






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

__UpperCAmelCase								=						256



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  ['''melgan''']



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							None:
							super().__init__()

							# From MELGAN
							UpperCAmelCase_    :      Optional[Any]            =						math.log(1E-5   )  # Matches MelGAN training.
							UpperCAmelCase_    :      Any            =						4.0  # Largest value for most examples
							UpperCAmelCase_    :      Optional[int]            =						1_2_8

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



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

							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						self.continuous_encoder(
							    encoder_inputs=_UpperCamelCase				,		encoder_inputs_mask=_UpperCamelCase   )

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Tuple            =						noise_time
							if not torch.is_tensor(_UpperCamelCase   ):
												UpperCAmelCase_    :      int            =						torch.tensor([timesteps]				,		dtype=torch.long				,		device=input_tokens.device   )
							elif torch.is_tensor(_UpperCamelCase   ) and len(timesteps.shape   ) == 0:
												UpperCAmelCase_    :      List[str]            =						timesteps[None].to(input_tokens.device   )

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

							UpperCAmelCase_    :      List[str]            =						self.decoder(
							    encodings_and_masks=_UpperCamelCase				,		decoder_input_tokens=_UpperCamelCase				,		decoder_noise_time=_UpperCamelCase   )
							return logits



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

							UpperCAmelCase_    :      List[Any]            =						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,		dtype=np.floataa   )
							UpperCAmelCase_    :      Union[str, Any]            =						np.zeros([1, 0, self.n_dims]				,		np.floataa   )
							UpperCAmelCase_    :      str            =						torch.ones((1, TARGET_FEATURE_LENGTH)				,		dtype=_UpperCamelCase				,		device=self.device   )

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

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

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

												# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
												UpperCAmelCase_    :      List[Any]            =						randn_tensor(
												    shape=encoder_continuous_inputs.shape				,		generator=_UpperCamelCase				,		device=self.device				,		dtype=self.decoder.dtype				,		)

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

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

																	# Compute previous output: x_t -> x_t-1
																	UpperCAmelCase_    :      List[str]            =						self.scheduler.step(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase   ).prev_sample

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

												UpperCAmelCase_    :      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(_UpperCamelCase				,		_UpperCamelCase   )

												logger.info('Generated segment'				,		_UpperCamelCase   )

							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_    :      List[Any]            =						self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
							else:
												UpperCAmelCase_    :      str            =						full_pred_mel

							if not return_dict:
												return (output,)

							return AudioPipelineOutput(audios=_UpperCamelCase   )














def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






