





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:     Dict 					=logging.get_logger(__name__)

_lowercase		:     Any 					={
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class 	UpperCamelCase_     (     snake_case__       ):
					_a			:	List[str]							=				'vilt'



					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)

							lowerCamelCase_  :    str   	=			vocab_size
							lowerCamelCase_  :    Dict   	=			type_vocab_size
							lowerCamelCase_  :    str   	=			modality_type_vocab_size
							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings

							lowerCamelCase_  :    List[Any]   	=			hidden_size
							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
							lowerCamelCase_  :    Any   	=			num_attention_heads
							lowerCamelCase_  :    List[Any]   	=			intermediate_size
							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
							lowerCamelCase_  :    Any   	=			initializer_range
							lowerCamelCase_  :    str   	=			layer_norm_eps

							lowerCamelCase_  :    Optional[Any]   	=			image_size
							lowerCamelCase_  :    List[Any]   	=			patch_size
							lowerCamelCase_  :    Tuple   	=			num_channels
							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
							lowerCamelCase_  :    Optional[int]   	=			max_image_length
							lowerCamelCase_  :    Optional[Any]   	=			num_images







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		:     Optional[Any] 					="""./tests/fixtures/elise_format0.mid"""






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



					def      __a    (		self    :    Dict	):
							torch.manual_seed(0	)
							lowerCamelCase_  :    List[str]   	=			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'					,			)

							lowerCamelCase_  :    Union[str, Any]   	=			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'					,			)

							lowerCamelCase_  :    Union[str, Any]   	=			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					,			)

							lowerCamelCase_  :    Any   	=			DDPMScheduler()

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



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    int=0	):
							if str(lowerCamelCase	).startswith('mps'	):
									lowerCamelCase_  :    Optional[int]   	=			torch.manual_seed(lowerCamelCase	)
							else:
									lowerCamelCase_  :    Dict   	=			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
							lowerCamelCase_  :    str   	=			{
							    '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      __a    (		self    :    Dict	):
							lowerCamelCase_  :    str   	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
							lowerCamelCase_  :    Dict   	=			self.get_dummy_components()
							lowerCamelCase_  :    Tuple   	=			SpectrogramDiffusionPipeline(**lowerCamelCase	)
							lowerCamelCase_  :    List[str]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

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

							lowerCamelCase_  :    Tuple   	=			mel[0, -3:, -3:]

							assert mel_slice.shape == (3, 3)
							lowerCamelCase_  :    List[str]   	=			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      __a    (		self    :    Any	):
							return super().test_save_load_local()



					@skip_mps
					def      __a    (		self    :    Tuple	):
							return super().test_dict_tuple_outputs_equivalent()



					@skip_mps
					def      __a    (		self    :    Dict	):
							return super().test_save_load_optional_components()



					@skip_mps
					def      __a    (		self    :    str	):
							return super().test_attention_slicing_forward_pass()



					def      __a    (		self    :    Any	):
							pass



					def      __a    (		self    :    Optional[int]	):
							pass





					@skip_mps
					def      __a    (		self    :    Optional[Any]	):
							return super().test_progress_bar()






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



					def      __a    (		self    :    Optional[int]	):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



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

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

							lowerCamelCase_  :    str   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							def callback(lowerCamelCase    :    int					,			lowerCamelCase    :    int	):
									# decode mel to audio
									lowerCamelCase_  :    Union[str, Any]   	=			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

							lowerCamelCase_  :    Any   	=			MidiProcessor()
							lowerCamelCase_  :    str   	=			processor(lowerCamelCase	)

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



					def      __a    (		self    :    str	):
							lowerCamelCase_  :    List[str]   	=			torch_device

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

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

							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			num_inference_steps=2					,			generator=lowerCamelCase	)

							lowerCamelCase_  :    Dict   	=			output.audios[0]

							assert abs(np.abs(lowerCamelCase	).sum() - 3_612.841	) < 1E-1





					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    int   	=			torch_device

							lowerCamelCase_  :    str   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
							lowerCamelCase_  :    Optional[int]   	=			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							lowerCamelCase_  :    Tuple   	=			MidiProcessor()

							lowerCamelCase_  :    int   	=			processor(lowerCamelCase	)

							# just run 4 denoising loops
							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:4]

							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase					,			num_inference_steps=1_00					,			generator=lowerCamelCase	)

							lowerCamelCase_  :    List[Any]   	=			output.audios[0]
							assert abs(np.abs(lowerCamelCase	).sum() - 9_389.1_111	) < 5E-2
