
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 :			Optional[Any]							,A :			Tuple							,A :			Dict			):
										super().__init__(A							,A			)
										__A             =			self.feature_extractor
										__A             =			False






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






						def __call__(	self :			Optional[Any]							,*A :			Tuple							,**A :			int			):
										# 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 :			Optional[int]							,*A :			List[str]							,**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 :			List[Any]			):
										return self.tokenizer.decode(*A							,**A			)







						def     UpperCamelCase_  (	self :			Dict							,A :			List[str]							,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 typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

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

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
