
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase			)       ->      Union[str, Any]:
     if isinstance(lowerCAmelCase__						,     (list, tuple)			) and isinstance(videos[0]						,     (list, tuple)			) and is_valid_image(videos[0][0]			):
          return videos

     elif isinstance(lowerCAmelCase__						,     (list, tuple)			) and is_valid_image(videos[0]			):
          return [videos]

     elif is_valid_image(lowerCAmelCase__			):
          return [[videos]]

     raise ValueError(F'Could not make batched video from {videos}'			)







class       _lowerCAmelCase  (		__A							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Dict = True					, SCREAMING_SNAKE_CASE__			:			Any = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			str = True					, SCREAMING_SNAKE_CASE__			:			Any = None					, SCREAMING_SNAKE_CASE__			:			Optional[Any] = True					, SCREAMING_SNAKE_CASE__			:			Dict = 1 / 2_55					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = True					, SCREAMING_SNAKE_CASE__			:			Tuple = True					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Any] = None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(**__A   )
      UpperCamelCase						    =					size if size is not None else {'shortest_edge': 2_56}
      UpperCamelCase						    =					get_size_dict(__A					, default_to_square=__A   )
      UpperCamelCase						    =					crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      UpperCamelCase						    =					get_size_dict(__A					, param_name='crop_size'   )

      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					do_center_crop
      UpperCamelCase						    =					crop_size
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_rescale
      UpperCamelCase						    =					rescale_factor
      UpperCamelCase						    =					offset
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase						    =					image_std if image_std is not None else IMAGENET_STANDARD_STD

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			int = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			int = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(__A					, default_to_square=__A   )
      if "shortest_edge" in size:
           UpperCamelCase						    =					get_resize_output_image_size(__A					, size['shortest_edge']					, default_to_square=__A   )
      elif "height" in size and "width" in size:
           UpperCamelCase						    =					(size['height'], size['width'])
      else:
           raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'   )
      return resize(__A					, size=__A					, resample=__A					, data_format=__A					, **__A   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[Any] = None					, **SCREAMING_SNAKE_CASE__			:			List[Any]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(__A   )
      if "height" not in size or "width" not in size:
           raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'   )
      return center_crop(__A					, size=(size['height'], size['width'])					, data_format=__A					, **__A   )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Tuple = True					, SCREAMING_SNAKE_CASE__			:			List[Any] = None					, **SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					image.astype(np.floataa   )
      if offset:
           UpperCamelCase						    =					image - (scale / 2)
      return rescale(__A					, scale=__A					, data_format=__A					, **__A   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Dict = None					, **SCREAMING_SNAKE_CASE__			:			Dict					, ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			str = None					, SCREAMING_SNAKE_CASE__			:			Dict = None					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Any] = None					, SCREAMING_SNAKE_CASE__			:			List[Any] = None					, SCREAMING_SNAKE_CASE__			:			List[str] = None					, SCREAMING_SNAKE_CASE__			:			str = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			Tuple = None					, SCREAMING_SNAKE_CASE__			:			Tuple = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = ChannelDimension.FIRST					, ):






      """simple docstring"""



      if do_resize and size is None or resample is None:
           raise ValueError('Size and resample 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.'   )

      if offset and not do_rescale:
           raise ValueError('For offset, do_rescale must also be set to True.'   )

      # All transformations expect numpy arrays.
      UpperCamelCase						    =					to_numpy_array(__A   )

      if do_resize:
           UpperCamelCase						    =					self.resize(image=__A					, size=__A					, resample=__A   )

      if do_center_crop:
           UpperCamelCase						    =					self.center_crop(__A					, size=__A   )

      if do_rescale:
           UpperCamelCase						    =					self.rescale(image=__A					, scale=__A					, offset=__A   )

      if do_normalize:
           UpperCamelCase						    =					self.normalize(image=__A					, mean=__A					, std=__A   )

      UpperCamelCase						    =					to_channel_dimension_format(__A					, __A   )
      return image






 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[str] = None					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None					, SCREAMING_SNAKE_CASE__			:			str = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Any] = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			str = None					, SCREAMING_SNAKE_CASE__			:			List[str] = None					, SCREAMING_SNAKE_CASE__			:			Dict = None					, SCREAMING_SNAKE_CASE__			:			Any = None					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Any] = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



      UpperCamelCase						    =					do_resize if do_resize is not None else self.do_resize
      UpperCamelCase						    =					resample if resample is not None else self.resample
      UpperCamelCase						    =					do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase						    =					do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase						    =					rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase						    =					offset if offset is not None else self.offset
      UpperCamelCase						    =					do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase						    =					image_mean if image_mean is not None else self.image_mean
      UpperCamelCase						    =					image_std if image_std is not None else self.image_std

      UpperCamelCase						    =					size if size is not None else self.size
      UpperCamelCase						    =					get_size_dict(__A					, default_to_square=__A   )
      UpperCamelCase						    =					crop_size if crop_size is not None else self.crop_size
      UpperCamelCase						    =					get_size_dict(__A					, param_name='crop_size'   )

      if not valid_images(__A   ):
           raise ValueError(
               'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
               'torch.Tensor, tf.Tensor or jax.ndarray.'   )

      UpperCamelCase						    =					make_batched(__A   )

      UpperCamelCase						    =					[
          [
              self._preprocess_image(
                  image=__A					, do_resize=__A					, size=__A					, resample=__A					, do_center_crop=__A					, crop_size=__A					, do_rescale=__A					, rescale_factor=__A					, offset=__A					, do_normalize=__A					, image_mean=__A					, image_std=__A					, data_format=__A					, )
              for img in video
          ]
          for video in videos
      ]

      UpperCamelCase						    =					{'pixel_values': videos}
      return BatchFeature(data=__A					, tensor_type=__A   )






from collections.abc import Callable




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  a
   __a					=  b
   if function(lowerCAmelCase__   ) == 0:  # one of the a or b is a root for the function
      return a
   elif function(lowerCAmelCase__   ) == 0:
      return b
   elif (
       function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) > 0
   ):  # if none of these are root and they are both positive or negative,
      # then this algorithm can't find the root
      raise ValueError("""could not find root in given interval."""   )
   else:
      __a					=  start + (end - start) / 2.0
      while abs(start - mid   ) > 10**-7:  # until precisely equals to 10^-7
         if function(lowerCAmelCase__   ) == 0:
            return mid
         elif function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) < 0:
            __a					=  mid
         else:
            __a					=  mid
         __a					=  start + (end - start) / 2.0
      return mid




def  a   (lowerCAmelCase__   ):
   return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()



