

'''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     _SCREAMING_SNAKE_CASE       (       __a						,unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE	:Union[str, Any]     			=	DDIMPipeline
 __SCREAMING_SNAKE_CASE	:Optional[Any]     			=	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __SCREAMING_SNAKE_CASE	:Tuple     			=	PipelineTesterMixin.required_optional_params - {
     """num_images_per_prompt""",
     """latents""",
     """callback""",
     """callback_steps""",
 }
 __SCREAMING_SNAKE_CASE	:Tuple     			=	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE	:Optional[int]     			=	False



 def 					snake_case__    (       self						:       Optional[int]     ):
      torch.manual_seed(0     )
      __magic_name__		  =							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''')					, )
      __magic_name__		  =							DDIMScheduler()
      __magic_name__		  =							{'''unet''': unet, '''scheduler''': scheduler}
      return components



 def 					snake_case__    (       self						:       Optional[int]					, a__						:       str					, a__						:       str=0     ):
      if str(lowerCamelCase_     ).startswith('''mps'''     ):
           __magic_name__		  =							torch.manual_seed(lowerCamelCase_     )
      else:
           __magic_name__		  =							torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
      __magic_name__		  =							{
          '''batch_size''': 1,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__		  =							'''cpu'''

      __magic_name__		  =							self.get_dummy_components()
      __magic_name__		  =							self.pipeline_class(**lowerCamelCase_     )
      pipe.to(lowerCamelCase_     )
      pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

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



 def 					snake_case__    (       self						:       Tuple     ):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )



 def 					snake_case__    (       self						:       List[str]     ):
      super().test_save_load_local(expected_max_difference=3E-3     )



 def 					snake_case__    (       self						:       Dict     ):
      super().test_save_load_optional_components(expected_max_difference=3E-3     )



 def 					snake_case__    (       self						:       Optional[int]     ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3     )






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



 def 					snake_case__    (       self						:       List[str]     ):
      __magic_name__		  =							'''google/ddpm-cifar10-32'''

      __magic_name__		  =							UNetaDModel.from_pretrained(lowerCamelCase_     )
      __magic_name__		  =							DDIMScheduler()

      __magic_name__		  =							DDIMPipeline(unet=lowerCamelCase_					, scheduler=lowerCamelCase_     )
      ddim.to(lowerCamelCase_     )
      ddim.set_progress_bar_config(disable=lowerCamelCase_     )

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

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

      assert image.shape == (1, 32, 32, 3)
      __magic_name__		  =							np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]     )

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



 def 					snake_case__    (       self						:       Optional[Any]     ):
      __magic_name__		  =							'''google/ddpm-ema-bedroom-256'''

      __magic_name__		  =							UNetaDModel.from_pretrained(lowerCamelCase_     )
      __magic_name__		  =							DDIMScheduler.from_pretrained(lowerCamelCase_     )

      __magic_name__		  =							DDIMPipeline(unet=lowerCamelCase_					, scheduler=lowerCamelCase_     )
      ddpm.to(lowerCamelCase_     )
      ddpm.set_progress_bar_config(disable=lowerCamelCase_     )

      __magic_name__		  =							torch.manual_seed(0     )
      __magic_name__		  =							ddpm(generator=lowerCamelCase_					, output_type='''numpy'''     ).images

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

      assert image.shape == (1, 256, 256, 3)
      __magic_name__		  =							np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]     )

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









"""simple docstring"""


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		min(_a				)
      A				  =		max(_a				)
      # normalize data
      return [round((x - x_min) / (x_max - x_min)     ,   _a				) for x in data]





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		mean(_a				)
      A				  =		stdev(_a				)
      # standardize data
      return [round((x - mu) / (sigma)     ,   _a				) for x in data]




