


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_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


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







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					int			       =		["pixel_values"]







		def __init__( self		,      A = True		,      A = None		,      A = None		,      A = PILImageResampling.BILINEAR		,      A = True		,      A = 1 / 2_55		,      A = True		,      A = None		,      A = None		,      **A		,      )  -> None:







					'''simple docstring'''







					super().__init__(**A	)
					lowerCamelCase    		=  size if size is not None else {"""shortest_edge""": 3_84}
					lowerCamelCase    		=  get_size_dict(A		,      default_to_square=A	)

					lowerCamelCase    		=  do_resize
					lowerCamelCase    		=  size
					# Default value set here for backwards compatibility where the value in config is None
					lowerCamelCase    		=  crop_pct if crop_pct is not None else 2_24 / 2_56
					lowerCamelCase    		=  resample
					lowerCamelCase    		=  do_rescale
					lowerCamelCase    		=  rescale_factor
					lowerCamelCase    		=  do_normalize
					lowerCamelCase    		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowerCamelCase    		=  image_std if image_std is not None else IMAGENET_STANDARD_STD







		def 					__A   ( self		,      A		,      A		,      A		,      A = PILImageResampling.BICUBIC		,      A = None		,      **A		,      )  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  get_size_dict(A		,      default_to_square=A	)
					if "shortest_edge" not in size:
								raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
					lowerCamelCase    		=  size["""shortest_edge"""]

					if shortest_edge < 3_84:
								# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
								lowerCamelCase    		=  int(shortest_edge / crop_pct	)
								lowerCamelCase    		=  get_resize_output_image_size(A		,      size=A		,      default_to_square=A	)
								lowerCamelCase    		=  resize(image=A		,      size=A		,      resample=A		,      data_format=A		,      **A	)
								# then crop to (shortest_edge, shortest_edge)
								return center_crop(image=A		,      size=(shortest_edge, shortest_edge)		,      data_format=A		,      **A	)
					else:
								# warping (no cropping) when evaluated at 384 or larger
								return resize(
								    A		,      size=(shortest_edge, shortest_edge)		,      resample=A		,      data_format=A		,      **A	)







		def 					__A   ( self		,      A		,      A		,      A = None		,      **A		,      )  -> Union[str, Any]:







					'''simple docstring'''







					return rescale(A		,      scale=A		,      data_format=A		,      **A	)







		def 					__A   ( self		,      A		,      A		,      A		,      A = None		,      **A		,      )  -> np.ndarray:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A = None		,      A = None		,      A = None		,      A = None		,      A = None		,      A = None		,      A = None		,      A = None		,      A = None		,      A = None		,      A = ChannelDimension.FIRST		,      **A		,      )  -> PIL.Image.Image:







					'''simple docstring'''







					lowerCamelCase    		=  do_resize if do_resize is not None else self.do_resize
					lowerCamelCase    		=  crop_pct if crop_pct is not None else self.crop_pct
					lowerCamelCase    		=  resample if resample is not None else self.resample
					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    		=  size if size is not None else self.size
					lowerCamelCase    		=  get_size_dict(A		,      default_to_square=A	)

					lowerCamelCase    		=  make_list_of_images(A	)

					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."""	)

					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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
								raise ValueError("""crop_pct must be specified if size < 384."""	)

					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.
					lowerCamelCase    		=  [to_numpy_array(A	) for image in images]

					if do_resize:
								lowerCamelCase    		=  [self.resize(image=A		,      size=A		,      crop_pct=A		,      resample=A	) for image in images]

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

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

					lowerCamelCase    		=  [to_channel_dimension_format(A		,      A	) for image in images]

					lowerCamelCase    		=  {"""pixel_values""": images}
					return BatchFeature(data=A		,      tensor_type=A	)





import random
from typing import Any






def 	__lowerCamelCase						(							lowerCamelCase__		:						list      ):





			'''simple docstring'''




			for _ in range(len(lowerCamelCase__      )      ):
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase		,		lowerCamelCase    		=  data[b], data[a]
			return data


if __name__ == "__main__":
		UpperCAmelCase   :    str     =		[0, 1, 2, 3, 4, 5, 6, 7]
		UpperCAmelCase   :    Union[str, Any]     =		["python", "says", "hello", "!"]
		print("Fisher-Yates Shuffle:")
		print("List", integers, strings)
		print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

