






"""simple docstring"""



from typing import List, Optional

import numpy as np

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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'EncodecFeatureExtractor'
							snake_case_     =							('T5Tokenizer', 'T5TokenizerFast')


							def __init__(			self      :						Dict			,    snake_case      :						Optional[int]			,    snake_case      :						int							):
													'''simple docstring'''





													super().__init__(snake_case			,    snake_case							)
													A__						:  Any		  =  self.feature_extractor
													A__						:  List[Any]		  =  False


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Optional[int]=None			,    snake_case      :						Any=None			,    snake_case      :						Tuple=True							):
													'''simple docstring'''





													return self.tokenizer.get_decoder_prompt_ids(task=snake_case			,    language=snake_case			,    no_timestamps=snake_case							)


							def __call__(			self      :						Optional[int]			,    *snake_case      :						int			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if self._in_target_context_manager:
																			return self.current_processor(*snake_case			,    **snake_case							)

													A__						:  Dict		  =  kwargs.pop("""audio"""			,    snake_case							)
													A__						:  Tuple		  =  kwargs.pop("""sampling_rate"""			,    snake_case							)
													A__						:  int		  =  kwargs.pop("""text"""			,    snake_case							)
													if len(snake_case							) > 0:
																			A__						:  Optional[Any]		  =  args[0]
																			A__						:  str		  =  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__						:  Union[str, Any]		  =  self.tokenizer(snake_case			,    **snake_case							)

													if audio is not None:
																			A__						:  List[Any]		  =  self.feature_extractor(snake_case			,    *snake_case			,    sampling_rate=snake_case			,    **snake_case							)

													if audio is None:
																			return inputs

													elif text is None:
																			return audio_inputs

													else:
																			A__						:  Optional[int]		  =  audio_inputs["""input_values"""]
																			if "padding_mask" in audio_inputs:
																									A__						:  List[str]		  =  audio_inputs["""padding_mask"""]
																			return inputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    *snake_case      :						List[str]			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  kwargs.pop("""audio"""			,    snake_case							)
													A__						:  Optional[Any]		  =  kwargs.pop("""padding_mask"""			,    snake_case							)

													if len(snake_case							) > 0:
																			A__						:  List[str]		  =  args[0]
																			A__						:  Tuple		  =  args[1:]

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


							def 						_UpperCamelCase       (			self      :						Any			,    *snake_case      :						List[str]			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return self.tokenizer.decode(*snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Any			,    snake_case      :						Optional = None							):
													'''simple docstring'''





													A__						:  Tuple		  =  to_numpy(snake_case							)
													A__  ,  A__  ,  A__						:  Any		  =  audio_values.shape

													if padding_mask is None:
																			return list(snake_case							)

													A__						:  List[Any]		  =  to_numpy(snake_case							)

													# 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__						:  Union[str, Any]		  =  seq_len - padding_mask.shape[-1]
													A__						:  int		  =  1 - self.feature_extractor.padding_value
													A__						:  str		  =  np.pad(snake_case			,    ((0, 0), (0, difference))			,    """constant"""			,    constant_values=snake_case							)

													A__						:  List[str]		  =  audio_values.tolist()
													for i in range(snake_case							):
																			A__						:  Any		  =  np.asarray(audio_values[i]							)[
																			    padding_mask[i][None, :] != self.feature_extractor.padding_value
																			]
																			A__						:  Tuple		  =  sliced_audio.reshape(snake_case			,    -1							)

													return audio_values














"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






