



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

lowercase  							=					2_56




class 			lowercase__			(      lowercase_    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					['''melgan''']






							def __init__(  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       )			->							List[str]:
									super().__init__()

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

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=(-1.0, 1.0)	,       snake_case=False						)			->							Optional[Any]:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		output_range
									if clip:
											_UpperCAmelCase							 =		torch.clip(snake_case	,       self.min_value	,       self.max_value						)
									# Scale to [0, 1].
									_UpperCAmelCase							 =		(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	,       snake_case	,       snake_case=(-1.0, 1.0)	,       snake_case=False						)			->							Union[str, Any]:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		input_range
									_UpperCAmelCase							 =		torch.clip(snake_case	,       snake_case	,       snake_case						) if clip else outputs
									# Scale to [0, 1].
									_UpperCAmelCase							 =		(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	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		input_tokens > 0
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.notes_encoder(
									    encoder_input_tokens=snake_case	,       encoder_inputs_mask=snake_case						)

									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.continuous_encoder(
									    encoder_inputs=snake_case	,       encoder_inputs_mask=snake_case						)

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		noise_time
									if not torch.is_tensor(snake_case						):
											_UpperCAmelCase							 =		torch.tensor([timesteps]	,       dtype=torch.long	,       device=input_tokens.device						)
									elif torch.is_tensor(snake_case						) and len(timesteps.shape						) == 0:
											_UpperCAmelCase							 =		timesteps[None].to(input_tokens.device						)

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

									_UpperCAmelCase							 =		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	,       snake_case	,       snake_case = None	,       snake_case = 100	,       snake_case = True	,       snake_case = "numpy"	,       snake_case = None	,       snake_case = 1	,       )			->							int:
									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						)}.'						)

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

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

											_UpperCAmelCase							 =		self.scale_features(
											    snake_case	,       output_range=[-1.0, 1.0]	,       clip=snake_case						)

											_UpperCAmelCase							 =		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
											_UpperCAmelCase							 =		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						)						):
													_UpperCAmelCase							 =		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
													_UpperCAmelCase							 =		self.scheduler.step(snake_case	,       snake_case	,       snake_case	,       generator=snake_case						).prev_sample

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

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

									if not return_dict:
											return (output,)

									return AudioPipelineOutput(audios=snake_case						)







"""simple docstring"""





import os




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		os.path.join(os.path.dirname(A       )     ,				'num.txt'       )
		with open(A       ) as file_hand:
				return str(sum(int(A       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
					print(solution())


