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	(							a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[str, Any]					     =			SpectrogramDiffusionPipeline
 _lowerCamelCase    :						Union[str, Any]					     =			PipelineTesterMixin.required_optional_params - {
     'callback',
     'latents',
     'callback_steps',
     'output_type',
     'num_images_per_prompt',
 }
 _lowerCamelCase    :						int					     =			False
 _lowerCamelCase    :						Any					     =			False
 _lowerCamelCase    :						Tuple					     =			TOKENS_TO_AUDIO_GENERATION_PARAMS
 _lowerCamelCase    :						List[str]					     =			TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

 def        __snake_case       (      self							:		Tuple	)			->				Tuple:
        torch.manual_seed(0	)
        _UpperCamelCase  :							Any	    =	SpectrogramNotesEncoder(
            max_length=2_0_4_8							,      vocab_size=1_5_3_6							,      d_model=7_6_8							,      dropout_rate=0.1							,      num_layers=1							,      num_heads=1							,      d_kv=4							,      d_ff=2_0_4_8							,      feed_forward_proj='gated-gelu'							,      )

        _UpperCamelCase  :							int	    =	SpectrogramContEncoder(
            input_dims=1_2_8							,      targets_context_length=2_5_6							,      d_model=7_6_8							,      dropout_rate=0.1							,      num_layers=1							,      num_heads=1							,      d_kv=4							,      d_ff=2_0_4_8							,      feed_forward_proj='gated-gelu'							,      )

        _UpperCamelCase  :							Optional[int]	    =	TaFilmDecoder(
            input_dims=1_2_8							,      targets_length=2_5_6							,      max_decoder_noise_time=2_0_0_0_0.0							,      d_model=7_6_8							,      num_layers=1							,      num_heads=1							,      d_kv=4							,      d_ff=2_0_4_8							,      dropout_rate=0.1							,      )

        _UpperCamelCase  :							Tuple	    =	DDPMScheduler()

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

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		Any=0	)			->				str:

        if str(lowerCamelCase_	).startswith('mps'	):
               _UpperCamelCase  :							Any	    =	torch.manual_seed(lowerCamelCase_	)
        else:
               _UpperCamelCase  :							Optional[int]	    =	torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
        _UpperCamelCase  :							Tuple	    =	{
            'input_tokens': [
                [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
            ],
            'generator': generator,
            'num_inference_steps': 4,
            'output_type': 'mel',
        }
        return inputs

 def        __snake_case       (      self							:		Tuple	)			->				int:
        _UpperCamelCase  :							Optional[int]	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Any	    =	self.get_dummy_components()
        _UpperCamelCase  :							List[str]	    =	SpectrogramDiffusionPipeline(**lowerCamelCase_	)
        _UpperCamelCase  :							Optional[int]	    =	pipe.to(lowerCamelCase_	)
        pipe.set_progress_bar_config(disable=lowerCamelCase_	)

        _UpperCamelCase  :							Optional[int]	    =	self.get_dummy_inputs(lowerCamelCase_	)
        _UpperCamelCase  :							Optional[int]	    =	pipe(**lowerCamelCase_	)
        _UpperCamelCase  :							Optional[Any]	    =	output.audios

        _UpperCamelCase  :							Union[str, Any]	    =	mel[0, -3:, -3:]

        assert mel_slice.shape == (3, 3)
        _UpperCamelCase  :							List[Any]	    =	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        __snake_case       (      self							:		str	)			->				str:
        return super().test_save_load_local()

 @skip_mps
 def        __snake_case       (      self							:		Any	)			->				List[Any]:
        return super().test_dict_tuple_outputs_equivalent()

 @skip_mps
 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[int]:
        return super().test_save_load_optional_components()

 @skip_mps
 def        __snake_case       (      self							:		Any	)			->				int:
        return super().test_attention_slicing_forward_pass()

 def        __snake_case       (      self							:		Optional[Any]	)			->				Tuple:
        pass

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[int]:
        pass

 @skip_mps
 def        __snake_case       (      self							:		List[Any]	)			->				List[str]:
        return super().test_progress_bar()







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



 '''simple docstring'''

 def        __snake_case       (      self							:		Tuple	)			->				List[str]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def        __snake_case       (      self							:		Tuple	)			->				Optional[Any]:
        _UpperCamelCase  :							str	    =	torch_device

        _UpperCamelCase  :							Dict	    =	SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
        _UpperCamelCase  :							List[str]	    =	pipe.melgan
        _UpperCamelCase  :							Optional[Any]	    =	None

        _UpperCamelCase  :							List[str]	    =	pipe.to(lowerCamelCase_	)
        pipe.set_progress_bar_config(disable=lowerCamelCase_	)

        def callback(lowerCamelCase							:		str							,      lowerCamelCase							:		Optional[int]	):
               # decode mel to audio
               _UpperCamelCase  :							int	    =	melgan(input_features=mel_output.astype(np.floataa	)	)[0]
               assert len(audio[0]	) == 8_1_9_2_0 * (step + 1)
               # simulate that audio is played
               return audio

        _UpperCamelCase  :							str	    =	MidiProcessor()
        _UpperCamelCase  :							List[str]	    =	processor(lowerCamelCase_	)

        _UpperCamelCase  :							Dict	    =	input_tokens[:3]
        _UpperCamelCase  :							Optional[Any]	    =	torch.manual_seed(0	)
        pipe(lowerCamelCase_							,      num_inference_steps=5							,      generator=lowerCamelCase_							,      callback=lowerCamelCase_							,      output_type='mel'	)

 def        __snake_case       (      self							:		Any	)			->				Optional[Any]:
        _UpperCamelCase  :							Optional[Any]	    =	torch_device

        _UpperCamelCase  :							int	    =	SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
        _UpperCamelCase  :							int	    =	pipe.to(lowerCamelCase_	)
        pipe.set_progress_bar_config(disable=lowerCamelCase_	)
        _UpperCamelCase  :							List[Any]	    =	MidiProcessor()

        _UpperCamelCase  :							List[str]	    =	processor(lowerCamelCase_	)
        # just run two denoising loops
        _UpperCamelCase  :							Any	    =	input_tokens[:2]

        _UpperCamelCase  :							Optional[int]	    =	torch.manual_seed(0	)
        _UpperCamelCase  :							List[Any]	    =	pipe(lowerCamelCase_							,      num_inference_steps=2							,      generator=lowerCamelCase_	)

        _UpperCamelCase  :							List[str]	    =	output.audios[0]

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

 def        __snake_case       (      self							:		Tuple	)			->				Tuple:
        _UpperCamelCase  :							Any	    =	torch_device

        _UpperCamelCase  :							Tuple	    =	SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
        _UpperCamelCase  :							Any	    =	pipe.to(lowerCamelCase_	)
        pipe.set_progress_bar_config(disable=lowerCamelCase_	)

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

        _UpperCamelCase  :							List[Any]	    =	processor(lowerCamelCase_	)

        # just run 4 denoising loops
        _UpperCamelCase  :							Optional[Any]	    =	input_tokens[:4]

        _UpperCamelCase  :							Union[str, Any]	    =	torch.manual_seed(0	)
        _UpperCamelCase  :							Dict	    =	pipe(lowerCamelCase_							,      num_inference_steps=1_0_0							,      generator=lowerCamelCase_	)

        _UpperCamelCase  :							Dict	    =	output.audios[0]
        assert abs(np.abs(lowerCamelCase_	).sum() - 9_3_8_9.1_1_1_1	) < 5e-2








_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
