






'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       int			)       ->      int:



 """simple docstring"""




 if isinstance(__lowerCAmelCase					,      (list, tuple)			) and isinstance(videos[0]					,      (list, tuple)			) and is_valid_image(videos[0][0]			):
  return videos

 elif isinstance(__lowerCAmelCase					,      (list, tuple)			) and is_valid_image(videos[0]			):
  return [videos]

 elif is_valid_image(__lowerCAmelCase			):
  return [[videos]]

 raise ValueError(F'Could not make batched video from {videos}'			)





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






      def __init__( self			:		str     ,      UpperCAmelCase			:		str = True     ,      UpperCAmelCase			:		List[str] = None     ,      UpperCAmelCase			:		Optional[int] = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		List[Any] = True     ,      UpperCAmelCase			:		int = None     ,      UpperCAmelCase			:		Optional[int] = True     ,      UpperCAmelCase			:		Optional[int] = 1 / 255     ,      UpperCAmelCase			:		List[Any] = True     ,      UpperCAmelCase			:		Optional[int] = True     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		str = None     ,      **UpperCAmelCase			:		int     ,      )  ->  Dict:


       '''simple docstring'''

       super().__init__(**_lowerCAmelCase		)
       lowercase					:   Optional[Any]				  =size if size is not None else {'''shortest_edge''': 256}
       lowercase					:   List[Any]				  =get_size_dict(_lowerCAmelCase     ,      default_to_square=_lowerCAmelCase		)
       lowercase					:   List[Any]				  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase					:   List[Any]				  =get_size_dict(_lowerCAmelCase     ,      param_name='''crop_size'''		)

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		List[str] = None     ,      **UpperCAmelCase			:		List[str]     ,      )  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Any				  =get_size_dict(_lowerCAmelCase     ,      default_to_square=_lowerCAmelCase		)
       if "shortest_edge" in size:
        lowercase					:   Dict				  =get_resize_output_image_size(_lowerCAmelCase     ,      size['''shortest_edge''']     ,      default_to_square=_lowerCAmelCase		)
       elif "height" in size and "width" in size:
        lowercase					:   List[Any]				  =(size['''height'''], size['''width'''])
       else:
        raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
       return resize(_lowerCAmelCase     ,      size=_lowerCAmelCase     ,      resample=_lowerCAmelCase     ,      data_format=_lowerCAmelCase     ,      **_lowerCAmelCase		)






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str] = None     ,      **UpperCAmelCase			:		str     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  =get_size_dict(_lowerCAmelCase		)
       if "height" not in size or "width" not in size:
        raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
       return center_crop(_lowerCAmelCase     ,      size=(size['''height'''], size['''width'''])     ,      data_format=_lowerCAmelCase     ,      **_lowerCAmelCase		)






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Optional[int] = True     ,      UpperCAmelCase			:		int = None     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  =image.astype(np.floataa		)
       if offset:
        lowercase					:   Optional[int]				  =image - (scale / 2)
       return rescale(_lowerCAmelCase     ,      scale=_lowerCAmelCase     ,      data_format=_lowerCAmelCase     ,      **_lowerCAmelCase		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict = None     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  Any:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		str = None     ,      UpperCAmelCase			:		List[str] = None     ,      UpperCAmelCase			:		int = None     ,      UpperCAmelCase			:		str = None     ,      UpperCAmelCase			:		Union[str, Any] = None     ,      UpperCAmelCase			:		Optional[Any] = None     ,      UpperCAmelCase			:		str = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		List[Any] = None     ,      UpperCAmelCase			:		Tuple = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Dict = ChannelDimension.FIRST     ,      )  ->  List[str]:


       '''simple docstring'''

       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.'''		)

       if offset and not do_rescale:
        raise ValueError('''For offset, do_rescale must also be set to True.'''		)

       # All transformations expect numpy arrays.
       lowercase					:   int				  =to_numpy_array(_lowerCAmelCase		)

       if do_resize:
        lowercase					:   Union[str, Any]				  =self.resize(image=_lowerCAmelCase     ,      size=_lowerCAmelCase     ,      resample=_lowerCAmelCase		)

       if do_center_crop:
        lowercase					:   List[str]				  =self.center_crop(_lowerCAmelCase     ,      size=_lowerCAmelCase		)

       if do_rescale:
        lowercase					:   str				  =self.rescale(image=_lowerCAmelCase     ,      scale=_lowerCAmelCase     ,      offset=_lowerCAmelCase		)

       if do_normalize:
        lowercase					:   Any				  =self.normalize(image=_lowerCAmelCase     ,      mean=_lowerCAmelCase     ,      std=_lowerCAmelCase		)

       lowercase					:   Optional[Any]				  =to_channel_dimension_format(_lowerCAmelCase     ,      _lowerCAmelCase		)
       return image






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Dict = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Union[str, Any] = None     ,      UpperCAmelCase			:		Tuple = None     ,      UpperCAmelCase			:		Union[str, Any] = None     ,      UpperCAmelCase			:		List[str] = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Tuple = None     ,      UpperCAmelCase			:		Any = None     ,      UpperCAmelCase			:		Optional[Any] = None     ,      UpperCAmelCase			:		List[Any] = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Tuple = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Any				  =do_resize if do_resize is not None else self.do_resize
       lowercase					:   Any				  =resample if resample is not None else self.resample
       lowercase					:   int				  =do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase					:   Dict				  =do_rescale if do_rescale is not None else self.do_rescale
       lowercase					:   List[str]				  =rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase					:   Union[str, Any]				  =offset if offset is not None else self.offset
       lowercase					:   Optional[int]				  =do_normalize if do_normalize is not None else self.do_normalize
       lowercase					:   List[str]				  =image_mean if image_mean is not None else self.image_mean
       lowercase					:   List[Any]				  =image_std if image_std is not None else self.image_std

       lowercase					:   Optional[int]				  =size if size is not None else self.size
       lowercase					:   Optional[int]				  =get_size_dict(_lowerCAmelCase     ,      default_to_square=_lowerCAmelCase		)
       lowercase					:   Optional[Any]				  =crop_size if crop_size is not None else self.crop_size
       lowercase					:   List[str]				  =get_size_dict(_lowerCAmelCase     ,      param_name='''crop_size'''		)

       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.'''		)

       lowercase					:   Optional[Any]				  =make_batched(_lowerCAmelCase		)

       lowercase					:   Any				  =[
           [
               self._preprocess_image(
                   image=_lowerCAmelCase     ,      do_resize=_lowerCAmelCase     ,      size=_lowerCAmelCase     ,      resample=_lowerCAmelCase     ,      do_center_crop=_lowerCAmelCase     ,      crop_size=_lowerCAmelCase     ,      do_rescale=_lowerCAmelCase     ,      rescale_factor=_lowerCAmelCase     ,      offset=_lowerCAmelCase     ,      do_normalize=_lowerCAmelCase     ,      image_mean=_lowerCAmelCase     ,      image_std=_lowerCAmelCase     ,      data_format=_lowerCAmelCase     ,      )
               for img in video
           ]
           for video in videos
       ]

       lowercase					:   Optional[int]				  ={'''pixel_values''': videos}
       return BatchFeature(data=_lowerCAmelCase     ,      tensor_type=_lowerCAmelCase		)











'''simple docstring'''



def 							lowercase_				(    __A :       int = 6_0_0_8_5_1_4_7_5_1_4_3			)       ->      int:



 """simple docstring"""




 try:
  lowercase					:   Any				  =int(__A			)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''			)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''			)
 lowercase					:   Optional[Any]				  =1
 lowercase					:   Dict				  =2
 while i * i <= n:
  while n % i == 0:
   lowercase					:   Optional[int]				  =i
   n //= i
  i += 1
 if n > 1:
  lowercase					:   Dict				  =n
 return int(__A			)


if __name__ == "__main__":
      print(f"""{solution() = }""")



