"""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,
    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


a							=logging.get_logger(__name__)


if is_vision_available():
 import PIL






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
    A__				:       Tuple  							=	['''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				)
          lowerCamelCase__ =size if size is not None else {"shortest_edge": 224}
          lowerCamelCase__ =get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase				)
          lowerCamelCase__ =crop_size if crop_size is not None else {"height": 224, "width": 224}
          lowerCamelCase__ =get_size_dict(_lowerCamelCase				)

          lowerCamelCase__ =do_resize
          lowerCamelCase__ =size
          lowerCamelCase__ =resample
          lowerCamelCase__ =do_center_crop
          lowerCamelCase__ =crop_size
          lowerCamelCase__ =do_rescale
          lowerCamelCase__ =rescale_factor
          lowerCamelCase__ =do_normalize
          lowerCamelCase__ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          lowerCamelCase__ =image_std if image_std is not None else OPENAI_CLIP_STD
          lowerCamelCase__ =do_convert_rgb

    def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = PILImageResampling.BICUBIC     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
          lowerCamelCase__ =get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase				)
          lowerCamelCase__ =get_resize_output_image_size(
              _lowerCamelCase     ,			size=(size["height"], size["width"])     ,			default_to_square=_lowerCamelCase				)
          return resize(_lowerCamelCase     ,			size=_lowerCamelCase     ,			resample=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase				)

    def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
          lowerCamelCase__ =get_size_dict(_lowerCamelCase				)
          return center_crop(_lowerCamelCase     ,			size=(size["height"], size["width"])     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase				)

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

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






    def 		_a     (				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     ,			):
          lowerCamelCase__ =do_resize if do_resize is not None else self.do_resize
          lowerCamelCase__ =size if size is not None else self.size
          lowerCamelCase__ =get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase				)
          lowerCamelCase__ =resample if resample is not None else self.resample
          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(_lowerCamelCase				)
          lowerCamelCase__ =do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase__ =rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase__ =do_normalize if do_normalize is not None else self.do_normalize
          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__ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowerCamelCase__ =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:
                lowerCamelCase__ =[convert_to_rgb(_lowerCamelCase				) for image in images]

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

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

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

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

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

          lowerCamelCase__ =[to_channel_dimension_format(_lowerCamelCase     ,			_lowerCamelCase				) for image in images]

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



"""simple docstring"""


def       lowerCamelCase_			( __lowerCAmelCase    )							-> bool:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						if len(__lowerCAmelCase    ) == 1:
												return True
						try:
												lowerCamelCase__ =series[1] / series[0]
												for index in range(len(__lowerCAmelCase    ) - 1    ):
																		if series[index + 1] / series[index] != common_ratio:
																								return False
						except ZeroDivisionError:
												return False
						return True





def       lowerCamelCase_			( __lowerCAmelCase    )							-> float:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						lowerCamelCase__ =1
						for value in series:
												answer *= value
						return pow(__lowerCAmelCase       ,   1 / len(__lowerCAmelCase    )    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


