


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(						_a, unittest.TestCase   ):
       lowercase_ :	Dict     =			DDIMPipeline
       lowercase_ :	Dict     =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       lowercase_ :	Tuple     =			PipelineTesterMixin.required_optional_params - {
           """num_images_per_prompt""",
           """latents""",
           """callback""",
           """callback_steps""",
       }
       lowercase_ :	List[Any]     =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       lowercase_ :	Dict     =			False
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Any      		=		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'),      )
           _lowercase       :							List[Any]      		=		DDIMScheduler()
           _lowercase       :							str      		=		{'unet': unet, 'scheduler': scheduler}
           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							str      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Dict      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							List[Any]      		=		{
               'batch_size': 1,
               'generator': generator,
               'num_inference_steps': 2,
               'output_type': 'numpy',
           }
           return inputs
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		'cpu'

           _lowercase       :							List[Any]      		=		self.get_dummy_components()
           _lowercase       :							int      		=		self.pipeline_class(**lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Dict      		=		pipe(**lowerCamelCase).images
           _lowercase       :							List[Any]      		=		image[0, -3:, -3:, -1]

           self.assertEqual(image.shape,      (1, 32, 32, 3))
           _lowercase       :							List[Any]      		=		np.array(
               [1.0_00E00, 5.7_17E-01, 4.7_17E-01, 1.0_00E00, 0.0_00E00, 1.0_00E00, 3.0_00E-04, 0.0_00E00, 9.0_00E-04])
           _lowercase       :							Optional[Any]      		=		np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(lowerCamelCase,      1E-3)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           super().test_save_load_local(expected_max_difference=3E-3)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

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




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




           """simple docstring"""

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






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		'google/ddpm-cifar10-32'

           _lowercase       :							Dict      		=		UNetaDModel.from_pretrained(lowerCamelCase)
           _lowercase       :							Tuple      		=		DDIMScheduler()

           _lowercase       :							List[Any]      		=		DDIMPipeline(unet=lowerCamelCase,      scheduler=lowerCamelCase)
           ddim.to(lowerCamelCase)
           ddim.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							int      		=		torch.manual_seed(0)
           _lowercase       :							Union[str, Any]      		=		ddim(generator=lowerCamelCase,      eta=0.0,      output_type='numpy').images

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

           assert image.shape == (1, 32, 32, 3)
           _lowercase       :							Optional[int]      		=		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 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Tuple      		=		'google/ddpm-ema-bedroom-256'

           _lowercase       :							Union[str, Any]      		=		UNetaDModel.from_pretrained(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		DDIMScheduler.from_pretrained(lowerCamelCase)

           _lowercase       :							Tuple      		=		DDIMPipeline(unet=lowerCamelCase,      scheduler=lowerCamelCase)
           ddpm.to(lowerCamelCase)
           ddpm.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							str      		=		torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		ddpm(generator=lowerCamelCase,      output_type='numpy').images

           _lowercase       :							Tuple      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 2_56, 2_56, 3)
           _lowercase       :							int      		=		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




import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
						print(solution())




