



'''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()


_UpperCamelCase							       =		"""./tests/fixtures/elise_format0.mid"""






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




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




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Any:
    torch.manual_seed(0     )
    __lowerCamelCase : Union[str, 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'    ,			)

    __lowerCamelCase : Tuple					 =				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'    ,			)

    __lowerCamelCase : str					 =				TaFilmDecoder(
        input_dims=128    ,			targets_length=256    ,			max_decoder_noise_time=2_0000.0    ,			d_model=768    ,			num_layers=1    ,			num_heads=1    ,			d_kv=4    ,			d_ff=2048    ,			dropout_rate=0.1    ,			)

    __lowerCamelCase : str					 =				DDPMScheduler()

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




  def 		_lowercase  (				self   :			str    ,			_a   :			Optional[Any]    ,			_a   :			Optional[int]=0     )      ->      Any:

    if str(__SCREAMING_SNAKE_CASE     ).startswith('mps'     ):
      __lowerCamelCase : Optional[int]					 =				torch.manual_seed(__SCREAMING_SNAKE_CASE     )
    else:
      __lowerCamelCase : Tuple					 =				torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )
    __lowerCamelCase : Optional[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   :			List[Any]     )      ->      Dict:
    __lowerCamelCase : str					 =				'cpu'  # ensure determinism for the device-dependent torch.Generator
    __lowerCamelCase : Optional[Any]					 =				self.get_dummy_components()
    __lowerCamelCase : Any					 =				SpectrogramDiffusionPipeline(**__SCREAMING_SNAKE_CASE     )
    __lowerCamelCase : Optional[int]					 =				pipe.to(__SCREAMING_SNAKE_CASE     )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

    __lowerCamelCase : Optional[int]					 =				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )
    __lowerCamelCase : List[Any]					 =				pipe(**__SCREAMING_SNAKE_CASE     )
    __lowerCamelCase : Union[str, Any]					 =				output.audios

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

    assert mel_slice.shape == (3, 3)
    __lowerCamelCase : int					 =				np.array(
        [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]     )
    assert np.abs(mel_slice.flatten() - expected_slice     ).max() < 1e-2




  @skip_mps
  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      str:
    return super().test_save_load_local()




  @skip_mps
  def 		_lowercase  (				self   :			str     )      ->      Dict:
    return super().test_dict_tuple_outputs_equivalent()




  @skip_mps
  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Optional[Any]:
    return super().test_save_load_optional_components()




  @skip_mps
  def 		_lowercase  (				self   :			Dict     )      ->      int:
    return super().test_attention_slicing_forward_pass()




  def 		_lowercase  (				self   :			Any     )      ->      int:
    pass




  def 		_lowercase  (				self   :			Optional[int]     )      ->      Optional[int]:
    pass




  @skip_mps
  def 		_lowercase  (				self   :			Tuple     )      ->      Union[str, Any]:
    return super().test_progress_bar()






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




  def 		_lowercase  (				self   :			str     )      ->      Dict:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  def 		_lowercase  (				self   :			List[str]     )      ->      List[str]:
    __lowerCamelCase : Optional[int]					 =				torch_device

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

    __lowerCamelCase : Optional[Any]					 =				pipe.to(__SCREAMING_SNAKE_CASE     )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

    def callback(_a   :			List[Any]    ,			_a   :			Optional[Any]     ):
      # decode mel to audio
      __lowerCamelCase : int					 =				melgan(input_features=mel_output.astype(np.floataa     )     )[0]
      assert len(audio[0]     ) == 8_1920 * (step + 1)
      # simulate that audio is played
      return audio

    __lowerCamelCase : str					 =				MidiProcessor()
    __lowerCamelCase : str					 =				processor(__SCREAMING_SNAKE_CASE     )

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




  def 		_lowercase  (				self   :			List[Any]     )      ->      Tuple:
    __lowerCamelCase : List[str]					 =				torch_device

    __lowerCamelCase : Any					 =				SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'     )
    __lowerCamelCase : int					 =				pipe.to(__SCREAMING_SNAKE_CASE     )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )
    __lowerCamelCase : Optional[int]					 =				MidiProcessor()

    __lowerCamelCase : List[Any]					 =				processor(__SCREAMING_SNAKE_CASE     )
    # just run two denoising loops
    __lowerCamelCase : Tuple					 =				input_tokens[:2]

    __lowerCamelCase : Optional[int]					 =				torch.manual_seed(0     )
    __lowerCamelCase : Optional[Any]					 =				pipe(__SCREAMING_SNAKE_CASE    ,			num_inference_steps=2    ,			generator=__SCREAMING_SNAKE_CASE     )

    __lowerCamelCase : Any					 =				output.audios[0]

    assert abs(np.abs(__SCREAMING_SNAKE_CASE     ).sum() - 3612.841     ) < 1e-1




  def 		_lowercase  (				self   :			Tuple     )      ->      Tuple:
    __lowerCamelCase : Optional[Any]					 =				torch_device

    __lowerCamelCase : Optional[int]					 =				SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'     )
    __lowerCamelCase : Tuple					 =				pipe.to(__SCREAMING_SNAKE_CASE     )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

    __lowerCamelCase : Union[str, Any]					 =				MidiProcessor()

    __lowerCamelCase : int					 =				processor(__SCREAMING_SNAKE_CASE     )

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

    __lowerCamelCase : str					 =				torch.manual_seed(0     )
    __lowerCamelCase : Union[str, Any]					 =				pipe(__SCREAMING_SNAKE_CASE    ,			num_inference_steps=100    ,			generator=__SCREAMING_SNAKE_CASE     )

    __lowerCamelCase : int					 =				output.audios[0]
    assert abs(np.abs(__SCREAMING_SNAKE_CASE     ).sum() - 9389.1111     ) < 5e-2










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase							       =		{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizerFast']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez import BarthezTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez_fast import BarthezTokenizerFast

else:
   import sys

   _UpperCamelCase							       =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






