


'''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


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





class   snake_case					(					__lowerCamelCase    ):





  """simple docstring"""




  _lowerCamelCase						=						["pixel_values"]






  def __init__(					self    ,						UpperCamelCase = True    ,						UpperCamelCase = None    ,						UpperCamelCase = PILImageResampling.BICUBIC    ,						UpperCamelCase = True    ,						UpperCamelCase = True    ,						UpperCamelCase = 1 / 255    ,						UpperCamelCase = None    ,						UpperCamelCase = True    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						**UpperCamelCase    ,						):







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

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






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = PILImageResampling.BILINEAR    ,						UpperCamelCase = None    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						get_size_dict(SCREAMING_SNAKE_CASE_  )

   if "shortest_edge" in size:
    lowerCamelCase_  				=						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:
    lowerCamelCase_  				=						(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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						**UpperCamelCase  ):







   """simple docstring"""
   return rescale(SCREAMING_SNAKE_CASE_    ,						scale=SCREAMING_SNAKE_CASE_    ,						data_format=SCREAMING_SNAKE_CASE_    ,						**SCREAMING_SNAKE_CASE_  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   return normalize(SCREAMING_SNAKE_CASE_    ,						mean=SCREAMING_SNAKE_CASE_    ,						std=SCREAMING_SNAKE_CASE_    ,						data_format=SCREAMING_SNAKE_CASE_    ,						**SCREAMING_SNAKE_CASE_  )







  def 						snake_case	(					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    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						do_resize if do_resize is not None else self.do_resize
   lowerCamelCase_  				=						do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase_  				=						do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase_  				=						do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase_  				=						crop_size if crop_size is not None else self.crop_size
   lowerCamelCase_  				=						get_size_dict(SCREAMING_SNAKE_CASE_    ,						param_name="crop_size"    ,						default_to_square=SCREAMING_SNAKE_CASE_  )
   lowerCamelCase_  				=						resample if resample is not None else self.resample
   lowerCamelCase_  				=						rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase_  				=						image_mean if image_mean is not None else self.image_mean
   lowerCamelCase_  				=						image_std if image_std is not None else self.image_std

   lowerCamelCase_  				=						size if size is not None else self.size
   lowerCamelCase_  				=						get_size_dict(SCREAMING_SNAKE_CASE_  )

   if not is_batched(SCREAMING_SNAKE_CASE_  ):
    lowerCamelCase_  				=						[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.
   lowerCamelCase_  				=						[to_numpy_array(SCREAMING_SNAKE_CASE_  ) for image in images]

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

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

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

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

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

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







'''simple docstring'''

import math





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 return math.pow(UpperCAmelCase_      ,       2	) - a





def __snake_case      (   UpperCAmelCase_		:		float	):
 return 2 * x





def __snake_case      (   UpperCAmelCase_		:		float	):
 lowerCamelCase_  				=						2.0

 while start <= a:
  lowerCamelCase_  				=						math.pow(UpperCAmelCase_      ,       2	)

 return start





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		int = 9999      ,       UpperCAmelCase_		:		float = 0.00_0000_0000_0001	):

 if a < 0:
  raise ValueError("math domain error"	)

 lowerCamelCase_  				=						get_initial_point(UpperCAmelCase_	)

 for _ in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						value
  lowerCamelCase_  				=						value - fx(UpperCAmelCase_      ,       UpperCAmelCase_	) / fx_derivative(UpperCAmelCase_	)
  if abs(prev_value - value	) < tolerance:
   return value

 return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()



