


"""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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_lowerCAmelCase							:    List[str]					   =	logging.get_logger(__name__)







def        lowerCamelCase_(		_lowerCamelCase       )    ->     List[List[ImageInput]]:
   '''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 							A_      (       _a ):
  lowerCAmelCase__      			=	['pixel_values']


  def __init__(			self:						Dict							,__lowerCAmelCase:						bool = True							,__lowerCAmelCase:						Dict[str, int] = None							,__lowerCAmelCase:						PILImageResampling = PILImageResampling.BILINEAR							,__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:						str							,):







     '''simple docstring'''







     super().__init__(**__lowerCAmelCase    )
     _lowerCamelCase   :				List[str]							=     size if size is not None else {"shortest_edge": 224}
     _lowerCamelCase   :				str							=     get_size_dict(__lowerCAmelCase							,default_to_square=__lowerCAmelCase    )
     _lowerCamelCase   :				Dict							=     crop_size if crop_size is not None else {"height": 224, "width": 224}
     _lowerCamelCase   :				List[Any]							=     get_size_dict(__lowerCAmelCase							,param_name="crop_size"    )

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


  def 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						np.ndarray							,__lowerCAmelCase:						Dict[str, int]							,__lowerCAmelCase:						PILImageResampling = PILImageResampling.BILINEAR							,__lowerCAmelCase:						Optional[Union[str, ChannelDimension]] = None							,**__lowerCAmelCase:						int							,):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     get_size_dict(__lowerCAmelCase							,default_to_square=__lowerCAmelCase    )
     if "shortest_edge" in size:
        _lowerCamelCase   :				Dict							=     get_resize_output_image_size(__lowerCAmelCase							,size["shortest_edge"]							,default_to_square=__lowerCAmelCase    )
     elif "height" in size and "width" in size:
        _lowerCamelCase   :				Optional[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 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						np.ndarray							,__lowerCAmelCase:						Dict[str, int]							,__lowerCAmelCase:						Optional[Union[str, ChannelDimension]] = None							,**__lowerCAmelCase:						Any							,):







     '''simple docstring'''







     _lowerCamelCase   :				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 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						np.ndarray							,__lowerCAmelCase:						Union[int, float]							,__lowerCAmelCase:						Optional[Union[str, ChannelDimension]] = None							,**__lowerCAmelCase:						Optional[Any]							,):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						np.ndarray							,__lowerCAmelCase:						Union[float, List[float]]							,__lowerCAmelCase:						Union[float, List[float]]							,__lowerCAmelCase:						Optional[Union[str, ChannelDimension]] = None							,**__lowerCAmelCase:						Dict							,):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						int							,__lowerCAmelCase:						ImageInput							,__lowerCAmelCase:						bool = None							,__lowerCAmelCase:						Dict[str, int] = None							,__lowerCAmelCase:						PILImageResampling = None							,__lowerCAmelCase:						bool = None							,__lowerCAmelCase:						Dict[str, 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:						Optional[ChannelDimension] = ChannelDimension.FIRST							,):







     '''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."    )

     # All transformations expect numpy arrays.
     _lowerCamelCase   :				int							=     to_numpy_array(__lowerCAmelCase    )

     if do_resize:
        _lowerCamelCase   :				Dict							=     self.resize(image=__lowerCAmelCase							,size=__lowerCAmelCase							,resample=__lowerCAmelCase    )

     if do_center_crop:
        _lowerCamelCase   :				Any							=     self.center_crop(__lowerCAmelCase							,size=__lowerCAmelCase    )

     if do_rescale:
        _lowerCamelCase   :				Optional[Any]							=     self.rescale(image=__lowerCAmelCase							,scale=__lowerCAmelCase    )

     if do_normalize:
        _lowerCamelCase   :				Optional[int]							=     self.normalize(image=__lowerCAmelCase							,mean=__lowerCAmelCase							,std=__lowerCAmelCase    )

     _lowerCamelCase   :				Tuple							=     to_channel_dimension_format(__lowerCAmelCase							,__lowerCAmelCase    )
     return image






  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						ImageInput							,__lowerCAmelCase:						bool = None							,__lowerCAmelCase:						Dict[str, int] = None							,__lowerCAmelCase:						PILImageResampling = None							,__lowerCAmelCase:						bool = None							,__lowerCAmelCase:						Dict[str, 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:						Optional[Union[str, TensorType]] = None							,__lowerCAmelCase:						ChannelDimension = ChannelDimension.FIRST							,**__lowerCAmelCase:						str							,):







     '''simple docstring'''







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

     _lowerCamelCase   :				Optional[Any]							=     size if size is not None else self.size
     _lowerCamelCase   :				Union[str, Any]							=     get_size_dict(__lowerCAmelCase							,default_to_square=__lowerCAmelCase    )
     _lowerCamelCase   :				int							=     crop_size if crop_size is not None else self.crop_size
     _lowerCamelCase   :				Any							=     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."    )

     _lowerCamelCase   :				List[Any]							=     make_batched(__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     [
         [
             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							,do_normalize=__lowerCAmelCase							,image_mean=__lowerCAmelCase							,image_std=__lowerCAmelCase							,data_format=__lowerCAmelCase							,)
             for img in video
         ]
         for video in videos
     ]

     _lowerCamelCase   :				Union[str, Any]							=     {"pixel_values": videos}
     return BatchFeature(data=__lowerCAmelCase							,tensor_type=__lowerCAmelCase    )






'''simple docstring'''




import random



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			num - 1
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0

		while s % 2 == 0:
				SCREAMING_SNAKE_CASE :				List[str]		=			s // 2
				t += 1

		for _ in range(5	):
				SCREAMING_SNAKE_CASE :				List[Any]		=			random.randrange(2			,			num - 1	)
				SCREAMING_SNAKE_CASE :				List[Any]		=			pow(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
				if v != 1:
						SCREAMING_SNAKE_CASE :				Tuple		=			0
						while v != (num - 1):
								if i == t - 1:
										return False
								else:
										SCREAMING_SNAKE_CASE :				Any		=			i + 1
										SCREAMING_SNAKE_CASE :				str		=			(v**2) % num
		return True



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if num < 2:
				return False

		SCREAMING_SNAKE_CASE :				List[str]		=			[
		    2,
		    3,
		    5,
		    7,
		    11,
		    13,
		    17,
		    19,
		    23,
		    29,
		    31,
		    37,
		    41,
		    43,
		    47,
		    53,
		    59,
		    61,
		    67,
		    71,
		    73,
		    79,
		    83,
		    89,
		    97,
		    1_01,
		    1_03,
		    1_07,
		    1_09,
		    1_13,
		    1_27,
		    1_31,
		    1_37,
		    1_39,
		    1_49,
		    1_51,
		    1_57,
		    1_63,
		    1_67,
		    1_73,
		    1_79,
		    1_81,
		    1_91,
		    1_93,
		    1_97,
		    1_99,
		    2_11,
		    2_23,
		    2_27,
		    2_29,
		    2_33,
		    2_39,
		    2_41,
		    2_51,
		    2_57,
		    2_63,
		    2_69,
		    2_71,
		    2_77,
		    2_81,
		    2_83,
		    2_93,
		    3_07,
		    3_11,
		    3_13,
		    3_17,
		    3_31,
		    3_37,
		    3_47,
		    3_49,
		    3_53,
		    3_59,
		    3_67,
		    3_73,
		    3_79,
		    3_83,
		    3_89,
		    3_97,
		    4_01,
		    4_09,
		    4_19,
		    4_21,
		    4_31,
		    4_33,
		    4_39,
		    4_43,
		    4_49,
		    4_57,
		    4_61,
		    4_63,
		    4_67,
		    4_79,
		    4_87,
		    4_91,
		    4_99,
		    5_03,
		    5_09,
		    5_21,
		    5_23,
		    5_41,
		    5_47,
		    5_57,
		    5_63,
		    5_69,
		    5_71,
		    5_77,
		    5_87,
		    5_93,
		    5_99,
		    6_01,
		    6_07,
		    6_13,
		    6_17,
		    6_19,
		    6_31,
		    6_41,
		    6_43,
		    6_47,
		    6_53,
		    6_59,
		    6_61,
		    6_73,
		    6_77,
		    6_83,
		    6_91,
		    7_01,
		    7_09,
		    7_19,
		    7_27,
		    7_33,
		    7_39,
		    7_43,
		    7_51,
		    7_57,
		    7_61,
		    7_69,
		    7_73,
		    7_87,
		    7_97,
		    8_09,
		    8_11,
		    8_21,
		    8_23,
		    8_27,
		    8_29,
		    8_39,
		    8_53,
		    8_57,
		    8_59,
		    8_63,
		    8_77,
		    8_81,
		    8_83,
		    8_87,
		    9_07,
		    9_11,
		    9_19,
		    9_29,
		    9_37,
		    9_41,
		    9_47,
		    9_53,
		    9_67,
		    9_71,
		    9_77,
		    9_83,
		    9_91,
		    9_97,
		]

		if num in low_primes:
				return True

		for prime in low_primes:
				if (num % prime) == 0:
						return False

		return rabin_miller(lowerCamelCase_	)



def 		__A		(  lowerCamelCase_ = 10_24	):


		"""simple docstring"""



		while True:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			random.randrange(2 ** (keysize - 1)			,			2 ** (keysize)	)
				if is_prime_low_num(lowerCamelCase_	):
						return num


if __name__ == "__main__":
							__UpperCAmelCase            =       generate_large_prime()
							print(("""Prime number:""", num))
							print(("""is_prime_low_num:""", is_prime_low_num(num)))



