





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


UpperCAmelCase		:  List[str]         =	logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase		:  Any         =	2_56





class 		_A(   SCREAMING_SNAKE_CASE_  ):


				"""simple docstring"""


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



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

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

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



				def    UpperCAmelCase_						(     self	,   _A	,   _A=(-1.0, 1.0)	,   _A=False	):
									__A      :		Dict         =							output_range
									if clip:
														__A      :		Any         =							torch.clip(__snake_case	,   self.min_value	,   self.max_value	)
									# Scale to [0, 1].
									__A      :		str         =							(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    UpperCAmelCase_						(     self	,   _A	,   _A=(-1.0, 1.0)	,   _A=False	):
									__A      :		Dict         =							input_range
									__A      :		Union[str, Any]         =							torch.clip(__snake_case	,   __snake_case	,   __snake_case	) if clip else outputs
									# Scale to [0, 1].
									__A      :		List[str]         =							(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    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	):
									__A      :		List[Any]         =							input_tokens > 0
									__A      :		List[str]         =							self.notes_encoder(
									    encoder_input_tokens=__snake_case	,   encoder_inputs_mask=__snake_case	)

									__A      :		Union[str, Any]         =							self.continuous_encoder(
									    encoder_inputs=__snake_case	,   encoder_inputs_mask=__snake_case	)

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



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	):
									__A      :		int         =							noise_time
									if not torch.is_tensor(__snake_case	):
														__A      :		Optional[Any]         =							torch.tensor([timesteps]	,   dtype=torch.long	,   device=input_tokens.device	)
									elif torch.is_tensor(__snake_case	) and len(timesteps.shape	) == 0:
														__A      :		int         =							timesteps[None].to(input_tokens.device	)

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

									__A      :		Union[str, Any]         =							self.decoder(
									    encodings_and_masks=__snake_case	,   decoder_input_tokens=__snake_case	,   decoder_noise_time=__snake_case	)
									return logits





				@torch.no_grad()
				def __call__(     self	,   _A	,   _A = None	,   _A = 100	,   _A = True	,   _A = "numpy"	,   _A = None	,   _A = 1	,   ):
									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(__snake_case	,   __snake_case	) or callback_steps <= 0)
									):
														raise ValueError(
														    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
														    F""" {type(__snake_case	)}."""	)

									__A      :		Any         =							np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]	,   dtype=np.floataa	)
									__A      :		Optional[int]         =							np.zeros([1, 0, self.n_dims]	,   np.floataa	)
									__A      :		Tuple         =							torch.ones((1, TARGET_FEATURE_LENGTH)	,   dtype=__snake_case	,   device=self.device	)

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

														__A      :		Union[str, Any]         =							self.scale_features(
														    __snake_case	,   output_range=[-1.0, 1.0]	,   clip=__snake_case	)

														__A      :		Optional[int]         =							self.encode(
														    input_tokens=torch.IntTensor([encoder_input_tokens]	).to(device=self.device	)	,   continuous_inputs=__snake_case	,   continuous_mask=__snake_case	,   )

														# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
														__A      :		Dict         =							randn_tensor(
														    shape=encoder_continuous_inputs.shape	,   generator=__snake_case	,   device=self.device	,   dtype=self.decoder.dtype	,   )

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

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

																			# Compute previous output: x_t -> x_t-1
																			__A      :		Union[str, Any]         =							self.scheduler.step(__snake_case	,   __snake_case	,   __snake_case	,   generator=__snake_case	).prev_sample

														__A      :		List[Any]         =							self.scale_to_features(__snake_case	,   input_range=[-1.0, 1.0]	)
														__A      :		Optional[Any]         =							mel[:1]
														__A      :		Any         =							mel.cpu().float().numpy()

														__A      :		Dict         =							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(__snake_case	,   __snake_case	)

														logger.info('Generated segment'	,   __snake_case	)

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

									if not return_dict:
														return (output,)

									return AudioPipelineOutput(audios=__snake_case	)












"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int = 1_0_0  )    ->							int:
						__magic_name__:	str							=       0
						__magic_name__:	Any							=       0
						for i in range(1      ,   n + 1  ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


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



