





"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCamelCase_          =	logging.get_logger(__name__)


if is_vision_available():
		import PIL






class       _SCREAMING_SNAKE_CASE(     SCREAMING_SNAKE_CASE__						):
							SCREAMING_SNAKE_CASE_     :   List[Any]					=						['''pixel_values''']




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





													"""simple docstring"""






													super().__init__(**SCREAMING_SNAKE_CASE__							)

													__SCREAMING_SNAKE_CASE     :int	       =						size if size is not None else {'''height''': 25_60, '''width''': 19_20}
													if isinstance(SCREAMING_SNAKE_CASE__	,(tuple, list)							):
																			# The previous feature extractor size parameter was in (width, height) format
																			__SCREAMING_SNAKE_CASE     :int	       =						size[::-1]
													__SCREAMING_SNAKE_CASE     :Tuple	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)

													__SCREAMING_SNAKE_CASE     :List[str]	       =						do_resize
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						size
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						resample
													__SCREAMING_SNAKE_CASE     :Any	       =						do_thumbnail
													__SCREAMING_SNAKE_CASE     :List[str]	       =						do_align_long_axis
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						do_pad
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						do_rescale
													__SCREAMING_SNAKE_CASE     :Tuple	       =						rescale_factor
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						do_normalize
													__SCREAMING_SNAKE_CASE     :Tuple	       =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   np.ndarray:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :int	       =						get_image_size(SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[int]	       =						size['''height'''], size['''width''']

													if (output_width < output_height and input_width > input_height) or (
													    output_width > output_height and input_width < input_height
													):
																			__SCREAMING_SNAKE_CASE     :Any	       =						np.rotaa(SCREAMING_SNAKE_CASE__	,3							)

													if data_format is not None:
																			__SCREAMING_SNAKE_CASE     :Optional[int]	       =						to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

													return image




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = None	,)    ->   np.ndarray:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :int	       =						size['''height'''], size['''width''']
													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :str	       =						get_image_size(SCREAMING_SNAKE_CASE__							)

													__SCREAMING_SNAKE_CASE     :Tuple	       =						output_width - input_width
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						output_height - input_height

													if random_padding:
																			__SCREAMING_SNAKE_CASE     :Optional[int]	       =						np.random.randint(low=0	,high=delta_height + 1							)
																			__SCREAMING_SNAKE_CASE     :Any	       =						np.random.randint(low=0	,high=delta_width + 1							)
													else:
																			__SCREAMING_SNAKE_CASE     :List[Any]	       =						delta_height // 2
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						delta_width // 2

													__SCREAMING_SNAKE_CASE     :Tuple	       =						delta_height - pad_top
													__SCREAMING_SNAKE_CASE     :List[str]	       =						delta_width - pad_left

													__SCREAMING_SNAKE_CASE     :Any	       =						((pad_top, pad_bottom), (pad_left, pad_right))
													return pad(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__							)




							def 					_UpperCamelCase  (		self	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   Any:





													"""simple docstring"""






													logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''							)
													return self.pad_image(*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :str	       =						get_image_size(SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Any	       =						size['''height'''], size['''width''']

													# We always resize to the smallest of either the input or output size.
													__SCREAMING_SNAKE_CASE     :Dict	       =						min(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :Tuple	       =						min(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

													if height == input_height and width == input_width:
																			return image

													if input_height > input_width:
																			__SCREAMING_SNAKE_CASE     :Dict	       =						int(input_width * height / input_height							)
													elif input_width > input_height:
																			__SCREAMING_SNAKE_CASE     :str	       =						int(input_height * width / input_width							)

													return resize(
													    SCREAMING_SNAKE_CASE__	,size=(height, width)	,resample=SCREAMING_SNAKE_CASE__	,reducing_gap=2.0	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :int	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :int	       =						min(size['''height''']	,size['''width''']							)
													__SCREAMING_SNAKE_CASE     :str	       =						get_resize_output_image_size(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,default_to_square=SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :List[str]	       =						resize(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,resample=SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)
													return resized_image




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   Any:





													"""simple docstring"""






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




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





													"""simple docstring"""






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




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST	,**SCREAMING_SNAKE_CASE__	,)    ->   PIL.Image.Image:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Any	       =						do_resize if do_resize is not None else self.do_resize
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						size if size is not None else self.size
													if isinstance(SCREAMING_SNAKE_CASE__	,(tuple, list)							):
																			# Previous feature extractor had size in (width, height) format
																			__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						size[::-1]
													__SCREAMING_SNAKE_CASE     :List[str]	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :Dict	       =						resample if resample is not None else self.resample
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
													__SCREAMING_SNAKE_CASE     :Dict	       =						do_pad if do_pad is not None else self.do_pad
													__SCREAMING_SNAKE_CASE     :List[str]	       =						do_rescale if do_rescale is not None else self.do_rescale
													__SCREAMING_SNAKE_CASE     :Dict	       =						rescale_factor if rescale_factor is not None else self.rescale_factor
													__SCREAMING_SNAKE_CASE     :List[str]	       =						do_normalize if do_normalize is not None else self.do_normalize
													__SCREAMING_SNAKE_CASE     :int	       =						image_mean if image_mean is not None else self.image_mean
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						image_std if image_std is not None else self.image_std

													__SCREAMING_SNAKE_CASE     :str	       =						make_list_of_images(SCREAMING_SNAKE_CASE__							)

													if not valid_images(SCREAMING_SNAKE_CASE__							):
																			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_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

													if do_pad and size is None:
																			raise ValueError('''Size must be specified if do_pad 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.
													__SCREAMING_SNAKE_CASE     :Any	       =						[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

													if do_align_long_axis:
																			__SCREAMING_SNAKE_CASE     :List[str]	       =						[self.align_long_axis(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__							) for image in images]

													if do_resize:
																			__SCREAMING_SNAKE_CASE     :str	       =						[self.resize(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,resample=SCREAMING_SNAKE_CASE__							) for image in images]

													if do_thumbnail:
																			__SCREAMING_SNAKE_CASE     :List[str]	       =						[self.thumbnail(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__							) for image in images]

													if do_pad:
																			__SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.pad_image(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,random_padding=SCREAMING_SNAKE_CASE__							) for image in images]

													if do_rescale:
																			__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						[self.rescale(image=SCREAMING_SNAKE_CASE__	,scale=SCREAMING_SNAKE_CASE__							) for image in images]

													if do_normalize:
																			__SCREAMING_SNAKE_CASE     :List[str]	       =						[self.normalize(image=SCREAMING_SNAKE_CASE__	,mean=SCREAMING_SNAKE_CASE__	,std=SCREAMING_SNAKE_CASE__							) for image in images]

													__SCREAMING_SNAKE_CASE     :Any	       =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) for image in images]

													__SCREAMING_SNAKE_CASE     :Tuple	       =						{'''pixel_values''': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE__	,tensor_type=SCREAMING_SNAKE_CASE__							)






"""simple docstring"""
def   __lowerCamelCase     (							a_  :	int = 10**6							)    ->   int:

      if n <= 0:
            raise ValueError('''Please enter an integer greater than 0'''							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						list(range(n + 1							)							)
      for number in range(2				,     n + 1							):
            if phi[number] == number:
                  phi[number] -= 1
                  for multiple in range(number * 2				,     n + 1				,     a_							):
                        __SCREAMING_SNAKE_CASE     :List[Any]	       =						(phi[multiple] // number) * (number - 1)

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						1
      for number in range(1				,     n + 1							):
            if (answer / phi[answer]) < (number / phi[number]):
                  __SCREAMING_SNAKE_CASE     :str	       =						number

      return answer


if __name__ == "__main__":
  print(solution())