"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class  _A						(       lowerCAmelCase	):
      snake_case__			:   List[str]							  =	'Wav2Vec2FeatureExtractor'
      snake_case__			:   str							  =	'AutoTokenizer'

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            super().__init__(__lowerCAmelCase		,       __lowerCAmelCase						)
            lowercase														=	self.feature_extractor
            lowercase														=	False

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            try:
                  return super().from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
            except OSError:
                  warnings.warn(
                      f'Loading a tokenizer inside {cls.__name__} from a config that does not'
                      """ include a `tokenizer_class` attribute is deprecated and will be """
                      """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
                      """ attribute to either your `config.json` or `tokenizer_config.json` """
                      """file to suppress this warning: """		,       __lowerCAmelCase		,       )

                  lowercase														=	WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
                  lowercase														=	WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)

                  return cls(feature_extractor=__lowerCAmelCase		,       tokenizer=__lowerCAmelCase						)

      def __call__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



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

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

            if text is None:
                  return inputs
            elif audio is None:
                  return encodings
            else:
                  lowercase														=	encodings["""input_ids"""]
                  return inputs

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



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

            lowercase														=	kwargs.pop("""input_features"""		,       __lowerCAmelCase						)
            lowercase														=	kwargs.pop("""labels"""		,       __lowerCAmelCase						)
            if len(__lowerCAmelCase						) > 0:
                  lowercase														=	args[0]
                  lowercase														=	args[1:]

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

            if labels is None:
                  return input_features
            elif input_features is None:
                  return labels
            else:
                  lowercase														=	labels["""input_ids"""]
                  return input_features

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



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

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



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







      @contextmanager
      def 				A__       (						self						):







            """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."""						)
            lowercase														=	True
            lowercase														=	self.tokenizer
            yield
            lowercase														=	self.feature_extractor
            lowercase														=	False




"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

            # standard deviation of the initial noise distribution
            lowercase														=	1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            lowercase														=	4

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

            if self.config.trained_betas is not None:
                  lowercase														=	torch.tensor(self.config.trained_betas		,       dtype=torch.floataa						)
            else:
                  lowercase														=	torch.sin(steps * math.pi / 2						) ** 2

            lowercase														=	(1.0 - self.betas**2) ** 0.5

            lowercase														=	(torch.atana(self.betas		,       self.alphas						) / math.pi * 2)[:-1]
            lowercase														=	timesteps.to(__lowerCAmelCase						)

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if self.num_inference_steps is None:
                  raise ValueError(
                      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

            lowercase														=	(self.timesteps == timestep).nonzero().item()
            lowercase														=	timestep_index + 1

            lowercase														=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__lowerCAmelCase						)

            if len(self.ets						) == 1:
                  lowercase														=	self.ets[-1]
            elif len(self.ets						) == 2:
                  lowercase														=	(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets						) == 3:
                  lowercase														=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                  lowercase														=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

            lowercase														=	self.alphas[prev_timestep_index]
            lowercase														=	self.betas[prev_timestep_index]

            lowercase														=	(sample - sigma * ets) / max(__lowerCAmelCase		,       1E-8						)
            lowercase														=	next_alpha * pred + ets * next_sigma

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



