




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 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			str							=	SpectrogramDiffusionPipeline
     a		:			Tuple							=	PipelineTesterMixin.required_optional_params - {
         """callback""",
         """latents""",
         """callback_steps""",
         """output_type""",
         """num_images_per_prompt""",
     }
     a		:			str							=	False
     a		:			List[Any]							=	False
     a		:			Optional[int]							=	TOKENS_TO_AUDIO_GENERATION_PARAMS
     a		:			Any							=	TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS







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



          '''simple docstring'''



          torch.manual_seed(0 )
          a      =      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"  ,		)

          a      =      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"  ,		)

          a      =      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  ,		)

          a      =      DDPMScheduler()

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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=0 )  ->    Any:



          '''simple docstring'''



          if str(A ).startswith("mps" ):
               a      =      torch.manual_seed(A )
          else:
               a      =      torch.Generator(device=A ).manual_seed(A )
          a      =      {
              "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 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      "cpu"  # ensure determinism for the device-dependent torch.Generator
          a      =      self.get_dummy_components()
          a      =      SpectrogramDiffusionPipeline(**A )
          a      =      pipe.to(A )
          pipe.set_progress_bar_config(disable=A )

          a      =      self.get_dummy_inputs(A )
          a      =      pipe(**A )
          a      =      output.audios

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

          assert mel_slice.shape == (3, 3)
          a      =      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 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          return super().test_save_load_local()







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          return super().test_dict_tuple_outputs_equivalent()







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          return super().test_save_load_optional_components()







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



          '''simple docstring'''



          return super().test_attention_slicing_forward_pass()







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



          '''simple docstring'''



          pass







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          pass







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return super().test_progress_bar()







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







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



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







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



          '''simple docstring'''



          a      =      torch_device

          a      =      SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion" )
          a      =      pipe.melgan
          a      =      None

          a      =      pipe.to(A )
          pipe.set_progress_bar_config(disable=A )

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

          a      =      MidiProcessor()
          a      =      processor(A )

          a      =      input_tokens[:3]
          a      =      torch.manual_seed(0 )
          pipe(A  ,		num_inference_steps=5  ,		generator=A  ,		callback=A  ,		output_type="mel" )







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



          '''simple docstring'''



          a      =      torch_device

          a      =      SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion" )
          a      =      pipe.to(A )
          pipe.set_progress_bar_config(disable=A )
          a      =      MidiProcessor()

          a      =      processor(A )
          # just run two denoising loops
          a      =      input_tokens[:2]

          a      =      torch.manual_seed(0 )
          a      =      pipe(A  ,		num_inference_steps=2  ,		generator=A )

          a      =      output.audios[0]

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







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



          '''simple docstring'''



          a      =      torch_device

          a      =      SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion" )
          a      =      pipe.to(A )
          pipe.set_progress_bar_config(disable=A )

          a      =      MidiProcessor()

          a      =      processor(A )

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

          a      =      torch.manual_seed(0 )
          a      =      pipe(A  ,		num_inference_steps=100  ,		generator=A )

          a      =      output.audios[0]
          assert abs(np.abs(A ).sum() - 9_3_8_9.1_1_1_1 ) < 5e-2







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     list:
     a      =      []
     a      =      1

     for i in range(len(__UpperCamelCase)):
          if i + 1 < len(__UpperCamelCase) and text[i] == text[i + 1]:
               count += 1
          else:
               encoded.append((text[i], count))
               a      =      1

     return encoded

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
   from doctest import testmod

   testmod(name="run_length_encode", verbose=True)
   testmod(name="run_length_decode", verbose=True)

