



'''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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase_	 =						logging.get_logger(__name__)






class __A    (						_UpperCamelCase		):



	'''simple docstring'''







	__lowerCamelCase					: Tuple    = ['pixel_values']



	def __init__(self				,     A = True				,     A = None				,     A = None				,     A = PILImageResampling.BILINEAR				,     A = True				,     A = 1 / 255				,     A = True				,     A = None				,     A = None				,     **A				,     )					->  Optional[int]:


						"""simple docstring"""



						super().__init__(**_UpperCAmelCase )
						_a											=						size if size is not None else {'''shortest_edge''': 384}
						_a											=						get_size_dict(_UpperCAmelCase				,     default_to_square=_UpperCAmelCase )

						_a											=						do_resize
						_a											=						size
						# Default value set here for backwards compatibility where the value in config is None
						_a											=						crop_pct if crop_pct is not None else 224 / 256
						_a											=						resample
						_a											=						do_rescale
						_a											=						rescale_factor
						_a											=						do_normalize
						_a											=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_a											=						image_std if image_std is not None else IMAGENET_STANDARD_STD



	def       a__						(self				,     A				,     A				,     A				,     A = PILImageResampling.BICUBIC				,     A = None				,     **A				,     )					->  List[Any]:


						"""simple docstring"""



						_a											=						get_size_dict(_UpperCAmelCase				,     default_to_square=_UpperCAmelCase )
						if "shortest_edge" not in size:
											raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''' )
						_a											=						size['''shortest_edge''']

						if shortest_edge < 384:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											_a											=						int(shortest_edge / crop_pct )
											_a											=						get_resize_output_image_size(_UpperCAmelCase				,     size=_UpperCAmelCase				,     default_to_square=_UpperCAmelCase )
											_a											=						resize(image=_UpperCAmelCase				,     size=_UpperCAmelCase				,     resample=_UpperCAmelCase				,     data_format=_UpperCAmelCase				,     **_UpperCAmelCase )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=_UpperCAmelCase				,     size=(shortest_edge, shortest_edge)				,     data_format=_UpperCAmelCase				,     **_UpperCAmelCase )
						else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    _UpperCAmelCase				,     size=(shortest_edge, shortest_edge)				,     resample=_UpperCAmelCase				,     data_format=_UpperCAmelCase				,     **_UpperCAmelCase )



	def       a__						(self				,     A				,     A				,     A = None				,     **A				,     )					->  Tuple:


						"""simple docstring"""



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



	def       a__						(self				,     A				,     A				,     A				,     A = None				,     **A				,     )					->  int:


						"""simple docstring"""



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



	def       a__						(self				,     A				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = None				,     A = ChannelDimension.FIRST				,     **A				,     )					->  Optional[int]:


						"""simple docstring"""



						_a											=						do_resize if do_resize is not None else self.do_resize
						_a											=						crop_pct if crop_pct is not None else self.crop_pct
						_a											=						resample if resample is not None else self.resample
						_a											=						do_rescale if do_rescale is not None else self.do_rescale
						_a											=						rescale_factor if rescale_factor is not None else self.rescale_factor
						_a											=						do_normalize if do_normalize is not None else self.do_normalize
						_a											=						image_mean if image_mean is not None else self.image_mean
						_a											=						image_std if image_std is not None else self.image_std

						_a											=						size if size is not None else self.size
						_a											=						get_size_dict(_UpperCAmelCase				,     default_to_square=_UpperCAmelCase )

						_a											=						make_list_of_images(_UpperCAmelCase )

						if not valid_images(_UpperCAmelCase ):
											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 or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.''' )

						if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.''' )

						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.
						_a											=						[to_numpy_array(_UpperCAmelCase ) for image in images]

						if do_resize:
											_a											=						[self.resize(image=_UpperCAmelCase				,     size=_UpperCAmelCase				,     crop_pct=_UpperCAmelCase				,     resample=_UpperCAmelCase ) for image in images]

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

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

						_a											=						[to_channel_dimension_format(_UpperCAmelCase				,     _UpperCAmelCase ) for image in images]

						_a											=						{'''pixel_values''': images}
						return BatchFeature(data=_UpperCAmelCase				,     tensor_type=_UpperCAmelCase )








'''simple docstring'''





from __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


if __name__ == "__main__":
  print(F"""{len(find_circular_primes()) = }""")



