




"""simple docstring"""







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       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


    """simple docstring"""





    __UpperCAmelCase :     List[str]       =      DDIMPipeline
    __UpperCAmelCase :     List[str]       =      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    __UpperCAmelCase :     str       =      PipelineTesterMixin.required_optional_params - {
        "num_images_per_prompt",
        "latents",
        "callback",
        "callback_steps",
    }
    __UpperCAmelCase :     Union[str, Any]       =      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
    __UpperCAmelCase :     str       =      False



    def       snake_case    (     self					:	Union[str, Any]						):
      torch.manual_seed(0						)
      __lowercase				:		List[str]												=				UNetaDModel(
          block_out_channels=(3_2, 6_4)    ,   layers_per_block=2    ,   sample_size=3_2    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")    ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")    ,   )
      __lowercase				:		Any												=				DDIMScheduler()
      __lowercase				:		Optional[Any]												=				{"unet": unet, "scheduler": scheduler}
      return components



    def       snake_case    (     self					:	Any    ,   lowercase__					:	Tuple    ,   lowercase__					:	Union[str, Any]=0						):
      if str(lowercase__						).startswith("mps"						):
        __lowercase				:		Optional[Any]												=				torch.manual_seed(lowercase__						)
      else:
        __lowercase				:		Optional[Any]												=				torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
      __lowercase				:		Dict												=				{
          "batch_size": 1,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs



    def       snake_case    (     self					:	Optional[int]						):
      __lowercase				:		List[Any]												=				"cpu"

      __lowercase				:		Tuple												=				self.get_dummy_components()
      __lowercase				:		List[str]												=				self.pipeline_class(**lowercase__						)
      pipe.to(lowercase__						)
      pipe.set_progress_bar_config(disable=lowercase__						)

      __lowercase				:		Optional[int]												=				self.get_dummy_inputs(lowercase__						)
      __lowercase				:		Optional[int]												=				pipe(**lowercase__						).images
      __lowercase				:		List[Any]												=				image[0, -3:, -3:, -1]

      self.assertEqual(image.shape    ,   (1, 3_2, 3_2, 3)						)
      __lowercase				:		List[Any]												=				np.array(
          [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]						)
      __lowercase				:		str												=				np.abs(image_slice.flatten() - expected_slice						).max()
      self.assertLessEqual(lowercase__    ,   1e-3						)



    def       snake_case    (     self					:	List[Any]						):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3						)



    def       snake_case    (     self					:	int						):
      super().test_save_load_local(expected_max_difference=3e-3						)



    def       snake_case    (     self					:	Optional[int]						):
      super().test_save_load_optional_components(expected_max_difference=3e-3						)



    def       snake_case    (     self					:	Union[str, Any]						):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3						)





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


    """simple docstring"""





    def       snake_case    (     self					:	Union[str, Any]						):
      __lowercase				:		List[str]												=				"google/ddpm-cifar10-32"

      __lowercase				:		List[Any]												=				UNetaDModel.from_pretrained(lowercase__						)
      __lowercase				:		Optional[Any]												=				DDIMScheduler()

      __lowercase				:		Tuple												=				DDIMPipeline(unet=lowercase__    ,   scheduler=lowercase__						)
      ddim.to(lowercase__						)
      ddim.set_progress_bar_config(disable=lowercase__						)

      __lowercase				:		Optional[Any]												=				torch.manual_seed(0						)
      __lowercase				:		List[str]												=				ddim(generator=lowercase__    ,   eta=0.0    ,   output_type="numpy"						).images

      __lowercase				:		List[Any]												=				image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      __lowercase				:		Dict												=				np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]						)

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



    def       snake_case    (     self					:	Dict						):
      __lowercase				:		List[str]												=				"google/ddpm-ema-bedroom-256"

      __lowercase				:		Tuple												=				UNetaDModel.from_pretrained(lowercase__						)
      __lowercase				:		Tuple												=				DDIMScheduler.from_pretrained(lowercase__						)

      __lowercase				:		Union[str, Any]												=				DDIMPipeline(unet=lowercase__    ,   scheduler=lowercase__						)
      ddpm.to(lowercase__						)
      ddpm.set_progress_bar_config(disable=lowercase__						)

      __lowercase				:		Optional[Any]												=				torch.manual_seed(0						)
      __lowercase				:		Optional[int]												=				ddpm(generator=lowercase__    ,   output_type="numpy"						).images

      __lowercase				:		List[str]												=				image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      __lowercase				:		Tuple												=				np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]						)

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






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		return abs(_lowerCamelCase				) if a == 0 else greatest_common_divisor(b % a,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				__lowercase						,__lowercase				:		Any												=				y, x % y
		return abs(_lowerCamelCase				)

def 						snake_case__       (					)			->Optional[int]:






		"""simple docstring"""


		try:
				__lowercase				:		Optional[int]												=				input("Enter two integers separated by comma (,): "				).split(","				)
				__lowercase				:		Optional[Any]												=				int(nums[0]				)
				__lowercase				:		str												=				int(nums[1]				)
				print(
				    F'greatest_common_divisor({num_a}, {num_a}) = '
				    F'{greatest_common_divisor(_lowerCamelCase,  _lowerCamelCase				)}'				)
				print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowerCamelCase,  _lowerCamelCase				)}'				)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"				)


if __name__ == "__main__":
						main()
