





'''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


lowercase__		:    Any =       logging.get_logger(__name__)


if is_vision_available():
   import PIL
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Optional[Any]		       =					['pixel_values']





       def __init__(							self     :	Optional[Any] ,				lowerCAmelCase__     :	bool = True ,				lowerCAmelCase__     :	Dict[str, int] = None ,				lowerCAmelCase__     :	PILImageResampling = PILImageResampling.BICUBIC ,				lowerCAmelCase__     :	bool = True ,				lowerCAmelCase__     :	Dict[str, int] = None ,				lowerCAmelCase__     :	bool = True ,				lowerCAmelCase__     :	Union[int, float] = 1 / 255 ,				lowerCAmelCase__     :	bool = True ,				lowerCAmelCase__     :	Optional[Union[float, List[float]]] = None ,				lowerCAmelCase__     :	Optional[Union[float, List[float]]] = None ,				lowerCAmelCase__     :	bool = True ,				**lowerCAmelCase__     :	List[str] ,				)					->  None:

            '''simple docstring'''




            super().__init__(**lowerCAmelCase__  )
            _UpperCamelCase       					= size if size is not None else {'''shortest_edge''': 224}
            _UpperCamelCase       					= get_size_dict(lowerCAmelCase__ ,				default_to_square=lowerCAmelCase__  )
            _UpperCamelCase       					= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
            _UpperCamelCase       					= get_size_dict(lowerCAmelCase__  )

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





       def snake_case__		(							self     :	Optional[int] ,				lowerCAmelCase__     :	np.ndarray ,				lowerCAmelCase__     :	Dict[str, int] ,				lowerCAmelCase__     :	PILImageResampling = PILImageResampling.BICUBIC ,				lowerCAmelCase__     :	Optional[Union[str, ChannelDimension]] = None ,				**lowerCAmelCase__     :	Union[str, Any] ,				)					->  np.ndarray:

            '''simple docstring'''




            _UpperCamelCase       					= get_size_dict(lowerCAmelCase__ ,				default_to_square=lowerCAmelCase__  )
            _UpperCamelCase       					= 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 snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	np.ndarray ,				lowerCAmelCase__     :	Dict[str, int] ,				lowerCAmelCase__     :	Optional[Union[str, ChannelDimension]] = None ,				**lowerCAmelCase__     :	Optional[Any] ,				)					->  np.ndarray:

            '''simple docstring'''




            _UpperCamelCase       					= get_size_dict(lowerCAmelCase__  )
            return center_crop(lowerCAmelCase__ ,				size=(size['''height'''], size['''width''']) ,				data_format=lowerCAmelCase__ ,				**lowerCAmelCase__  )





       def snake_case__		(							self     :	Dict ,				lowerCAmelCase__     :	np.ndarray ,				lowerCAmelCase__     :	Union[int, float] ,				lowerCAmelCase__     :	Optional[Union[str, ChannelDimension]] = None ,				**lowerCAmelCase__     :	List[str] ,				)					->  Any:

            '''simple docstring'''




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





       def snake_case__		(							self     :	Optional[Any] ,				lowerCAmelCase__     :	np.ndarray ,				lowerCAmelCase__     :	Union[float, List[float]] ,				lowerCAmelCase__     :	Union[float, List[float]] ,				lowerCAmelCase__     :	Optional[Union[str, ChannelDimension]] = None ,				**lowerCAmelCase__     :	List[str] ,				)					->  np.ndarray:

            '''simple docstring'''




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







       def snake_case__		(							self     :	str ,				lowerCAmelCase__     :	ImageInput ,				lowerCAmelCase__     :	bool = None ,				lowerCAmelCase__     :	Dict[str, int] = None ,				lowerCAmelCase__     :	PILImageResampling = None ,				lowerCAmelCase__     :	bool = None ,				lowerCAmelCase__     :	int = None ,				lowerCAmelCase__     :	bool = None ,				lowerCAmelCase__     :	float = None ,				lowerCAmelCase__     :	bool = None ,				lowerCAmelCase__     :	Optional[Union[float, List[float]]] = None ,				lowerCAmelCase__     :	Optional[Union[float, List[float]]] = None ,				lowerCAmelCase__     :	bool = None ,				lowerCAmelCase__     :	Optional[Union[str, TensorType]] = None ,				lowerCAmelCase__     :	Optional[ChannelDimension] = ChannelDimension.FIRST ,				**lowerCAmelCase__     :	Optional[Any] ,				)					->  PIL.Image.Image:

            '''simple docstring'''




            _UpperCamelCase       					= do_resize if do_resize is not None else self.do_resize
            _UpperCamelCase       					= size if size is not None else self.size
            _UpperCamelCase       					= get_size_dict(lowerCAmelCase__ ,				default_to_square=lowerCAmelCase__  )
            _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       					= crop_size if crop_size is not None else self.crop_size
            _UpperCamelCase       					= get_size_dict(lowerCAmelCase__  )
            _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       					= 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       					= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            _UpperCamelCase       					= 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:
                 _UpperCamelCase       					= [convert_to_rgb(lowerCAmelCase__  ) for image in images]

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

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

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

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

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

            _UpperCamelCase       					= [to_channel_dimension_format(lowerCAmelCase__ ,				lowerCAmelCase__  ) for image in images]

            _UpperCamelCase       					= {'''pixel_values''': images}
            return BatchFeature(data=lowerCAmelCase__ ,				tensor_type=lowerCAmelCase__  )










'''simple docstring'''

from __future__ import annotations







def       a__       (       lowercase      :  list[int | float]   )	->	int | float:





     """simple docstring"""

     if len(lowercase   ) == 0:
          raise ValueError('''find_min() arg is an empty sequence'''   )
     _UpperCamelCase       					= nums[0]
     for num in nums:
          _UpperCamelCase       					= min(lowercase,      lowercase   )
     return min_num


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)



