


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							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      42
     _lowerCamelCase   =      None







class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      2


     @register_to_config
     def __init__(	self  ,       __A = 0.02  ,       __A = 100  ,       __A = 1.007  ,       __A = 80  ,       __A = 0.05  ,       __A = 50  ,       ):
        # standard deviation of the initial noise distribution
        __a					=  sigma_max

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        return sample


     def 	snake_case_  (	self  ,       __A  ,       __A = 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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = 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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = 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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = 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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        raise NotImplementedError()





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Optional[Any]    		=	'MCTCTFeatureExtractor'
	UpperCamelCase_  :					List[Any]    		=	'AutoTokenizer'







	def __init__(     self      :Tuple   ,     lowercase      :List[str]   ,     lowercase      :Dict			)		->      Tuple:







				"""simple docstring"""






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







	def __call__(     self      :Union[str, Any]   ,     *lowercase      :Union[str, Any]   ,     **lowercase      :str			)		->      int:







				"""simple docstring"""






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

				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'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''sampling_rate'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''text'''   ,     lowercase			)
				if len(lowercase			) > 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(lowercase   ,     *lowercase   ,     sampling_rate=lowercase   ,     **lowercase			)
				if text is not None:
							SCREAMING_SNAKE_CASE     						= self.tokenizer(lowercase   ,     **lowercase			)

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







	def  snake_case__     (     self      :Dict   ,     *lowercase      :Union[str, Any]   ,     **lowercase      :List[str]			)		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[Any]   ,     *lowercase      :List[Any]   ,     **lowercase      :List[str]			)		->      int:







				"""simple docstring"""






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

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

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

				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  snake_case__     (     self      :Dict   ,     *lowercase      :List[str]   ,     **lowercase      :Union[str, Any]			)		->      List[Any]:







				"""simple docstring"""






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







	@contextmanager
	def  snake_case__     (     self      :str			)		->      Union[str, Any]:







				"""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