




import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					DanceDiffusionPipeline
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					UNCONDITIONAL_AUDIO_GENERATION_PARAMS
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					PipelineTesterMixin.required_optional_params - {
				    """callback""",
				    """latents""",
				    """callback_steps""",
				    """output_type""",
				    """num_images_per_prompt""",
				}
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					False
				SCREAMING_SNAKE_CASE_       :							Dict							 =					False





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							torch.manual_seed(0							)
							a    =			UNetaDModel(
							    block_out_channels=(32, 32, 64)					,      extra_in_channels=16					,      sample_size=5_12					,      sample_rate=1_60_00					,      in_channels=2					,      out_channels=2					,      flip_sin_to_cos=__lowerCamelCase					,      use_timestep_embedding=__lowerCamelCase					,      time_embedding_type="fourier"					,      mid_block_type="UNetMidBlock1D"					,      down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,      up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,      )
							a    =			IPNDMScheduler()

							a    =			{
							    "unet": unet,
							    "scheduler": scheduler,
							}
							return components





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     int=0							)					->     Optional[Any]:
							if str(__lowerCamelCase							).startswith("mps"							):
										a    =			torch.manual_seed(__lowerCamelCase							)
							else:
										a    =			torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)
							a    =			{
							    "batch_size": 1,
							    "generator": generator,
							    "num_inference_steps": 4,
							}
							return inputs





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Any:
							a    =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							a    =			self.get_dummy_components()
							a    =			DanceDiffusionPipeline(**__lowerCamelCase							)
							a    =			pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			self.get_dummy_inputs(__lowerCamelCase							)
							a    =			pipe(**__lowerCamelCase							)
							a    =			output.audios

							a    =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							a    =			np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000]							)
							assert np.abs(audio_slice.flatten() - expected_slice							).max() < 1e-2





				@skip_mps
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[Any]:
							return super().test_save_load_local()





				@skip_mps
				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3							)





				@skip_mps
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[str]:
							return super().test_save_load_optional_components()





				@skip_mps
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Union[str, Any]:
							return super().test_attention_slicing_forward_pass()





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




@slow
@require_torch_gpu
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     str:
							a    =			torch_device

							a    =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"							)
							a    =			pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			torch.manual_seed(0							)
							a    =			pipe(generator=__lowerCamelCase					,      num_inference_steps=1_00					,      audio_length_in_s=4.096							)
							a    =			output.audios

							a    =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							a    =			np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020]							)

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





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[Any]:
							a    =			torch_device

							a    =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"					,      torch_dtype=torch.floataa							)
							a    =			pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			torch.manual_seed(0							)
							a    =			pipe(generator=__lowerCamelCase					,      num_inference_steps=1_00					,      audio_length_in_s=4.096							)
							a    =			output.audios

							a    =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							a    =			np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341]							)

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












import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






