

'''simple docstring'''
import unittest
from timeit import timeit







def       _A	(			snake_case	,							snake_case  )    ->			int:
   _lowercase    :   str      =     first_num if first_num >= second_num else second_num
   _lowercase    :   str      =     max_num
   while (common_mult % first_num > 0) or (common_mult % second_num > 0):
      common_mult += max_num
   return common_mult







def       _A	(			snake_case	,							snake_case  )    ->			int:
   return b if a == 0 else greatest_common_divisor(b % a	,							snake_case  )







def       _A	(			snake_case	,							snake_case  )    ->			int:
   return first_num // greatest_common_divisor(snake_case	,							snake_case  ) * second_num







def       _A	(			)    ->			Tuple:
   _lowercase    :   Any      =     (
       "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
   )
   print(
       "least_common_multiple_slow():"	,							timeit("least_common_multiple_slow(1000, 999)"	,							setup=snake_case  )	,							)
   print(
       "least_common_multiple_fast():"	,							timeit("least_common_multiple_fast(1000, 999)"	,							setup=snake_case  )	,							)


class      a__						(							unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							(
       (10, 20),
       (13, 15),
       (4, 31),
       (10, 42),
       (43, 34),
       (5, 12),
       (12, 25),
       (10, 25),
       (6, 9),
   )
   _SCREAMING_SNAKE_CASE					:  int			 =							(20, 195, 124, 210, 1462, 60, 300, 50, 18)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      for i, (first_num, second_num) in enumerate(self.test_inputs						):
         _lowercase    :   Tuple      =     least_common_multiple_slow(_UpperCamelCase    ,       _UpperCamelCase						)
         _lowercase    :   int      =     least_common_multiple_fast(_UpperCamelCase    ,       _UpperCamelCase						)
         with self.subTest(i=_UpperCamelCase						):
            self.assertEqual(_UpperCamelCase    ,       self.expected_results[i]						)
            self.assertEqual(_UpperCamelCase    ,       self.expected_results[i]						)


if __name__ == "__main__":
    benchmark()
    unittest.main()









'''simple docstring'''
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      a__						(							unittest.TestCase						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=7    ,       _UpperCamelCase=3    ,       _UpperCamelCase=30    ,       _UpperCamelCase=400    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=False    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     size if size is not None else {"height": 30, "width": 30}
      _lowercase    :   str      =     parent
      _lowercase    :   Optional[int]      =     batch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   Any      =     min_resolution
      _lowercase    :   Any      =     max_resolution
      _lowercase    :   Tuple      =     do_resize
      _lowercase    :   List[Any]      =     size
      _lowercase    :   Tuple      =     do_normalize
      _lowercase    :   Optional[Any]      =     image_mean
      _lowercase    :   Any      =     image_std
      _lowercase    :   Dict      =     do_reduce_labels






   def 		_lowerCamelCase     (				self						):





      """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       _A	(			)    ->			List[Any]:
   _lowercase    :   List[str]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   Union[str, Any]      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   Optional[int]      =     Image.open(dataset[1]["file"]  )

   return image, map







def       _A	(			)    ->			Optional[int]:
   _lowercase    :   List[Any]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   int      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   str      =     Image.open(dataset[1]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[2]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[3]["file"]  )

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


@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							SegformerImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     SegformerImageProcessingTester(self						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_reduce_labels"						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Dict      =     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    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       reduce_labels=_UpperCamelCase						)
      self.assertEqual(image_processor.size    ,       {"height": 42, "width": 42}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      _lowercase    :   str      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       Image.Image						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     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    :   int      =     image_processing(_UpperCamelCase    ,       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 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _lowercase    :   List[str]      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       numpify=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       np.ndarray						)

      # Test not batched input
      _lowercase    :   Dict      =     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    :   List[Any]      =     image_processing(_UpperCamelCase    ,       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 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   List[Any]      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       torch.Tensor						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     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    :   Dict      =     image_processing(_UpperCamelCase    ,       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 		_lowerCamelCase     (				self						):





      """simple docstring"""





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

      # Test not batched input
      _lowercase    :   List[str]      =     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    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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    :   Optional[int]      =     prepare_semantic_single_inputs()

      _lowercase    :   int      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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    :   Dict      =     prepare_semantic_batch_inputs()

      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     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    :   str      =     prepare_semantic_single_inputs()
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 150						)

      _lowercase    :   str      =     True
      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)






