



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


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

__snake_case       :	Tuple            =2_5_6



class  lowerCamelCase__    (							A_):




		'''simple docstring'''



		snake_case_			=['''melgan''']



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,)  ->    Any:

						"""simple docstring"""






						super().__init__()

						# From MELGAN
						lowerCAmelCase__  :       Any				=  math.log(1e-5				)  # Matches MelGAN training.
						lowerCAmelCase__  :       Optional[int]				=  4.0  # Largest value for most examples
						lowerCAmelCase__  :       Dict				=  1_28

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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=(-1.0, 1.0)	,__lowerCamelCase=False				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  output_range
						if clip:
										lowerCAmelCase__  :       Dict				=  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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=(-1.0, 1.0)	,__lowerCamelCase=False				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  input_range
						lowerCAmelCase__  :       int				=  torch.clip(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				) if clip else outputs
						# Scale to [0, 1].
						lowerCAmelCase__  :       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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  input_tokens > 0
						lowerCAmelCase__  :       List[Any]				=  self.notes_encoder(
						    encoder_input_tokens=__lowerCamelCase	,encoder_inputs_mask=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  self.continuous_encoder(
						    encoder_inputs=__lowerCamelCase	,encoder_inputs_mask=__lowerCamelCase				)

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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  noise_time
						if not torch.is_tensor(__lowerCamelCase				):
										lowerCAmelCase__  :       List[Any]				=  torch.tensor([timesteps]	,dtype=torch.long	,device=input_tokens.device				)
						elif torch.is_tensor(__lowerCamelCase				) and len(timesteps.shape				) == 0:
										lowerCAmelCase__  :       List[str]				=  timesteps[None].to(input_tokens.device				)

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

						lowerCAmelCase__  :       Optional[int]				=  self.decoder(
						    encodings_and_masks=__lowerCamelCase	,decoder_input_tokens=__lowerCamelCase	,decoder_noise_time=__lowerCamelCase				)
						return logits



		@torch.no_grad()
		def __call__(self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = 1_00	,__lowerCamelCase = True	,__lowerCamelCase = "numpy"	,__lowerCamelCase = None	,__lowerCamelCase = 1	,)  ->    Tuple:

						"""simple docstring"""






						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__  :       Optional[Any]				=  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]	,dtype=np.floataa				)
						lowerCAmelCase__  :       List[Any]				=  np.zeros([1, 0, self.n_dims]	,np.floataa				)
						lowerCAmelCase__  :       List[Any]				=  torch.ones((1, TARGET_FEATURE_LENGTH)	,dtype=__lowerCamelCase	,device=self.device				)

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

										lowerCAmelCase__  :       List[Any]				=  self.scale_features(
										    __lowerCamelCase	,output_range=[-1.0, 1.0]	,clip=__lowerCamelCase				)

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

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

										lowerCAmelCase__  :       Optional[int]				=  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__  :       Optional[int]				=  self.melgan(input_features=full_pred_mel.astype(np.floataa				)				)
						else:
										lowerCAmelCase__  :       Any				=  full_pred_mel

						if not return_dict:
										return (output,)

						return AudioPipelineOutput(audios=__lowerCamelCase				)






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

__snake_case       :	Optional[int]            =(3, 9, -1_1, 0, 7, 5, 1, -1)
__snake_case       :	str            =(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    None:

						"""simple docstring"""






						lowerCAmelCase__  :       Node | None				=  None
						for i in sorted(__lowerCamelCase	,reverse=__lowerCamelCase				):
										lowerCAmelCase__  :       Dict				=  Node(__lowerCamelCase	,self.head				)



		def __iter__(self				)  ->    Iterator[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.head
						while node:
										yield node.data
										lowerCAmelCase__  :       Dict				=  node.next_node



		def __len__(self				)  ->    int:

						"""simple docstring"""






						return sum(1 for _ in self				)



		def __str__(self				)  ->    str:

						"""simple docstring"""






						return " -> ".join([str(__lowerCamelCase				) for node in self]				)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	SortedLinkedList ,lowerCamelCase_	:	SortedLinkedList):





				'''simple docstring'''

				return SortedLinkedList(list(lowerCamelCase_) + list(lowerCamelCase_))


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__snake_case       :	Any            =SortedLinkedList
							print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

