





import unittest

import numpy as np
from datasets import load_dataset

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

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 SegformerImageProcessor
class 				SCREAMING_SNAKE_CASE   (unittest.TestCase      ):


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





     """simple docstring"""







     lowercase__				=  size if size is not None else {'height': 30, 'width': 30}
     lowercase__				=  parent
     lowercase__				=  batch_size
     lowercase__				=  num_channels
     lowercase__				=  min_resolution
     lowercase__				=  max_resolution
     lowercase__				=  do_resize
     lowercase__				=  size
     lowercase__				=  do_normalize
     lowercase__				=  image_mean
     lowercase__				=  image_std
     lowercase__				=  do_reduce_labels


   def        SCREAMING_SNAKE_CASE_							(    self			:				List[Any]       )->   Optional[Any]:





     """simple docstring"""







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






def 			__UpperCamelCase  ()				->   List[str]:
  lowercase__				=  load_dataset('hf-internal-testing/fixtures_ade20k'		,						split='test'		)

  lowercase__				=  Image.open(dataset[0]['file']		)
  lowercase__				=  Image.open(dataset[1]['file']		)

  return image, map




def 			__UpperCamelCase  ()				->   str:
  lowercase__				=  load_dataset('hf-internal-testing/fixtures_ade20k'		,						split='test'		)

  lowercase__				=  Image.open(dataset[0]['file']		)
  lowercase__				=  Image.open(dataset[1]['file']		)
  lowercase__				=  Image.open(dataset[2]['file']		)
  lowercase__				=  Image.open(dataset[3]['file']		)

  return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE   (lowerCAmelCase__			,	unittest.TestCase      ):
   _UpperCamelCase    :		List[str] 					=       SegformerImageProcessor if is_vision_available() else None


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   int:





     """simple docstring"""







     lowercase__				=  SegformerImageProcessingTester(self       )


   @property
   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[Any]       )->   List[Any]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def        SCREAMING_SNAKE_CASE_							(    self			:				Union[str, Any]       )->   Optional[int]:





     """simple docstring"""







     lowercase__				=  self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(_lowerCamelCase			,     'do_resize'       )       )
     self.assertTrue(hasattr(_lowerCamelCase			,     'size'       )       )
     self.assertTrue(hasattr(_lowerCamelCase			,     'do_normalize'       )       )
     self.assertTrue(hasattr(_lowerCamelCase			,     'image_mean'       )       )
     self.assertTrue(hasattr(_lowerCamelCase			,     'image_std'       )       )
     self.assertTrue(hasattr(_lowerCamelCase			,     'do_reduce_labels'       )       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				str       )->   str:





     """simple docstring"""







     lowercase__				=  self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size			,     {'height': 30, 'width': 30}       )
     self.assertEqual(image_processor.do_reduce_labels			,     _lowerCamelCase       )

     lowercase__				=  self.image_processing_class.from_dict(self.image_processor_dict			,     size=42			,     reduce_labels=_lowerCamelCase       )
     self.assertEqual(image_processor.size			,     {'height': 42, 'width': 42}       )
     self.assertEqual(image_processor.do_reduce_labels			,     _lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				int       )->   str:





     """simple docstring"""







     pass


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[int]       )->   Tuple:





     """simple docstring"""







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

     # Test not batched input
     lowercase__				=  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
     lowercase__				=  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        SCREAMING_SNAKE_CASE_							(    self			:				List[str]       )->   Optional[int]:





     """simple docstring"""







     lowercase__				=  self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     lowercase__				=  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
     lowercase__				=  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
     lowercase__				=  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        SCREAMING_SNAKE_CASE_							(    self			:				Tuple       )->   Any:





     """simple docstring"""







     lowercase__				=  self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     lowercase__				=  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
     lowercase__				=  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
     lowercase__				=  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        SCREAMING_SNAKE_CASE_							(    self			:				List[str]       )->   Tuple:





     """simple docstring"""







     lowercase__				=  self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     lowercase__				=  prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_lowerCamelCase			,     torchify=_lowerCamelCase       )
     lowercase__				=  []
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase			,     torch.Tensor       )
       maps.append(torch.zeros(image.shape[-2:]       ).long()       )

     # Test not batched input
     lowercase__				=  image_processing(image_inputs[0]			,     maps[0]			,     return_tensors='pt'       )
     self.assertEqual(
         encoding['pixel_values'].shape			,     (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(
         encoding['labels'].shape			,     (
             1,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(encoding['labels'].dtype			,     torch.long       )
     self.assertTrue(encoding['labels'].min().item() >= 0       )
     self.assertTrue(encoding['labels'].max().item() <= 255       )

     # Test batched
     lowercase__				=  image_processing(_lowerCamelCase			,     _lowerCamelCase			,     return_tensors='pt'       )
     self.assertEqual(
         encoding['pixel_values'].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'],
         )			,     )
     self.assertEqual(
         encoding['labels'].shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(encoding['labels'].dtype			,     torch.long       )
     self.assertTrue(encoding['labels'].min().item() >= 0       )
     self.assertTrue(encoding['labels'].max().item() <= 255       )

     # Test not batched input (PIL images)
     lowercase__			,    lowercase__				=  prepare_semantic_single_inputs()

     lowercase__				=  image_processing(_lowerCamelCase			,     _lowerCamelCase			,     return_tensors='pt'       )
     self.assertEqual(
         encoding['pixel_values'].shape			,     (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(
         encoding['labels'].shape			,     (
             1,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(encoding['labels'].dtype			,     torch.long       )
     self.assertTrue(encoding['labels'].min().item() >= 0       )
     self.assertTrue(encoding['labels'].max().item() <= 255       )

     # Test batched input (PIL images)
     lowercase__			,    lowercase__				=  prepare_semantic_batch_inputs()

     lowercase__				=  image_processing(_lowerCamelCase			,     _lowerCamelCase			,     return_tensors='pt'       )
     self.assertEqual(
         encoding['pixel_values'].shape			,     (
             2,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(
         encoding['labels'].shape			,     (
             2,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )			,     )
     self.assertEqual(encoding['labels'].dtype			,     torch.long       )
     self.assertTrue(encoding['labels'].min().item() >= 0       )
     self.assertTrue(encoding['labels'].max().item() <= 255       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[Any]       )->   Optional[int]:





     """simple docstring"""







     lowercase__				=  self.image_processing_class(**self.image_processor_dict       )

     # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
     lowercase__			,    lowercase__				=  prepare_semantic_single_inputs()
     lowercase__				=  image_processing(_lowerCamelCase			,     _lowerCamelCase			,     return_tensors='pt'       )
     self.assertTrue(encoding['labels'].min().item() >= 0       )
     self.assertTrue(encoding['labels'].max().item() <= 150       )

     lowercase__				=  True
     lowercase__				=  image_processing(_lowerCamelCase			,     _lowerCamelCase			,     return_tensors='pt'       )
     self.assertTrue(encoding['labels'].min().item() >= 0       )
     self.assertTrue(encoding['labels'].max().item() <= 255       )









'''simple docstring'''



import math




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,				int(math.sqrt(lowerCamelCase_ ) + 1 )						,				6 ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True




def       _lowerCAmelCase  ( lowerCamelCase_	:     int = 6_0_0_8_5_1_4_7_5_1_4_3 ):

			try:
						__lowercase   =							int(lowerCamelCase_ )
			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   =							0
			if is_prime(lowerCamelCase_ ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(lowerCamelCase_ ):
						return n
			for i in range(3						,				int(math.sqrt(lowerCamelCase_ ) ) + 1						,				2 ):
						if n % i == 0:
									if is_prime(n // i ):
												__lowercase   =							n // i
												break
									elif is_prime(lowerCamelCase_ ):
												__lowercase   =							i
			return max_number


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





