




import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase_		=							logging.get_logger(__name__)





class 		A__    (       __SCREAMING_SNAKE_CASE			):






	def __init__(     self    ,					*lowerCamelCase    ,					**lowerCamelCase						)				->       None:




						"""simple docstring"""






						warnings.warn(
						    '''The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use LayoutLMv3ImageProcessor instead.'''    ,					lowerCamelCase    ,					)
						super().__init__(*lowerCamelCase    ,					**lowerCamelCase						)









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()


lowercase_		=							'''./tests/fixtures/elise_format0.mid'''





@unittest.skip("The note-seq package currently throws an error on import"			)
class 		A__    (       __SCREAMING_SNAKE_CASE		,					unittest.TestCase			):
	lowerCamelCase__		:			Optional[int]       =SpectrogramDiffusionPipeline
	lowerCamelCase__		:			List[str]       =PipelineTesterMixin.required_optional_params - {
	    "callback",
	    "latents",
	    "callback_steps",
	    "output_type",
	    "num_images_per_prompt",
	}
	lowerCamelCase__		:			Union[str, Any]       =False
	lowerCamelCase__		:			List[Any]       =False
	lowerCamelCase__		:			List[str]       =TOKENS_TO_AUDIO_GENERATION_PARAMS
	lowerCamelCase__		:			str       =TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						torch.manual_seed(0						)
						__magic_name__ :							Optional[Any]		       =							SpectrogramNotesEncoder(
						    max_length=2048    ,					vocab_size=1536    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					feed_forward_proj='''gated-gelu'''    ,					)

						__magic_name__ :							Any		       =							SpectrogramContEncoder(
						    input_dims=128    ,					targets_context_length=256    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					feed_forward_proj='''gated-gelu'''    ,					)

						__magic_name__ :							List[Any]		       =							TaFilmDecoder(
						    input_dims=128    ,					targets_length=256    ,					max_decoder_noise_time=2_0_0_0_0.0    ,					d_model=768    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					dropout_rate=0.1    ,					)

						__magic_name__ :							Any		       =							DDPMScheduler()

						__magic_name__ :							Dict		       =							{
						    '''notes_encoder''': notes_encoder.eval(),
						    '''continuous_encoder''': continuous_encoder.eval(),
						    '''decoder''': decoder.eval(),
						    '''scheduler''': scheduler,
						    '''melgan''': None,
						}
						return components






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=0						)				->       Dict:




						"""simple docstring"""






						if str(lowerCamelCase						).startswith('''mps'''						):
											__magic_name__ :							List[str]		       =							torch.manual_seed(lowerCamelCase						)
						else:
											__magic_name__ :							Any		       =							torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
						__magic_name__ :							Any		       =							{
						    '''input_tokens''': [
						        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
						    ],
						    '''generator''': generator,
						    '''num_inference_steps''': 4,
						    '''output_type''': '''mel''',
						}
						return inputs






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						__magic_name__ :							Union[str, Any]		       =							self.get_dummy_components()
						__magic_name__ :							str		       =							SpectrogramDiffusionPipeline(**lowerCamelCase						)
						__magic_name__ :							List[Any]		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__ :							Tuple		       =							self.get_dummy_inputs(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							pipe(**lowerCamelCase						)
						__magic_name__ :							Any		       =							output.audios

						__magic_name__ :							Union[str, Any]		       =							mel[0, -3:, -3:]

						assert mel_slice.shape == (3, 3)
						__magic_name__ :							Dict		       =							np.array(
						    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0]						)
						assert np.abs(mel_slice.flatten() - expected_slice						).max() < 1e-2






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_local()






	@skip_mps
	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_optional_components()






	@skip_mps
	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						return super().test_attention_slicing_forward_pass()






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						pass






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						pass






	@skip_mps
	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 		A__    (       unittest.TestCase			):






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






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






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							torch_device

						__magic_name__ :							Any		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							Optional[int]		       =							pipe.melgan
						__magic_name__ :							List[str]		       =							None

						__magic_name__ :							Tuple		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						def callback(lowerCamelCase    ,					lowerCamelCase						):
											# decode mel to audio
											__magic_name__ :							Tuple		       =							melgan(input_features=mel_output.astype(np.floataa						)						)[0]
											assert len(audio[0]						) == 81920 * (step + 1)
											# simulate that audio is played
											return audio

						__magic_name__ :							int		       =							MidiProcessor()
						__magic_name__ :							List[str]		       =							processor(lowerCamelCase						)

						__magic_name__ :							Dict		       =							input_tokens[:3]
						__magic_name__ :							Any		       =							torch.manual_seed(0						)
						pipe(lowerCamelCase    ,					num_inference_steps=5    ,					generator=lowerCamelCase    ,					callback=lowerCamelCase    ,					output_type='''mel'''						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							int		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							str		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)
						__magic_name__ :							Any		       =							MidiProcessor()

						__magic_name__ :							Optional[Any]		       =							processor(lowerCamelCase						)
						# just run two denoising loops
						__magic_name__ :							Any		       =							input_tokens[:2]

						__magic_name__ :							int		       =							torch.manual_seed(0						)
						__magic_name__ :							Any		       =							pipe(lowerCamelCase    ,					num_inference_steps=2    ,					generator=lowerCamelCase						)

						__magic_name__ :							Union[str, Any]		       =							output.audios[0]

						assert abs(np.abs(lowerCamelCase						).sum() - 3_6_1_2.8_4_1						) < 1e-1






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							str		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							List[Any]		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							MidiProcessor()

						__magic_name__ :							str		       =							processor(lowerCamelCase						)

						# just run 4 denoising loops
						__magic_name__ :							List[str]		       =							input_tokens[:4]

						__magic_name__ :							Union[str, Any]		       =							torch.manual_seed(0						)
						__magic_name__ :							Dict		       =							pipe(lowerCamelCase    ,					num_inference_steps=100    ,					generator=lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							output.audios[0]
						assert abs(np.abs(lowerCamelCase						).sum() - 9_3_8_9.1_1_1_1						) < 5e-2



