




'''simple docstring'''





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class     lowercase  ( _UpperCamelCase):






      def __init__(self			,	a__			,	a__       )							-> List[str]:
         super().__init__()
         self.register_modules(unet=__lowerCamelCase			,	scheduler=__lowerCamelCase       )






      @torch.no_grad()
      def __call__(self			,	a__ = 1			,	a__ = None			,	a__ = 5_0			,	a__ = "pil"			,	a__ = True			,	**a__			,	)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,	generator=__lowerCamelCase			,	)
         SCREAMING_SNAKE_CASE__      :					Any							    =			image.to(self.device       )

         # set step values
         self.scheduler.set_timesteps(__lowerCamelCase       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
            # 1. predict noise model_output
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.unet(__lowerCamelCase			,	__lowerCamelCase       ).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.scheduler.step(__lowerCamelCase			,	__lowerCamelCase			,	__lowerCamelCase       ).prev_sample

         SCREAMING_SNAKE_CASE__      :					Any							    =			(image / 2 + 0.5).clamp(0			,	1       )
         SCREAMING_SNAKE_CASE__      :					int							    =			image.cpu().permute(0			,	2			,	3			,	1       ).numpy()
         if output_type == "pil":
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.numpy_to_pil(__lowerCamelCase       )

         if not return_dict:
            return (image,), "This is a local test"

         return ImagePipelineOutput(images=__lowerCamelCase       ), "This is a local test"







import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


__lowerCAmelCase						:							Optional[int]			     =						'./tests/fixtures/elise_format0.mid'




@unittest.skip("""The note-seq package currently throws an error on import"""  )
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Tuple									=   SpectrogramDiffusionPipeline
				SCREAMING_SNAKE_CASE_			:				Dict									=   PipelineTesterMixin.required_optional_params - {
				    """callback""",
				    """latents""",
				    """callback_steps""",
				    """output_type""",
				    """num_images_per_prompt""",
				}
				SCREAMING_SNAKE_CASE_			:				int									=   False
				SCREAMING_SNAKE_CASE_			:				Tuple									=   False
				SCREAMING_SNAKE_CASE_			:				str									=   TOKENS_TO_AUDIO_GENERATION_PARAMS
				SCREAMING_SNAKE_CASE_			:				List[str]									=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	str:
							torch.manual_seed(0				)
							a       =     SpectrogramNotesEncoder(
							    max_length=20_48   , vocab_size=15_36   , d_model=7_68   , dropout_rate=0.1   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , feed_forward_proj="gated-gelu"   , )

							a       =     SpectrogramContEncoder(
							    input_dims=1_28   , targets_context_length=2_56   , d_model=7_68   , dropout_rate=0.1   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , feed_forward_proj="gated-gelu"   , )

							a       =     TaFilmDecoder(
							    input_dims=1_28   , targets_length=2_56   , max_decoder_noise_time=20_000.0   , d_model=7_68   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , dropout_rate=0.1   , )

							a       =     DDPMScheduler()

							a       =     {
							    "notes_encoder": notes_encoder.eval(),
							    "continuous_encoder": continuous_encoder.eval(),
							    "decoder": decoder.eval(),
							    "scheduler": scheduler,
							    "melgan": None,
							}
							return components





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Union[str, Any]=0				)   ->	Dict:
							if str(__lowerCamelCase				).startswith("mps"				):
										a       =     torch.manual_seed(__lowerCamelCase				)
							else:
										a       =     torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
							a       =     {
							    "input_tokens": [
							        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
							    ],
							    "generator": generator,
							    "num_inference_steps": 4,
							    "output_type": "mel",
							}
							return inputs





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     "cpu"  # ensure determinism for the device-dependent torch.Generator
							a       =     self.get_dummy_components()
							a       =     SpectrogramDiffusionPipeline(**__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       =     mel[0, -3:, -3:]

							assert mel_slice.shape == (3, 3)
							a       =     np.array(
							    [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0]				)
							assert np.abs(mel_slice.flatten() - expected_slice				).max() < 1e-2





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





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





				@skip_mps
				def 				__UpperCAmelCase				( self							:	int				)   ->	Any:
							return super().test_save_load_optional_components()





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





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





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Tuple:
							pass





				@skip_mps
				def 				__UpperCAmelCase				( self							:	int				)   ->	Optional[Any]:
							return super().test_progress_bar()




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



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





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Any:
							# TODO - test that pipeline can decode tokens in a callback
							# so that music can be played live
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.melgan
							a       =     None

							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							def callback(__lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]				):
										# decode mel to audio
										a       =     melgan(input_features=mel_output.astype(np.floataa				)				)[0]
										assert len(audio[0]				) == 8_19_20 * (step + 1)
										# simulate that audio is played
										return audio

							a       =     MidiProcessor()
							a       =     processor(__lowerCamelCase				)

							a       =     input_tokens[:3]
							a       =     torch.manual_seed(0				)
							pipe(__lowerCamelCase   , num_inference_steps=5   , generator=__lowerCamelCase   , callback=__lowerCamelCase   , output_type="mel"				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Union[str, Any]:
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)
							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)
							# just run two denoising loops
							a       =     input_tokens[:2]

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=2   , generator=__lowerCamelCase				)

							a       =     output.audios[0]

							assert abs(np.abs(__lowerCamelCase				).sum() - 3_612.841				) < 1e-1





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

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)

							# just run 4 denoising loops
							a       =     input_tokens[:4]

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=1_00   , generator=__lowerCamelCase				)

							a       =     output.audios[0]
							assert abs(np.abs(__lowerCamelCase				).sum() - 9_389.1_111				) < 5e-2






