			(    a__		):




     """simple docstring"""


     return math.sqrt(a__		) * math.sqrt(a__		) == num







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = n
     while left <= right:
          __SCREAMING_SNAKE_CASE      = (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               __SCREAMING_SNAKE_CASE      = mid - 1
          else:
               __SCREAMING_SNAKE_CASE      = mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()






