


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case												=							{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case												=							['''FlavaFeatureExtractor''']
   __snake_case												=							['''FlavaImageProcessor''']
   __snake_case												=							['''FlavaProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case												=							[
       '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlavaForPreTraining''',
       '''FlavaImageCodebook''',
       '''FlavaImageModel''',
       '''FlavaModel''',
       '''FlavaMultimodalModel''',
       '''FlavaPreTrainedModel''',
       '''FlavaTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_flava import (
       FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
       FlavaConfig,
       FlavaImageCodebookConfig,
       FlavaImageConfig,
       FlavaMultimodalConfig,
       FlavaTextConfig,
   )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_flava import FlavaFeatureExtractor
      from .image_processing_flava import FlavaImageProcessor
      from .processing_flava import FlavaProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flava import (
          FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlavaForPreTraining,
          FlavaImageCodebook,
          FlavaImageModel,
          FlavaModel,
          FlavaMultimodalModel,
          FlavaPreTrainedModel,
          FlavaTextModel,
      )

else:
   import sys

   __snake_case												=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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__      		=					'./tests/fixtures/elise_format0.mid'
@unittest.skip('The note-seq package currently throws an error on import'		)
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   SpectrogramDiffusionPipeline
 UpperCAmelCase_      					=   PipelineTesterMixin.required_optional_params - {
     'callback',
     'latents',
     'callback_steps',
     'output_type',
     'num_images_per_prompt',
 }
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   TOKENS_TO_AUDIO_GENERATION_PARAMS
 UpperCAmelCase_      					=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__	      =   SpectrogramNotesEncoder(
            max_length=2_048     ,       vocab_size=1_536     ,       d_model=768     ,       dropout_rate=0.1     ,       num_layers=1     ,       num_heads=1     ,       d_kv=4     ,       d_ff=2_048     ,       feed_forward_proj='gated-gelu'     ,       )

        lowerCAmelCase__	      =   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=2_048     ,       feed_forward_proj='gated-gelu'     ,       )

        lowerCAmelCase__	      =   TaFilmDecoder(
            input_dims=128     ,       targets_length=256     ,       max_decoder_noise_time=20_000.0     ,       d_model=768     ,       num_layers=1     ,       num_heads=1     ,       d_kv=4     ,       d_ff=2_048     ,       dropout_rate=0.1     ,       )

        lowerCAmelCase__	      =   DDPMScheduler()

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




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    List[str]=0):







        '''simple docstring'''



        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__	      =   torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__	      =   torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__	      =   {
            'input_tokens': [
                [1_134, 90, 1_135, 1_133, 1_080, 112, 1_132, 1_080, 1_133, 1_079, 133, 1_132, 1_079, 1_133, 1] + [0] * 2_033
            ],
            'generator': generator,
            'num_inference_steps': 4,
            'output_type': 'mel',
        }
        return inputs




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__	      =   self.get_dummy_components()
        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline(**lowercase__)
        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__	      =   self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__	      =   pipe(**lowercase__)
        lowerCAmelCase__	      =   output.audios

        lowerCAmelCase__	      =   mel[0, -3:, -3:]

        assert mel_slice.shape == (3, 3)
        lowerCAmelCase__	      =   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 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        return super().test_save_load_local()




 @skip_mps
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        return super().test_save_load_optional_components()




 @skip_mps
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        return super().test_attention_slicing_forward_pass()




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        pass




 @skip_mps
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return super().test_progress_bar()






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






 '''simple docstring'''






 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch_device

        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__	      =   pipe.melgan
        lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        def callback(lowercase__   :    int     ,       lowercase__   :    List[Any]):
               # decode mel to audio
               lowerCAmelCase__	      =   melgan(input_features=mel_output.astype(np.floataa))[0]
               assert len(audio[0]) == 81_920 * (step + 1)
               # simulate that audio is played
               return audio

        lowerCAmelCase__	      =   MidiProcessor()
        lowerCAmelCase__	      =   processor(lowercase__)

        lowerCAmelCase__	      =   input_tokens[:3]
        lowerCAmelCase__	      =   torch.manual_seed(0)
        pipe(lowercase__     ,       num_inference_steps=5     ,       generator=lowercase__     ,       callback=lowercase__     ,       output_type='mel')




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch_device

        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        lowerCAmelCase__	      =   MidiProcessor()

        lowerCAmelCase__	      =   processor(lowercase__)
        # just run two denoising loops
        lowerCAmelCase__	      =   input_tokens[:2]

        lowerCAmelCase__	      =   torch.manual_seed(0)
        lowerCAmelCase__	      =   pipe(lowercase__     ,       num_inference_steps=2     ,       generator=lowercase__)

        lowerCAmelCase__	      =   output.audios[0]

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




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch_device

        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__	      =   MidiProcessor()

        lowerCAmelCase__	      =   processor(lowercase__)

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

        lowerCAmelCase__	      =   torch.manual_seed(0)
        lowerCAmelCase__	      =   pipe(lowercase__     ,       num_inference_steps=100     ,       generator=lowercase__)

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