"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
				from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__A     = logging.get_logger(__name__)  # pylint: disable=invalid-name

__A     = 256
class        _snake_case   (    __UpperCAmelCase						):
						snake_case__	     =	["melgan"]






						def __init__(				self					:	List[str]							,    UpperCAmelCase					:	SpectrogramNotesEncoder							,    UpperCAmelCase					:	SpectrogramContEncoder							,    UpperCAmelCase					:	TaFilmDecoder							,    UpperCAmelCase					:	DDPMScheduler							,    UpperCAmelCase					:	OnnxRuntimeModel if is_onnx_available() else Any							,    ):
													super().__init__()

													# From MELGAN
													__lowerCamelCase    :						Optional[int]						      =   math.log(1E-5			)  # Matches MelGAN training.
													__lowerCamelCase    :						Union[str, Any]						      =   4.0  # Largest value for most examples
													__lowerCamelCase    :						Tuple						      =   128

													self.register_modules(
													    notes_encoder=lowerCamelCase__							,    continuous_encoder=lowerCamelCase__							,    decoder=lowerCamelCase__							,    scheduler=lowerCamelCase__							,    melgan=lowerCamelCase__							,    )






						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	Tuple							,    UpperCAmelCase					:	List[str]=(-1.0, 1.0)							,    UpperCAmelCase					:	str=False			):
													__lowerCamelCase      ,     __lowerCamelCase    :						List[Any]						      =   output_range
													if clip:
																				__lowerCamelCase    :						Optional[Any]						      =   torch.clip(lowerCamelCase__							,    self.min_value							,    self.max_value			)
													# Scale to [0, 1].
													__lowerCamelCase    :						Dict						      =   (features - self.min_value) / (self.max_value - self.min_value)
													# Scale to [min_out, max_out].
													return zero_one * (max_out - min_out) + min_out






						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	Union[str, Any]							,    UpperCAmelCase					:	Tuple=(-1.0, 1.0)							,    UpperCAmelCase					:	List[str]=False			):
													__lowerCamelCase      ,     __lowerCamelCase    :						List[Any]						      =   input_range
													__lowerCamelCase    :						Dict						      =   torch.clip(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__			) if clip else outputs
													# Scale to [0, 1].
													__lowerCamelCase    :						Optional[Any]						      =   (outputs - min_out) / (max_out - min_out)
													# Scale to [self.min_value, self.max_value].
													return zero_one * (self.max_value - self.min_value) + self.min_value






						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	Tuple							,    UpperCAmelCase					:	int							,    UpperCAmelCase					:	List[Any]			):
													__lowerCamelCase    :						List[str]						      =   input_tokens > 0
													__lowerCamelCase      ,     __lowerCamelCase    :						List[Any]						      =   self.notes_encoder(
													    encoder_input_tokens=lowerCamelCase__							,    encoder_inputs_mask=lowerCamelCase__			)

													__lowerCamelCase      ,     __lowerCamelCase    :						int						      =   self.continuous_encoder(
													    encoder_inputs=lowerCamelCase__							,    encoder_inputs_mask=lowerCamelCase__			)

													return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]






						def 					lowerCamelCase__   (				self					:	Any							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	Tuple							,    UpperCAmelCase					:	Tuple			):
													__lowerCamelCase    :						int						      =   noise_time
													if not torch.is_tensor(lowerCamelCase__			):
																				__lowerCamelCase    :						Any						      =   torch.tensor([timesteps]							,    dtype=torch.long							,    device=input_tokens.device			)
													elif torch.is_tensor(lowerCamelCase__			) and len(timesteps.shape			) == 0:
																				__lowerCamelCase    :						str						      =   timesteps[None].to(input_tokens.device			)

													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													__lowerCamelCase    :						int						      =   timesteps * torch.ones(input_tokens.shape[0]							,    dtype=timesteps.dtype							,    device=timesteps.device			)

													__lowerCamelCase    :						Dict						      =   self.decoder(
													    encodings_and_masks=lowerCamelCase__							,    decoder_input_tokens=lowerCamelCase__							,    decoder_noise_time=lowerCamelCase__			)
													return logits







						@torch.no_grad()
						def __call__(				self					:	Any							,    UpperCAmelCase					:	List[List[int]]							,    UpperCAmelCase					:	Optional[torch.Generator] = None							,    UpperCAmelCase					:	int = 100							,    UpperCAmelCase					:	bool = True							,    UpperCAmelCase					:	str = "numpy"							,    UpperCAmelCase					:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,    UpperCAmelCase					:	int = 1							,    ):
													if (callback_steps is None) or (
													    callback_steps is not None and (not isinstance(lowerCamelCase__							,    lowerCamelCase__			) or callback_steps <= 0)
													):
																				raise ValueError(
																				    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																				    F""" {type(lowerCamelCase__			)}."""			)

													__lowerCamelCase    :						List[Any]						      =   np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							,    dtype=np.floataa			)
													__lowerCamelCase    :						Optional[Any]						      =   np.zeros([1, 0, self.n_dims]							,    np.floataa			)
													__lowerCamelCase    :						Optional[int]						      =   torch.ones((1, TARGET_FEATURE_LENGTH)							,    dtype=lowerCamelCase__							,    device=self.device			)

													for i, encoder_input_tokens in enumerate(lowerCamelCase__			):
																				if i == 0:
																											__lowerCamelCase    :						int						      =   torch.from_numpy(pred_mel[:1].copy()			).to(
																											    device=self.device							,    dtype=self.decoder.dtype			)
																											# The first chunk has no previous context.
																											__lowerCamelCase    :						List[str]						      =   torch.zeros((1, TARGET_FEATURE_LENGTH)							,    dtype=lowerCamelCase__							,    device=self.device			)
																				else:
																											# The full song pipeline does not feed in a context feature, so the mask
																											# will be all 0s after the feature converter. Because we know we're
																											# feeding in a full context chunk from the previous prediction, set it
																											# to all 1s.
																											__lowerCamelCase    :						Optional[Any]						      =   ones

																				__lowerCamelCase    :						Optional[Any]						      =   self.scale_features(
																				    lowerCamelCase__							,    output_range=[-1.0, 1.0]							,    clip=lowerCamelCase__			)

																				__lowerCamelCase    :						int						      =   self.encode(
																				    input_tokens=torch.IntTensor([encoder_input_tokens]			).to(device=self.device			)							,    continuous_inputs=lowerCamelCase__							,    continuous_mask=lowerCamelCase__							,    )

																				# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																				__lowerCamelCase    :						Any						      =   randn_tensor(
																				    shape=encoder_continuous_inputs.shape							,    generator=lowerCamelCase__							,    device=self.device							,    dtype=self.decoder.dtype							,    )

																				# set step values
																				self.scheduler.set_timesteps(lowerCamelCase__			)

																				# Denoising diffusion loop
																				for j, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
																											__lowerCamelCase    :						Dict						      =   self.decode(
																											    encodings_and_masks=lowerCamelCase__							,    input_tokens=lowerCamelCase__							,    noise_time=t / self.scheduler.config.num_train_timesteps							,    )

																											# Compute previous output: x_t -> x_t-1
																											__lowerCamelCase    :						List[str]						      =   self.scheduler.step(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    generator=lowerCamelCase__			).prev_sample

																				__lowerCamelCase    :						Union[str, Any]						      =   self.scale_to_features(lowerCamelCase__							,    input_range=[-1.0, 1.0]			)
																				__lowerCamelCase    :						List[str]						      =   mel[:1]
																				__lowerCamelCase    :						Dict						      =   mel.cpu().float().numpy()

																				__lowerCamelCase    :						List[Any]						      =   np.concatenate([full_pred_mel, pred_mel[:1]]							,    axis=1			)

																				# call the callback, if provided
																				if callback is not None and i % callback_steps == 0:
																											callback(lowerCamelCase__							,    lowerCamelCase__			)

																				logger.info("Generated segment"							,    lowerCamelCase__			)

													if output_type == "numpy" and not is_onnx_available():
																				raise ValueError(
																				    "Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'."			)
													elif output_type == "numpy" and self.melgan is None:
																				raise ValueError(
																				    "Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'."			)

													if output_type == "numpy":
																				__lowerCamelCase    :						str						      =   self.melgan(input_features=full_pred_mel.astype(np.floataa			)			)
													else:
																				__lowerCamelCase    :						int						      =   full_pred_mel

													if not return_dict:
																				return (output,)

													return AudioPipelineOutput(audios=lowerCamelCase__			)
"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = (PNDMScheduler,)
					snake_case__				    = (("num_inference_steps", 50),)







					def 		__lowerCAmelCase						(				self   :	List[str]    ,**lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{
									    'num_train_timesteps': 1_000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									}

									config.update(**lowerCamelCase__			)
									return config







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Optional[Any]=0    ,**lowerCamelCase__   :	List[str]			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
													scheduler.set_timesteps(lowerCamelCase__			)
													# copy over dummy past residuals
													UpperCAmelCase__             =							dummy_past_residuals[:]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(lowerCamelCase__			)
																	UpperCAmelCase__             =							scheduler_class.from_pretrained(lowerCamelCase__			)
																	new_scheduler.set_timesteps(lowerCamelCase__			)
																	# copy over dummy past residuals
																	UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									pass







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	List[str]=0    ,**lowerCamelCase__   :	Tuple			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
													scheduler.set_timesteps(lowerCamelCase__			)

													# copy over dummy past residuals (must be after setting timesteps)
													UpperCAmelCase__             =							dummy_past_residuals[:]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(lowerCamelCase__			)
																	UpperCAmelCase__             =							scheduler_class.from_pretrained(lowerCamelCase__			)
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(lowerCamelCase__			)

																	# copy over dummy past residual (must be after setting timesteps)
																	UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,**lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

									UpperCAmelCase__             =							10
									UpperCAmelCase__             =							self.dummy_model()
									UpperCAmelCase__             =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__			)

									for i, t in enumerate(scheduler.prk_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									return sample







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)

									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCamelCase__    ,'set_timesteps'			):
																	scheduler.set_timesteps(lowerCamelCase__			)
													elif num_inference_steps is not None and not hasattr(lowerCamelCase__    ,'set_timesteps'			):
																	UpperCAmelCase__             =							num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

													self.assertEqual(output_a.shape    ,sample.shape			)
													self.assertEqual(output_a.shape    ,output_a.shape			)

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

													self.assertEqual(output_a.shape    ,sample.shape			)
													self.assertEqual(output_a.shape    ,output_a.shape			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for timesteps in [100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase__			)

									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(steps_offset=1			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
									scheduler.set_timesteps(10			)
									assert torch.equal(
									    scheduler.timesteps    ,torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)    ,)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,[0.0_0_2, 0.0_2]			):
													self.check_over_configs(beta_start=lowerCamelCase__    ,beta_end=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									for t in [1, 5, 10]:
													self.check_over_forward(time_step=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for t, num_inference_steps in zip([1, 5, 10]    ,[10, 50, 100]			):
													self.check_over_forward(num_inference_steps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									UpperCAmelCase__             =							27

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							0.1 * sample

													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													scheduler.set_timesteps(lowerCamelCase__			)

													# before power of 3 fix, would error on first step, so we only need to do two
													for i, t in enumerate(scheduler.prk_timesteps[:2]			):
																	UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample







					def 		__lowerCAmelCase						(				self   :	int			):
									with self.assertRaises(lowerCamelCase__			):
													UpperCAmelCase__             =							self.scheduler_classes[0]
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													scheduler.step_plms(self.dummy_sample    ,1    ,self.dummy_sample			).prev_sample







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop()
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
									assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop(prediction_type='v_prediction'			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
									assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
									assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
									assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3



