


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


snake_case    :   Optional[Any]	    =					logging.get_logger(__name__)


if is_vision_available():
     import PIL







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	['pixel_values']




 def __init__(		self ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = PILImageResampling.BICUBIC ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = True ,  _lowerCamelCase = 1 / 255 ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = True ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )
       a						:str										=	size if size is not None else {'''shortest_edge''': 224}
       a						:int										=	get_size_dict(_lowerCamelCase ,  default_to_square=_lowerCamelCase   )
       a						:Optional[int]										=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       a						:Union[str, Any]										=	get_size_dict(_lowerCamelCase ,  default_to_square=_lowerCamelCase ,  param_name='''crop_size'''   )

       a						:Dict										=	do_resize
       a						:Optional[int]										=	size
       a						:Optional[int]										=	resample
       a						:Dict										=	do_center_crop
       a						:List[str]										=	crop_size
       a						:Any										=	do_rescale
       a						:Optional[int]										=	rescale_factor
       a						:str										=	do_normalize
       a						:Tuple										=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       a						:Any										=	image_std if image_std is not None else OPENAI_CLIP_STD
       a						:List[Any]										=	do_convert_rgb




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = PILImageResampling.BICUBIC ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       a						:Union[str, 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()}'''   )
       a						:List[str]										=	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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       a						:Union[str, Any]										=	get_size_dict(_lowerCamelCase   )
       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(_lowerCamelCase ,  size=(size['''height'''], size['''width''']) ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       return rescale(_lowerCamelCase ,  scale=_lowerCamelCase ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       return normalize(_lowerCamelCase ,  mean=_lowerCamelCase ,  std=_lowerCamelCase ,  data_format=_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = ChannelDimension.FIRST ,  **_lowerCamelCase ,  ):
       a						:List[Any]										=	do_resize if do_resize is not None else self.do_resize
       a						:Any										=	size if size is not None else self.size
       a						:Any										=	get_size_dict(_lowerCamelCase ,  param_name='''size''' ,  default_to_square=_lowerCamelCase   )
       a						:str										=	resample if resample is not None else self.resample
       a						:int										=	do_center_crop if do_center_crop is not None else self.do_center_crop
       a						:Dict										=	crop_size if crop_size is not None else self.crop_size
       a						:int										=	get_size_dict(_lowerCamelCase ,  param_name='''crop_size''' ,  default_to_square=_lowerCamelCase   )
       a						:Optional[Any]										=	do_rescale if do_rescale is not None else self.do_rescale
       a						:List[str]										=	rescale_factor if rescale_factor is not None else self.rescale_factor
       a						:Optional[Any]										=	do_normalize if do_normalize is not None else self.do_normalize
       a						:List[str]										=	image_mean if image_mean is not None else self.image_mean
       a						:Dict										=	image_std if image_std is not None else self.image_std
       a						:List[Any]										=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       a						:Optional[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.'''   )

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

       # All transformations expect numpy arrays.
       a						:Union[str, Any]										=	[to_numpy_array(_lowerCamelCase   ) for image in images]

       if do_resize:
             a						:Optional[int]										=	[self.resize(image=_lowerCamelCase ,  size=_lowerCamelCase ,  resample=_lowerCamelCase   ) for image in images]

       if do_center_crop:
             a						:List[Any]										=	[self.center_crop(image=_lowerCamelCase ,  size=_lowerCamelCase   ) for image in images]

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

       if do_normalize:
             a						:Union[str, Any]										=	[self.normalize(image=_lowerCamelCase ,  mean=_lowerCamelCase ,  std=_lowerCamelCase   ) for image in images]

       a						:Dict										=	[to_channel_dimension_format(_lowerCamelCase ,  _lowerCamelCase   ) for image in images]

       a						:List[str]										=	{'''pixel_values''': images}
       return BatchFeature(data=_lowerCamelCase ,  tensor_type=_lowerCamelCase   )









import math
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		float						):




      """simple docstring"""





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




      """simple docstring"""





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




      """simple docstring"""





      a						:int										=	2.0

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

      return start
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		int = 9999			,  UpperCAmelCase_						:		float = 0.00000000000001						):




      """simple docstring"""





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

      a						:List[Any]										=	get_initial_point(UpperCAmelCase_						)

      for _ in range(UpperCAmelCase_						):
            a						:Optional[int]										=	value
            a						:int										=	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()





