import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	List[Any]						=DDIMPipeline
  SCREAMING_SNAKE_CASE_	:	List[Any]						=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  SCREAMING_SNAKE_CASE_	:	Tuple						=PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  SCREAMING_SNAKE_CASE_	:	int						=False






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       torch.manual_seed(0	)
       UpperCamelCase__										=      UNetaDModel(
           block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,				)
       UpperCamelCase__										=      DDIMScheduler()
       UpperCamelCase__										=      {'''unet''': unet, '''scheduler''': scheduler}
       return components






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=0	)		->      str:





       '''simple docstring'''





       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            UpperCamelCase__										=      torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            UpperCamelCase__										=      torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {
           '''batch_size''': 1,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      '''cpu'''

       UpperCamelCase__										=      self.get_dummy_components()
       UpperCamelCase__										=      self.pipeline_class(**SCREAMING_SNAKE_CASE_	)
       pipe.to(SCREAMING_SNAKE_CASE_	)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      pipe(**SCREAMING_SNAKE_CASE_	).images
       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       self.assertEqual(image.shape   ,				(1, 32, 32, 3)	)
       UpperCamelCase__										=      np.array(
           [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]	)
       UpperCamelCase__										=      np.abs(image_slice.flatten() - expected_slice	).max()
       self.assertLessEqual(SCREAMING_SNAKE_CASE_   ,				1E-3	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       super().test_save_load_local(expected_max_difference=3E-3	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       super().test_save_load_optional_components(expected_max_difference=3E-3	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       super().test_inference_batch_single_identical(expected_max_diff=3E-3	)



@slow
@require_torch_gpu
class     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      '''google/ddpm-cifar10-32'''

       UpperCamelCase__										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      DDIMScheduler()

       UpperCamelCase__										=      DDIMPipeline(unet=SCREAMING_SNAKE_CASE_   ,				scheduler=SCREAMING_SNAKE_CASE_	)
       ddim.to(SCREAMING_SNAKE_CASE_	)
       ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.manual_seed(0	)
       UpperCamelCase__										=      ddim(generator=SCREAMING_SNAKE_CASE_   ,				eta=0.0   ,				output_type='''numpy'''	).images

       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__										=      np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      '''google/ddpm-ema-bedroom-256'''

       UpperCamelCase__										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      DDIMPipeline(unet=SCREAMING_SNAKE_CASE_   ,				scheduler=SCREAMING_SNAKE_CASE_	)
       ddpm.to(SCREAMING_SNAKE_CASE_	)
       ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.manual_seed(0	)
       UpperCamelCase__										=      ddpm(generator=SCREAMING_SNAKE_CASE_   ,				output_type='''numpy'''	).images

       UpperCamelCase__										=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)
       UpperCamelCase__										=      np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     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.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


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



