


'''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        a   (   snake_case_     , unittest.TestCase				):
 _lowerCAmelCase           =		DDIMPipeline
 _lowerCAmelCase           =		UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 _lowerCAmelCase           =		PipelineTesterMixin.required_optional_params - {
     """num_images_per_prompt""",
     """latents""",
     """callback""",
     """callback_steps""",
 }
 _lowerCAmelCase           =		UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 _lowerCAmelCase           =		False
 def 							__UpperCAmelCase						(       self  )    ->							Dict:
      torch.manual_seed(0  )
      _a						=							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')					, )
      _a						=							DDIMScheduler()
      _a						=							{'unet': unet, 'scheduler': scheduler}
      return components
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__=0  )    ->							Any:
      if str(__magic_name__  ).startswith('mps'  ):
           _a						=							torch.manual_seed(__magic_name__  )
      else:
           _a						=							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _a						=							{
          'batch_size': 1,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }
      return inputs
 def 							__UpperCAmelCase						(       self  )    ->							int:
      _a						=							'cpu'

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

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

      self.assertEqual(image.shape					, (1, 32, 32, 3)  )
      _a						=							np.array(
          [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]  )
      _a						=							np.abs(image_slice.flatten() - expected_slice  ).max()
      self.assertLessEqual(__magic_name__					, 1e-3  )
 def 							__UpperCAmelCase						(       self  )    ->							int:
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )
 def 							__UpperCAmelCase						(       self  )    ->							Union[str, Any]:
      super().test_save_load_local(expected_max_difference=3e-3  )
 def 							__UpperCAmelCase						(       self  )    ->							List[Any]:
      super().test_save_load_optional_components(expected_max_difference=3e-3  )




 def 							__UpperCAmelCase						(       self  )    ->							Any:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





@slow
@require_torch_gpu
class        a   (   unittest.TestCase				):
 def 							__UpperCAmelCase						(       self  )    ->							Dict:
      _a						=							'google/ddpm-cifar10-32'

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

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

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

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

      assert image.shape == (1, 32, 32, 3)
      _a						=							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:
      _a						=							'google/ddpm-ema-bedroom-256'

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

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

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

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

      assert image.shape == (1, 2_56, 2_56, 3)
      _a						=							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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


