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__						      =			'./tests/fixtures/elise_format0.mid'



@unittest.skip('The note-seq package currently throws an error on import'			)
class 			UpperCamelCase					(			_a ,	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 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           torch.manual_seed(0    )
           _lowerCamelCase           =					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'''					,					)

           _lowerCamelCase           =					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'''					,					)

           _lowerCamelCase           =					TaFilmDecoder(
               input_dims=1_28					,					targets_length=2_56					,					max_decoder_noise_time=2_00_00.0					,					d_model=7_68					,					num_layers=1					,					num_heads=1					,					d_kv=4					,					d_ff=20_48					,					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 		UpperCamelCase_    (       self					,					A_					,					A_=0    )			->					Union[str, Any]:






           """simple docstring"""
           if str(_a    ).startswith('''mps'''    ):
                 _lowerCamelCase           =					torch.manual_seed(_a    )
           else:
                 _lowerCamelCase           =					torch.Generator(device=_a    ).manual_seed(_a    )
           _lowerCamelCase           =					{
               "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[Any]:






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

           _lowerCamelCase           =					self.get_dummy_inputs(_a    )
           _lowerCamelCase           =					pipe(**_a    )
           _lowerCamelCase           =					output.audios

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

           assert mel_slice.shape == (3, 3)
           _lowerCamelCase           =					np.array(
               [-11.51_29_25, -4.788215, -0.46172905, -2.051715, -10.53_91_47, -10.97_09_63, -9.091634, 4.0, 4.0]    )
           assert np.abs(mel_slice.flatten() - expected_slice    ).max() < 1E-2



     @skip_mps
     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






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



     @skip_mps
     def 		UpperCamelCase_    (       self    )			->					str:






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



     @skip_mps
     def 		UpperCamelCase_    (       self    )			->					Tuple:






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



     @skip_mps
     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






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



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           pass



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






           """simple docstring"""
           pass





     @skip_mps
     def 		UpperCamelCase_    (       self    )			->					List[str]:






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





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

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					int:






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



     def 		UpperCamelCase_    (       self    )			->					Any:






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

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

           _lowerCamelCase           =					pipe.to(_a    )
           pipe.set_progress_bar_config(disable=_a    )

           def callback(A_					,					A_    ):
                 # decode mel to audio
                 _lowerCamelCase           =					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

           _lowerCamelCase           =					MidiProcessor()
           _lowerCamelCase           =					processor(_a    )

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



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






           """simple docstring"""
           _lowerCamelCase           =					torch_device

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

           _lowerCamelCase           =					processor(_a    )
           # just run two denoising loops
           _lowerCamelCase           =					input_tokens[:2]

           _lowerCamelCase           =					torch.manual_seed(0    )
           _lowerCamelCase           =					pipe(_a					,					num_inference_steps=2					,					generator=_a    )

           _lowerCamelCase           =					output.audios[0]

           assert abs(np.abs(_a    ).sum() - 36_12.8_41    ) < 1E-1





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






           """simple docstring"""
           _lowerCamelCase           =					torch_device

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

           _lowerCamelCase           =					MidiProcessor()

           _lowerCamelCase           =					processor(_a    )

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

           _lowerCamelCase           =					torch.manual_seed(0    )
           _lowerCamelCase           =					pipe(_a					,					num_inference_steps=1_00					,					generator=_a    )

           _lowerCamelCase           =					output.audios[0]
           assert abs(np.abs(_a    ).sum() - 93_89.11_11    ) < 5E-2




import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





