'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


_lowercase		:				List[Any]	       =		logging.get_logger(__name__)





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = ['''pixel_values''']







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						bool = True       ,    lowercase_       :						Dict[str, int] = None       ,    lowercase_       :						PILImageResampling = PIL.Image.BICUBIC       ,    lowercase_       :						bool = True       ,    lowercase_       :						Dict[str, int] = None       ,    lowercase_       :						Union[int, float] = 1 / 255       ,    lowercase_       :						bool = True       ,    lowercase_       :						bool = True       ,    lowercase_       :						Optional[Union[float, List[float]]] = None       ,    lowercase_       :						Optional[Union[float, List[float]]] = None       ,    **lowercase_       :						Union[str, Any]       ,    ):
										super().__init__(**lowercase_					)
										lowercase_      :    Any       		= size if size is not None else {"""height""": 256, """width""": 256}
										lowercase_      :    str       		= get_size_dict(lowercase_					)
										lowercase_      :    Optional[int]       		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_      :    Dict       		= get_size_dict(lowercase_       ,    param_name="""crop_size"""					)

										lowercase_      :    Dict       		= do_resize
										lowercase_      :    List[str]       		= size
										lowercase_      :    List[str]       		= resample
										lowercase_      :    Tuple       		= do_center_crop
										lowercase_      :    Union[str, Any]       		= crop_size
										lowercase_      :    List[str]       		= do_rescale
										lowercase_      :    Any       		= rescale_factor
										lowercase_      :    Optional[Any]       		= do_normalize
										lowercase_      :    Tuple       		= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase_      :    Optional[int]       		= image_std if image_std is not None else IMAGENET_STANDARD_STD







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.ndarray       ,    lowercase_       :						Dict[str, int]       ,    lowercase_       :						PILImageResampling = PIL.Image.BICUBIC       ,    lowercase_       :						Optional[Union[str, ChannelDimension]] = None       ,    **lowercase_       :						Optional[Any]       ,    ):
										lowercase_      :    Optional[int]       		= get_size_dict(lowercase_					)
										if "height" not in size or "width" not in size:
																	raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
										return resize(
										    lowercase_       ,    size=(size["""height"""], size["""width"""])       ,    resample=lowercase_       ,    data_format=lowercase_       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						np.ndarray       ,    lowercase_       :						Dict[str, int]       ,    lowercase_       :						Optional[Union[str, ChannelDimension]] = None       ,    **lowercase_       :						List[str]       ,    ):
										lowercase_      :    List[Any]       		= get_size_dict(lowercase_					)
										if "height" not in size or "width" not in size:
																	raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
										return center_crop(lowercase_       ,    size=(size["""height"""], size["""width"""])       ,    data_format=lowercase_       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.ndarray       ,    lowercase_       :						Union[int, float]       ,    lowercase_       :						Optional[Union[str, ChannelDimension]] = None       ,    **lowercase_       :						Any       ,    ):
										return rescale(lowercase_       ,    scale=lowercase_       ,    data_format=lowercase_       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						np.ndarray       ,    lowercase_       :						Union[float, List[float]]       ,    lowercase_       :						Union[float, List[float]]       ,    lowercase_       :						Optional[Union[str, ChannelDimension]] = None       ,    **lowercase_       :						Any       ,    ):
										return normalize(lowercase_       ,    mean=lowercase_       ,    std=lowercase_       ,    data_format=lowercase_       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						ImageInput       ,    lowercase_       :						bool = None       ,    lowercase_       :						Dict[str, int] = None       ,    lowercase_       :						str=None       ,    lowercase_       :						bool = None       ,    lowercase_       :						Dict[str, int] = None       ,    lowercase_       :						bool = None       ,    lowercase_       :						float = None       ,    lowercase_       :						bool = None       ,    lowercase_       :						Optional[Union[float, List[float]]] = None       ,    lowercase_       :						Optional[Union[float, List[float]]] = None       ,    lowercase_       :						Optional[Union[str, TensorType]] = None       ,    lowercase_       :						ChannelDimension = ChannelDimension.FIRST       ,    **lowercase_       :						List[str]       ,    ):
										lowercase_      :    str       		= do_resize if do_resize is not None else self.do_resize
										lowercase_      :    str       		= resample if resample is not None else self.resample
										lowercase_      :    Optional[int]       		= do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_      :    Optional[Any]       		= do_rescale if do_rescale is not None else self.do_rescale
										lowercase_      :    Optional[int]       		= rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_      :    int       		= do_normalize if do_normalize is not None else self.do_normalize
										lowercase_      :    Optional[Any]       		= image_mean if image_mean is not None else self.image_mean
										lowercase_      :    List[str]       		= image_std if image_std is not None else self.image_std

										lowercase_      :    Tuple       		= size if size is not None else self.size
										lowercase_      :    Dict       		= get_size_dict(lowercase_					)
										lowercase_      :    Optional[int]       		= crop_size if crop_size is not None else self.crop_size
										lowercase_      :    List[str]       		= get_size_dict(lowercase_       ,    param_name="""crop_size"""					)

										lowercase_      :    Union[str, Any]       		= make_list_of_images(lowercase_					)

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

										# All transformations expect numpy arrays.
										lowercase_      :    Optional[Any]       		= [to_numpy_array(lowercase_					) for image in images]

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

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

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

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

										lowercase_      :    Optional[Any]       		= [to_channel_dimension_format(lowercase_       ,    lowercase_					) for image in images]

										lowercase_      :    List[Any]       		= {"""pixel_values""": images}
										return BatchFeature(data=lowercase_       ,    tensor_type=lowercase_					)






'''simple docstring'''




_lowercase		:				int	       =		"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"




def 							lowerCamelCase			(   UpperCAmelCase__ :      bytes				)    ->   bytes:
							# Make sure the supplied data is a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Union[str, Any]       		= F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
														raise TypeError(UpperCAmelCase__				)

							lowercase_      :    Dict       		= """""".join(bin(UpperCAmelCase__				)[2:].zfill(8				) for byte in data				)

							lowercase_      :    Union[str, Any]       		= len(UpperCAmelCase__				) % 6 != 0

							if padding_needed:
														# The padding that will be added later
														lowercase_      :    List[Any]       		= b"""=""" * ((6 - len(UpperCAmelCase__				) % 6) // 2)

														# Append binary_stream with arbitrary binary digits (0's by default) to make its
														# length a multiple of 6.
														binary_stream += "0" * (6 - len(UpperCAmelCase__				) % 6)
							else:
														lowercase_      :    Union[str, Any]       		= b""""""

							# Encode every 6 binary digits to their corresponding Base64 character
							return (
							    "".join(
							        B64_CHARSET[int(binary_stream[index : index + 6]   ,   2				)]
							        for index in range(0   ,   len(UpperCAmelCase__				)   ,   6				)				).encode()
							    + padding
							)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   bytes:
							# Make sure encoded_data is either a string or a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				) and not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    List[str]       		= (
														    """argument should be a bytes-like object or ASCII string, """
														    F'''not \'{encoded_data.__class__.__name__}\''''
														)
														raise TypeError(UpperCAmelCase__				)

							# In case encoded_data is a bytes-like object, make sure it contains only
							# ASCII characters so we convert it to a string object
							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														try:
																					lowercase_      :    Optional[int]       		= encoded_data.decode("""utf-8"""				)
														except UnicodeDecodeError:
																					raise ValueError("""base64 encoded data should only contain ASCII characters"""				)

							lowercase_      :    Any       		= encoded_data.count("""="""				)

							# Check if the encoded string contains non base64 characters
							if padding:
														assert all(
														    char in B64_CHARSET for char in encoded_data[:-padding]				), "Invalid base64 character(s) found."
							else:
														assert all(
														    char in B64_CHARSET for char in encoded_data				), "Invalid base64 character(s) found."

							# Check the padding
							assert len(UpperCAmelCase__				) % 4 == 0 and padding < 3, "Incorrect padding"

							if padding:
														# Remove padding if there is one
														lowercase_      :    Optional[int]       		= encoded_data[:-padding]

														lowercase_      :    Any       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)[: -padding * 2]
							else:
														lowercase_      :    int       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)

							lowercase_      :    Optional[int]       		= [
							    int(binary_stream[index : index + 8]   ,   2				)
							    for index in range(0   ,   len(UpperCAmelCase__				)   ,   8				)
							]

							return bytes(UpperCAmelCase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





