

'''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 							A__   (	UpperCAmelCase__  ):
  A__					=    42
  A__					=    42





  def __init__(		self				:     Any							, _a				:     Tuple							, _a				:     List[str]			)     ->   Optional[int]:





       '''simple docstring'''






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





  @torch.no_grad()
  def __call__(		self				:     str							, _a				:     List[str] = 1							, _a				:     int = 50							, _a				:     List[Any] = None							, _a				:     Any = "pil"							, _a				:     List[str] = True							, **_a				:     str							, )     ->   Union[Tuple, ImagePipelineOutput]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.unet.config.sample_size
       _SCREAMING_SNAKE_CASE							   =(batch_size, 3, img_size, img_size)

       _SCREAMING_SNAKE_CASE							   =self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _SCREAMING_SNAKE_CASE							   =randn_tensor(_SCREAMING_SNAKE_CASE							, generator=_SCREAMING_SNAKE_CASE							, device=self.device			) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

       for t in self.progress_bar(self.scheduler.timesteps			):
            # here sigma_t == t_i from the paper
            _SCREAMING_SNAKE_CASE							   =self.scheduler.schedule[t]
            _SCREAMING_SNAKE_CASE							   =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
            _SCREAMING_SNAKE_CASE							   =self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, generator=_SCREAMING_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].
            _SCREAMING_SNAKE_CASE							   =(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
            _SCREAMING_SNAKE_CASE							   =self.scheduler.step(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE			)

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

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE			)



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class 							A__   (	A__  ):
  A__					=    'MCTCTFeatureExtractor'
  A__					=    'AutoTokenizer'





  def __init__(		self				:     Optional[Any]							, _a				:     Optional[int]							, _a				:     Union[str, Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       super().__init__(_a							, _a			)
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False





  def __call__(		self				:     Dict							, *_a				:     str							, **_a				:     Dict			)     ->   Dict:





       '''simple docstring'''






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

       if "raw_speech" in kwargs:
            warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('raw_speech'			)
       else:
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('audio'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('sampling_rate'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('text'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =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 audio is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor(_a							, *_a							, sampling_rate=_a							, **_a			)
       if text is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer(_a							, **_a			)

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            _SCREAMING_SNAKE_CASE							   =encodings['input_ids']
            return inputs





  def 						A  (		self				:     Any							, *_a				:     List[str]							, **_a				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							, **_a			)





  def 						A  (		self				:     Dict							, *_a				:     Tuple							, **_a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






       if self._in_target_context_manager:
            return self.current_processor.pad(*_a							, **_a			)

       _SCREAMING_SNAKE_CASE							   =kwargs.pop('input_features'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('labels'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =args[1:]

       if input_features is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor.pad(_a							, *_a							, **_a			)
       if labels is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer.pad(_a							, **_a			)

       if labels is None:
            return input_features
       elif input_features is None:
            return labels
       else:
            _SCREAMING_SNAKE_CASE							   =labels['input_ids']
            return input_features





  def 						A  (		self				:     Tuple							, *_a				:     Dict							, **_a				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






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





  @contextmanager
  def 						A  (		self				:     Optional[Any]			)     ->   Dict:





       '''simple docstring'''






       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your audio inputs, or in a separate call.'			)
       _SCREAMING_SNAKE_CASE							   =True
       _SCREAMING_SNAKE_CASE							   =self.tokenizer
       yield
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False
