from typing import Any

import numpy as np






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       assert np.shape(lowerCAmelCase__      )[0] == np.shape(lowerCAmelCase__      )[1]

       # If matrix not symmetric, exit right away.
       if np.allclose(lowerCAmelCase__      ,    matrix.T      ) is False:
              return False

       # Get eigenvalues and eignevectors for a symmetric matrix.
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   np.linalg.eigh(lowerCAmelCase__      )

       # Check sign of all eigenvalues.
       # np.all returns a value of type np.bool_
       return bool(np.all(eigen_values > 0      )      )






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   np.random.randn(lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.dot(lowerCAmelCase__      ,    random_matrix.T      )
       assert _is_matrix_spd(lowerCAmelCase__      )
       return spd_matrix






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = 1_0_0_0      ,    lowerCAmelCase__ = 1e-8      ,    ):
       assert np.shape(lowerCAmelCase__      )[0] == np.shape(lowerCAmelCase__      )[1]
       assert np.shape(lowerCAmelCase__      )[0] == np.shape(lowerCAmelCase__      )[0]
       assert _is_matrix_spd(lowerCAmelCase__      )

       # Initialize solution guess, residual, search direction.
       lowerCAmelCase__	      =   np.zeros((np.shape(lowerCAmelCase__      )[0], 1)      )
       lowerCAmelCase__	      =   np.copy(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.copy(lowerCAmelCase__      )

       # Set initial errors in solution guess and residual.
       lowerCAmelCase__	      =   1e9
       lowerCAmelCase__	      =   1e9
       lowerCAmelCase__	      =   1e9

       # Set iteration counter to threshold number of iterations.
       lowerCAmelCase__	      =   0

       while error > tol:
              # Save this value so we only calculate the matrix-vector product once.
              lowerCAmelCase__	      =   np.dot(lowerCAmelCase__      ,    lowerCAmelCase__      )

              # The main algorithm.

              # Update search direction magnitude.
              lowerCAmelCase__	      =   np.dot(ra.T      ,    lowerCAmelCase__      ) / np.dot(pa.T      ,    lowerCAmelCase__      )
              # Update solution guess.
              lowerCAmelCase__	      =   xa + alpha * pa
              # Calculate new residual.
              lowerCAmelCase__	      =   ra - alpha * w
              # Calculate new Krylov subspace scale.
              lowerCAmelCase__	      =   np.dot(r.T      ,    lowerCAmelCase__      ) / np.dot(ra.T      ,    lowerCAmelCase__      )
              # Calculate new A conjuage search direction.
              lowerCAmelCase__	      =   r + beta * pa

              # Calculate errors.
              lowerCAmelCase__	      =   np.linalg.norm(r - ra      )
              lowerCAmelCase__	      =   np.linalg.norm(x - xa      )
              lowerCAmelCase__	      =   np.maximum(lowerCAmelCase__      ,    lowerCAmelCase__      )

              # Update variables.
              lowerCAmelCase__	      =   np.copy(lowerCAmelCase__      )
              lowerCAmelCase__	      =   np.copy(lowerCAmelCase__      )
              lowerCAmelCase__	      =   np.copy(lowerCAmelCase__      )

              # Update number of iterations.
              iterations += 1
              if iterations > max_iterations:
                     break

       return x






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   3
       lowerCAmelCase__	      =   _create_spd_matrix(lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.random.randn(lowerCAmelCase__      ,    1      )
       lowerCAmelCase__	      =   np.dot(lowerCAmelCase__      ,    lowerCAmelCase__      )

       # Numpy solution.
       lowerCAmelCase__	      =   np.linalg.solve(lowerCAmelCase__      ,    lowerCAmelCase__      )

       # Our implementation.
       lowerCAmelCase__	      =   conjugate_gradient(lowerCAmelCase__      ,    lowerCAmelCase__      )

       # Ensure both solutions are close to x_true (and therefore one another).
       assert np.linalg.norm(x_numpy - x_true      ) <= 1e-6
       assert np.linalg.norm(x_conjugate_gradient - x_true      ) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_conjugate_gradient()

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
