




"""simple docstring"""



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor




class      UpperCAmelCase_  (						unittest.TestCase							):




				def __init__(   self						:	Dict     ,			snake_case_						:	Dict     ,			snake_case_						:	int=7     ,			snake_case_						:	str=3     ,			snake_case_						:	Dict=18     ,			snake_case_						:	Dict=30     ,			snake_case_						:	List[Any]=400     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	Optional[int]=None     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	List[Any]=[0.5, 0.5, 0.5]     ,			snake_case_						:	Union[str, Any]=[0.5, 0.5, 0.5]     ,			)				->							int:




					'''simple docstring'''

					A__	=    size if size is not None else {'''height''': 18, '''width''': 18}
					A__	=    parent
					A__	=    batch_size
					A__	=    num_channels
					A__	=    image_size
					A__	=    min_resolution
					A__	=    max_resolution
					A__	=    do_resize
					A__	=    size
					A__	=    do_normalize
					A__	=    image_mean
					A__	=    image_std




				def 							__magic_name__							(   self						:	List[str]				)				->							int:




					'''simple docstring'''

					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					}






@require_torch
@require_vision
class      UpperCAmelCase_  (						lowerCamelCase__,       unittest.TestCase							):
				lowercase__						 =				DPTImageProcessor if is_vision_available() else None




				def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




					'''simple docstring'''

					A__	=    DPTImageProcessingTester(self				)




				@property
				def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Any:




					'''simple docstring'''

					return self.image_processor_tester.prepare_image_processor_dict()




				def 							__magic_name__							(   self						:	str				)				->							Union[str, Any]:




					'''simple docstring'''

					A__	=    self.image_processing_class(**self.image_processor_dict				)
					self.assertTrue(hasattr(__lowerCamelCase     ,			"image_mean"				)				)
					self.assertTrue(hasattr(__lowerCamelCase     ,			"image_std"				)				)
					self.assertTrue(hasattr(__lowerCamelCase     ,			"do_normalize"				)				)
					self.assertTrue(hasattr(__lowerCamelCase     ,			"do_resize"				)				)
					self.assertTrue(hasattr(__lowerCamelCase     ,			"size"				)				)




				def 							__magic_name__							(   self						:	List[str]				)				->							Any:




					'''simple docstring'''

					A__	=    self.image_processing_class.from_dict(self.image_processor_dict				)
					self.assertEqual(image_processor.size     ,			{"height": 18, "width": 18}				)

					A__	=    self.image_processing_class.from_dict(self.image_processor_dict     ,			size=42				)
					self.assertEqual(image_processor.size     ,			{"height": 42, "width": 42}				)




				def 							__magic_name__							(   self						:	Union[str, Any]				)				->							List[Any]:




					'''simple docstring'''

					A__	=    self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					A__	=    prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__lowerCamelCase				)
					for image in image_inputs:
						self.assertIsInstance(__lowerCamelCase     ,			Image.Image				)

					# Test not batched input
					A__	=    image_processing(image_inputs[0]     ,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,			(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )     ,			)

					# Test batched
					A__	=    image_processing(__lowerCamelCase     ,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,			(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )     ,			)




				def 							__magic_name__							(   self						:	str				)				->							Dict:




					'''simple docstring'''

					A__	=    self.image_processing_class(**self.image_processor_dict				)
					# create random numpy tensors
					A__	=    prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__lowerCamelCase     ,			numpify=__lowerCamelCase				)
					for image in image_inputs:
						self.assertIsInstance(__lowerCamelCase     ,			np.ndarray				)

					# Test not batched input
					A__	=    image_processing(image_inputs[0]     ,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,			(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )     ,			)

					# Test batched
					A__	=    image_processing(__lowerCamelCase     ,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,			(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )     ,			)




				def 							__magic_name__							(   self						:	List[Any]				)				->							Tuple:




					'''simple docstring'''

					A__	=    self.image_processing_class(**self.image_processor_dict				)
					# create random PyTorch tensors
					A__	=    prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__lowerCamelCase     ,			torchify=__lowerCamelCase				)
					for image in image_inputs:
						self.assertIsInstance(__lowerCamelCase     ,			torch.Tensor				)

					# Test not batched input
					A__	=    image_processing(image_inputs[0]     ,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,			(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )     ,			)

					# Test batched
					A__	=    image_processing(__lowerCamelCase     ,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,			(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )     ,			)











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

 if n % 2 == 0:
  return n == 2

 # this means n is odd
 A__	=    n - 1
 A__	=    0
 while d % 2 == 0:
  d /= 2
  exp += 1

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	abs(int(input("Enter bound : ").strip()))
      print("Here's the list of primes:")
      print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





