



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  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      DanceDiffusionPipeline
     _A      =      UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     _A      =      PipelineTesterMixin.required_optional_params - {
         'callback',
         'latents',
         'callback_steps',
         'output_type',
         'num_images_per_prompt',
     }
     _A      =      UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     _A      =      False
     _A      =      False







     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		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=__a					,      use_timestep_embedding=__a					,      time_embedding_type="fourier"					,      mid_block_type="UNetMidBlock1D"					,      down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,      up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,      )
           lowerCamelCase             =		IPNDMScheduler()

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







     def     _a   (self					,      __a					,      __a=0     ):






           '''simple docstring'''



           if str(__a     ).startswith("mps"     ):
                 lowerCamelCase             =		torch.manual_seed(__a     )
           else:
                 lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(__a     )
           lowerCamelCase             =		{
               "batch_size": 1,
               "generator": generator,
               "num_inference_steps": 4,
           }
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		DanceDiffusionPipeline(**__a     )
           lowerCamelCase             =		pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		pipe(**__a     )
           lowerCamelCase             =		output.audios

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

           assert audio.shape == (1, 2, components["unet"].sample_size)
           lowerCamelCase             =		np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]     )
           assert np.abs(audio_slice.flatten() - expected_slice     ).max() < 1E-2







     @skip_mps
     def     _a   (self     ):






           '''simple docstring'''



           return super().test_save_load_local()







     @skip_mps
     def     _a   (self     ):






           '''simple docstring'''



           return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )







     @skip_mps
     def     _a   (self     ):






           '''simple docstring'''



           return super().test_save_load_optional_components()







     @skip_mps
     def     _a   (self     ):






           '''simple docstring'''



           return super().test_attention_slicing_forward_pass()







     def     _a   (self     ):






           '''simple docstring'''



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





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



     def     _a   (self     ):






           '''simple docstring'''



           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		torch_device

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

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

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

           assert audio.shape == (1, 2, pipe.unet.sample_size)
           lowerCamelCase             =		np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]     )

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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		torch_device

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

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

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

           assert audio.shape == (1, 2, pipe.unet.sample_size)
           lowerCamelCase             =		np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]     )

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




from ..utils import DummyObject, requires_backends





class  lowerCamelCase__	(    metaclass=UpperCAmelCase_):
     """simple docstring"""



     _A      =      ['transformers', 'torch', 'note_seq']







     def __init__(self					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(self					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )