



"""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


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

_lowercase				:				Any   		=		2_5_6

class  __SCREAMING_SNAKE_CASE			(   lowercase_     ):
					_a         =							['''melgan''']



					def __init__( self							:						Dict,						lowerCamelCase							:						int,						lowerCamelCase							:						int,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Any,						lowerCamelCase							:						Tuple,						)->      List[Any]:
								super().__init__()

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

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



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

								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=self.continuous_encoder(
								    encoder_inputs=lowerCamelCase,						encoder_inputs_mask=lowerCamelCase  )

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



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						Dict,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						int  )->      Optional[int]:
								lowerCamelCase__	:	str				=noise_time
								if not torch.is_tensor(lowerCamelCase  ):
											lowerCamelCase__	:	Tuple				=torch.tensor([timesteps],						dtype=torch.long,						device=input_tokens.device  )
								elif torch.is_tensor(lowerCamelCase  ) and len(timesteps.shape  ) == 0:
											lowerCamelCase__	:	Optional[int]				=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__	:	str				=self.decoder(
								    encodings_and_masks=lowerCamelCase,						decoder_input_tokens=lowerCamelCase,						decoder_noise_time=lowerCamelCase  )
								return logits




					@torch.no_grad()
					def __call__( self							:						Optional[int],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str = None,						lowerCamelCase							:						str = 100,						lowerCamelCase							:						List[Any] = True,						lowerCamelCase							:						Optional[Any] = "numpy",						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						Dict = 1,						)->      List[str]:
								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[str]				=np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],						dtype=np.floataa  )
								lowerCamelCase__	:	Optional[int]				=np.zeros([1, 0, self.n_dims],						np.floataa  )
								lowerCamelCase__	:	List[str]				=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__	:	Dict				=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__	:	List[str]				=ones

											lowerCamelCase__	:	Dict				=self.scale_features(
											    lowerCamelCase,						output_range=[-1.0, 1.0],						clip=lowerCamelCase  )

											lowerCamelCase__	:	List[str]				=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__	:	List[Any]				=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__	:	Any				=self.scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						generator=lowerCamelCase  ).prev_sample

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

											lowerCamelCase__	:	Tuple				=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__	:	Union[str, Any]				=self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
								else:
											lowerCamelCase__	:	Optional[int]				=full_pred_mel

								if not return_dict:
											return (output,)

								return AudioPipelineOutput(audios=lowerCamelCase  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			return " ".join(
			    ''''''.join(word[::-1]			) if len(__lowerCamelCase			) > 4 else word for word in sentence.split()			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words("Hey wollef sroirraw"))




