
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "EncodecFeatureExtractor"
	snake_case_					    =     ("T5Tokenizer", "T5TokenizerFast")






	def __init__(       self					: Tuple							,A					: Any							,A					: List[str] ):
					super().__init__(A							,A )
					__A   			= self.feature_extractor
					__A   			= False






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: Tuple=None							,A					: List[str]=None							,A					: Union[str, Any]=True ):
					return self.tokenizer.get_decoder_prompt_ids(task=A							,language=A							,no_timestamps=A )






	def __call__(       self					: int							,*A					: Any							,**A					: Any ):
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*A							,**A )

					__A   			= kwargs.pop("audio"							,A )
					__A   			= kwargs.pop("sampling_rate"							,A )
					__A   			= kwargs.pop("text"							,A )
					if len(A ) > 0:
									__A   			= args[0]
									__A   			= args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process." )

					if text is not None:
									__A   			= self.tokenizer(A							,**A )

					if audio is not None:
									__A   			= self.feature_extractor(A							,*A							,sampling_rate=A							,**A )

					if audio is None:
									return inputs

					elif text is None:
									return audio_inputs

					else:
									__A   			= audio_inputs["input_values"]
									if "padding_mask" in audio_inputs:
													__A   			= audio_inputs["padding_mask"]
									return inputs






	def 				UpperCamelCase_  (       self					: List[Any]							,*A					: Optional[Any]							,**A					: Optional[int] ):
					__A   			= kwargs.pop("audio"							,A )
					__A   			= kwargs.pop("padding_mask"							,A )

					if len(A ) > 0:
									__A   			= args[0]
									__A   			= args[1:]

					if audio_values is not None:
									return self._decode_audio(A							,padding_mask=A )
					else:
									return self.tokenizer.batch_decode(*A							,**A )






	def 				UpperCamelCase_  (       self					: List[Any]							,*A					: Dict							,**A					: Tuple ):
					return self.tokenizer.decode(*A							,**A )







	def 				UpperCamelCase_  (       self					: int							,A					: Union[str, Any]							,A					: Optional = None ):
					__A   			= to_numpy(A )
					__A			,							__A			,							__A   			= audio_values.shape

					if padding_mask is None:
									return list(A )

					__A   			= to_numpy(A )

					# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
					# token (so that the generated audio values are **not** treated as padded tokens)
					__A   			= seq_len - padding_mask.shape[-1]
					__A   			= 1 - self.feature_extractor.padding_value
					__A   			= np.pad(A							,((0, 0), (0, difference))							,"constant"							,constant_values=A )

					__A   			= audio_values.tolist()
					for i in range(A ):
									__A   			= np.asarray(audio_values[i] )[
									    padding_mask[i][None, :] != self.feature_extractor.padding_value
									]
									__A   			= sliced_audio.reshape(A							,-1 )

					return audio_values


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
