





'''simple docstring'''







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()


snake_case_  :							str            =	"./tests/fixtures/elise_format0.mid"


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

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Dict:







													"""simple docstring"""
													torch.manual_seed(0 )
													UpperCAmelCase_      :   List[Any]      =	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'''							,  )

													UpperCAmelCase_      :   Tuple      =	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'''							,  )

													UpperCAmelCase_      :   List[Any]      =	TaFilmDecoder(
													    input_dims=1_28							,  targets_length=2_56							,  max_decoder_noise_time=2_0_0_0_0.0							,  d_model=7_68							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  dropout_rate=0.1							,  )

													UpperCAmelCase_      :   Optional[int]      =	DDPMScheduler()

													UpperCAmelCase_      :   List[Any]      =	{
													    '''notes_encoder''': notes_encoder.eval(),
													    '''continuous_encoder''': continuous_encoder.eval(),
													    '''decoder''': decoder.eval(),
													    '''scheduler''': scheduler,
													    '''melgan''': None,
													}
													return components

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  Union[str, Any]=0 )       ->							Any:







													"""simple docstring"""
													if str(__magic_name__ ).startswith('''mps''' ):
																			UpperCAmelCase_      :   Dict      =	torch.manual_seed(__magic_name__ )
													else:
																			UpperCAmelCase_      :   Optional[int]      =	torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''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				:  Optional[int] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_      :   str      =	self.get_dummy_components()
													UpperCAmelCase_      :   Optional[Any]      =	SpectrogramDiffusionPipeline(**__magic_name__ )
													UpperCAmelCase_      :   str      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   Dict      =	self.get_dummy_inputs(__magic_name__ )
													UpperCAmelCase_      :   Any      =	pipe(**__magic_name__ )
													UpperCAmelCase_      :   str      =	output.audios

													UpperCAmelCase_      :   List[Any]      =	mel[0, -3:, -3:]

													assert mel_slice.shape == (3, 3)
													UpperCAmelCase_      :   Union[str, Any]      =	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      UpperCAmelCase__						(				self				:  List[str] )       ->							Tuple:







													"""simple docstring"""
													return super().test_save_load_local()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													return super().test_save_load_optional_components()

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







													"""simple docstring"""
													return super().test_attention_slicing_forward_pass()

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							int:







													"""simple docstring"""
													pass

							def      UpperCAmelCase__						(				self				:  Any )       ->							Dict:







													"""simple docstring"""
													pass



							@skip_mps
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													return super().test_progress_bar()





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

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

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







													"""simple docstring"""
													# TODO - test that pipeline can decode tokens in a callback
													# so that music can be played live
													UpperCAmelCase_      :   Dict      =	torch_device

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

													UpperCAmelCase_      :   Optional[int]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													def callback(__magic_name__				:  str							,  __magic_name__				:  List[str] ):
																			# decode mel to audio
																			UpperCAmelCase_      :   Tuple      =	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

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()
													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	input_tokens[:3]
													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													pipe(__magic_name__							,  num_inference_steps=5							,  generator=__magic_name__							,  callback=__magic_name__							,  output_type='''mel''' )

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







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	torch_device

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

													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )
													# just run two denoising loops
													UpperCAmelCase_      :   int      =	input_tokens[:2]

													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Optional[Any]      =	pipe(__magic_name__							,  num_inference_steps=2							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	output.audios[0]

													assert abs(np.abs(__magic_name__ ).sum() - 3_6_1_2.8_4_1 ) < 1E-1



							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	torch_device

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

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[Any]      =	processor(__magic_name__ )

													# just run 4 denoising loops
													UpperCAmelCase_      :   Dict      =	input_tokens[:4]

													UpperCAmelCase_      :   Tuple      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	pipe(__magic_name__							,  num_inference_steps=1_00							,  generator=__magic_name__ )

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							=			{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPRContextEncoderTokenizerFast",
          "DPRQuestionEncoderTokenizerFast",
          "DPRReaderTokenizerFast",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPRContextEncoder",
          "DPRPretrainedContextEncoder",
          "DPRPreTrainedModel",
          "DPRPretrainedQuestionEncoder",
          "DPRPretrainedReader",
          "DPRQuestionEncoder",
          "DPRReader",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDPRContextEncoder",
          "TFDPRPretrainedContextEncoder",
          "TFDPRPretrainedQuestionEncoder",
          "TFDPRPretrainedReader",
          "TFDPRQuestionEncoder",
          "TFDPRReader",
      ]


if TYPE_CHECKING:
      from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
      from .tokenization_dpr import (
          DPRContextEncoderTokenizer,
          DPRQuestionEncoderTokenizer,
          DPRReaderOutput,
          DPRReaderTokenizer,
      )

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_dpr_fast import (
                DPRContextEncoderTokenizerFast,
                DPRQuestionEncoderTokenizerFast,
                DPRReaderTokenizerFast,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpr import (
                DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPRContextEncoder,
                DPRPretrainedContextEncoder,
                DPRPreTrainedModel,
                DPRPretrainedQuestionEncoder,
                DPRPretrainedReader,
                DPRQuestionEncoder,
                DPRReader,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_dpr import (
                TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDPRContextEncoder,
                TFDPRPretrainedContextEncoder,
                TFDPRPretrainedQuestionEncoder,
                TFDPRPretrainedReader,
                TFDPRQuestionEncoder,
                TFDPRReader,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





