
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


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

lowercase__	:Dict										=					256







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : str       =['''melgan''']




							def __init__(       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,):
								super().__init__()

								# From MELGAN
								lowercase					=		math.log(1E-5)  # Matches MelGAN training.
								lowercase					=		4.0  # Largest value for most examples
								lowercase					=		1_2_8

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




							def 				A__   (       self     ,A__     ,A__=(-1.0, 1.0)     ,A__=False):
								lowercase			,		lowercase					=		output_range
								if clip:
									lowercase					=		torch.clip(A__     ,self.min_value     ,self.max_value)
								# Scale to [0, 1].
								lowercase					=		(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 				A__   (       self     ,A__     ,A__=(-1.0, 1.0)     ,A__=False):
								lowercase			,		lowercase					=		input_range
								lowercase					=		torch.clip(A__     ,A__     ,A__) if clip else outputs
								# Scale to [0, 1].
								lowercase					=		(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 				A__   (       self     ,A__     ,A__     ,A__):
								lowercase					=		input_tokens > 0
								lowercase			,		lowercase					=		self.notes_encoder(
								    encoder_input_tokens=A__     ,encoder_inputs_mask=A__)

								lowercase			,		lowercase					=		self.continuous_encoder(
								    encoder_inputs=A__     ,encoder_inputs_mask=A__)

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




							def 				A__   (       self     ,A__     ,A__     ,A__):
								lowercase					=		noise_time
								if not torch.is_tensor(A__):
									lowercase					=		torch.tensor([timesteps]     ,dtype=torch.long     ,device=input_tokens.device)
								elif torch.is_tensor(A__) and len(timesteps.shape) == 0:
									lowercase					=		timesteps[None].to(input_tokens.device)

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

								lowercase					=		self.decoder(
								    encodings_and_masks=A__     ,decoder_input_tokens=A__     ,decoder_noise_time=A__)
								return logits





							@torch.no_grad()
							def __call__(       self     ,A__     ,A__ = None     ,A__ = 1_0_0     ,A__ = True     ,A__ = "numpy"     ,A__ = None     ,A__ = 1     ,):
								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(A__     ,A__) or callback_steps <= 0)
								):
									raise ValueError(
									    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
									    f' {type(A__)}.')

								lowercase					=		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]     ,dtype=np.floataa)
								lowercase					=		np.zeros([1, 0, self.n_dims]     ,np.floataa)
								lowercase					=		torch.ones((1, TARGET_FEATURE_LENGTH)     ,dtype=A__     ,device=self.device)

								for i, encoder_input_tokens in enumerate(A__):
									if i == 0:
										lowercase					=		torch.from_numpy(pred_mel[:1].copy()).to(
										    device=self.device     ,dtype=self.decoder.dtype)
										# The first chunk has no previous context.
										lowercase					=		torch.zeros((1, TARGET_FEATURE_LENGTH)     ,dtype=A__     ,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.
										lowercase					=		ones

									lowercase					=		self.scale_features(
									    A__     ,output_range=[-1.0, 1.0]     ,clip=A__)

									lowercase					=		self.encode(
									    input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)     ,continuous_inputs=A__     ,continuous_mask=A__     ,)

									# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
									lowercase					=		randn_tensor(
									    shape=encoder_continuous_inputs.shape     ,generator=A__     ,device=self.device     ,dtype=self.decoder.dtype     ,)

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

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

										# Compute previous output: x_t -> x_t-1
										lowercase					=		self.scheduler.step(A__     ,A__     ,A__     ,generator=A__).prev_sample

									lowercase					=		self.scale_to_features(A__     ,input_range=[-1.0, 1.0])
									lowercase					=		mel[:1]
									lowercase					=		mel.cpu().float().numpy()

									lowercase					=		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(A__     ,A__)

									logger.info('''Generated segment'''     ,A__)

								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":
									lowercase					=		self.melgan(input_features=full_pred_mel.astype(np.floataa))
								else:
									lowercase					=		full_pred_mel

								if not return_dict:
									return (output,)

								return AudioPipelineOutput(audios=A__)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(UniPCMultistepScheduler,)
							lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)
																	# copy over dummy past residuals
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

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

																	lowercase			,		lowercase					=		sample, sample
																	for t in range(A__     ,time_step + scheduler.config.solver_order + 1):
																						lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																						lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

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

																						# copy over dummy past residual (must be after setting timesteps)
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self     ,A__=None     ,**A__):
												if scheduler is None:
																	lowercase					=		self.scheduler_classes[0]
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(**A__)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

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

																	# copy over dummy past residuals (must be done after set_timesteps)
																	lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	lowercase					=		scheduler.timesteps[5]
																	lowercase					=		scheduler.timesteps[6]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self):
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												lowercase					=		UniPCMultistepScheduler(**self.get_scheduler_config())
												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3

												lowercase					=		DPMSolverSinglestepScheduler.from_config(scheduler.config)
												lowercase					=		DEISMultistepScheduler.from_config(scheduler.config)
												lowercase					=		DPMSolverMultistepScheduler.from_config(scheduler.config)
												lowercase					=		UniPCMultistepScheduler.from_config(scheduler.config)

												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												self.check_over_configs(thresholding=A__)
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,solver_order=A__     ,solver_type=A__     ,)




							def 				A__   (       self):
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=A__)




							def 				A__   (       self):
												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											lowercase					=		self.full_loop(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											assert not torch.isnan(A__).any(), "Samples have nan numbers"




							def 				A__   (       self):
												self.check_over_configs(lower_order_final=A__)
												self.check_over_configs(lower_order_final=A__)




							def 				A__   (       self):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=A__     ,time_step=0)




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.1014) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(thresholding=A__     ,dynamic_thresholding_ratio=0)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter.half()
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												assert sample.dtype == torch.floataa





							def 				A__   (       self     ,**A__):
												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps)
																	assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps




