

"""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


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase							:  Tuple            =						256



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					['''melgan''']



			def __init__(			self  :					int       ,						lowerCAmelCase__  :					SpectrogramNotesEncoder       ,						lowerCAmelCase__  :					SpectrogramContEncoder       ,						lowerCAmelCase__  :					TaFilmDecoder       ,						lowerCAmelCase__  :					DDPMScheduler       ,						lowerCAmelCase__  :					OnnxRuntimeModel if is_onnx_available() else Any       ,						):
									super().__init__()

									# From MELGAN
									SCREAMING_SNAKE_CASE_:			Dict  		=	math.log(1E-5)  # Matches MelGAN training.
									SCREAMING_SNAKE_CASE_:			str  		=	4.0  # Largest value for most examples
									SCREAMING_SNAKE_CASE_:			Any  		=	128

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					str=(-1.0, 1.0)       ,						lowerCAmelCase__  :					Tuple=False):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	output_range
									if clip:
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.clip(lowerCAmelCase__       ,						self.min_value       ,						self.max_value)
									# Scale to [0, 1].
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	(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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					List[Any]=(-1.0, 1.0)       ,						lowerCAmelCase__  :					Tuple=False):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	input_range
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.clip(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__) if clip else outputs
									# Scale to [0, 1].
									SCREAMING_SNAKE_CASE_:			Tuple  		=	(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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	input_tokens > 0
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.notes_encoder(
									    encoder_input_tokens=lowerCAmelCase__       ,						encoder_inputs_mask=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	self.continuous_encoder(
									    encoder_inputs=lowerCAmelCase__       ,						encoder_inputs_mask=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					str):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	noise_time
									if not torch.is_tensor(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.tensor([timesteps]       ,						dtype=torch.long       ,						device=input_tokens.device)
									elif torch.is_tensor(lowerCAmelCase__) and len(timesteps.shape) == 0:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	timesteps[None].to(input_tokens.device)

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

									SCREAMING_SNAKE_CASE_:			str  		=	self.decoder(
									    encodings_and_masks=lowerCAmelCase__       ,						decoder_input_tokens=lowerCAmelCase__       ,						decoder_noise_time=lowerCAmelCase__)
									return logits




			@torch.no_grad()
			def __call__(			self  :					Dict       ,						lowerCAmelCase__  :					List[List[int]]       ,						lowerCAmelCase__  :					Optional[torch.Generator] = None       ,						lowerCAmelCase__  :					int = 100       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					str = "numpy"       ,						lowerCAmelCase__  :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						lowerCAmelCase__  :					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__)}.")

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]       ,						dtype=np.floataa)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.zeros([1, 0, self.n_dims]       ,						np.floataa)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.ones((1, TARGET_FEATURE_LENGTH)       ,						dtype=lowerCAmelCase__       ,						device=self.device)

									for i, encoder_input_tokens in enumerate(lowerCAmelCase__):
															if i == 0:
																					SCREAMING_SNAKE_CASE_:			Any  		=	torch.from_numpy(pred_mel[:1].copy()).to(
																					    device=self.device       ,						dtype=self.decoder.dtype)
																					# The first chunk has no previous context.
																					SCREAMING_SNAKE_CASE_:			int  		=	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.
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ones

															SCREAMING_SNAKE_CASE_:			str  		=	self.scale_features(
															    lowerCAmelCase__       ,						output_range=[-1.0, 1.0]       ,						clip=lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			List[Any]  		=	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
															SCREAMING_SNAKE_CASE_:			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)):
																					SCREAMING_SNAKE_CASE_:			str  		=	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
																					SCREAMING_SNAKE_CASE_:			int  		=	self.scheduler.step(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						generator=lowerCAmelCase__).prev_sample

															SCREAMING_SNAKE_CASE_:			str  		=	self.scale_to_features(lowerCAmelCase__       ,						input_range=[-1.0, 1.0])
															SCREAMING_SNAKE_CASE_:			Tuple  		=	mel[:1]
															SCREAMING_SNAKE_CASE_:			str  		=	mel.cpu().float().numpy()

															SCREAMING_SNAKE_CASE_:			List[str]  		=	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":
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.melgan(input_features=full_pred_mel.astype(np.floataa))
									else:
															SCREAMING_SNAKE_CASE_:			int  		=	full_pred_mel

									if not return_dict:
															return (output,)

									return AudioPipelineOutput(audios=lowerCAmelCase__)









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






