




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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase__			:       Tuple         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   ["pixel_values"]



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

       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      do_rescale
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      resample
       SCREAMING_SNAKE_CASE_     						=      rescale_factor
       SCREAMING_SNAKE_CASE_     						=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       SCREAMING_SNAKE_CASE_     						=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		Dict[str, int]    ,    _lowerCAmelCase  :		PILImageResampling = PILImageResampling.BILINEAR    ,    _lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None    ,    **_lowerCAmelCase  :		Optional[Any]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      get_size_dict(_lowerCAmelCase						)

       if "shortest_edge" in size:
             SCREAMING_SNAKE_CASE_     						=      get_resize_output_image_size(_lowerCAmelCase    ,    size=size['shortest_edge']    ,    default_to_square=_lowerCAmelCase						)
             # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
       elif "height" in size and "width" in size:
             SCREAMING_SNAKE_CASE_     						=      (size['height'], size['width'])
       else:
             raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"						)
       return resize(_lowerCAmelCase    ,    size=_lowerCAmelCase    ,    resample=_lowerCAmelCase    ,    data_format=_lowerCAmelCase    ,    **_lowerCAmelCase						)



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



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




 def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		ImageInput    ,    _lowerCAmelCase  :		Optional[bool] = None    ,    _lowerCAmelCase  :		Dict[str, int] = None    ,    _lowerCAmelCase  :		PILImageResampling = None    ,    _lowerCAmelCase  :		bool = None    ,    _lowerCAmelCase  :		int = None    ,    _lowerCAmelCase  :		Optional[bool] = None    ,    _lowerCAmelCase  :		Optional[float] = None    ,    _lowerCAmelCase  :		Optional[bool] = None    ,    _lowerCAmelCase  :		Optional[Union[float, List[float]]] = None    ,    _lowerCAmelCase  :		Optional[Union[float, List[float]]] = None    ,    _lowerCAmelCase  :		Optional[Union[str, TensorType]] = None    ,    _lowerCAmelCase  :		Union[str, ChannelDimension] = ChannelDimension.FIRST    ,    **_lowerCAmelCase  :		str    ,    ):
       SCREAMING_SNAKE_CASE_     						=      do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE_     						=      do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE_     						=      do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE_     						=      do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE_     						=      get_size_dict(_lowerCAmelCase    ,    param_name='crop_size'    ,    default_to_square=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE_     						=      rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE_     						=      image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE_     						=      size if size is not None else self.size
       SCREAMING_SNAKE_CASE_     						=      get_size_dict(_lowerCAmelCase						)

       if not is_batched(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_     						=      [images]

       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.'						)

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

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

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

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

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

       SCREAMING_SNAKE_CASE_     						=      [to_channel_dimension_format(_lowerCAmelCase    ,    _lowerCAmelCase						) for image in images]

       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': images}
       return BatchFeature(data=_lowerCAmelCase    ,    tensor_type=_lowerCAmelCase						)





import random
from typing import Any

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list							) ->			list[Any]:
      for _ in range(len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      data[b], data[a]
      return data


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