





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							(		_lowerCamelCase     ):
      a						       =							42
      a						       =							42
      a						       =							None






class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase     ):
      a						       =							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_     :				List[str]			      =	sigma_max

         # setable values
         A_     :				int			      =	None
         A_     :				np.IntTensor			      =	None
         A_     :				torch.FloatTensor			      =	None  # sigma(t_i)
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         return sample
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				int			      =	num_inference_steps
         A_     :				Union[str, Any]			      =	np.arange(0      ,       self.num_inference_steps       )[::-1].copy()
         A_     :				Union[str, Any]			      =	torch.from_numpy(a__       ).to(a__       )
         A_     :				Optional[int]			      =	[
             (
                 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_     :				Dict			      =	torch.tensor(a__      ,       dtype=torch.floataa      ,       device=a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = None       ):
         if self.config.s_min <= sigma <= self.config.s_max:
            A_     :				Any			      =	min(self.config.s_churn / self.num_inference_steps      ,       2**0.5 - 1       )
         else:
            A_     :				Union[str, Any]			      =	0

         # sample eps ~ N(0, S_noise^2 * I)
         A_     :				str			      =	self.config.s_noise * randn_tensor(sample.shape      ,       generator=a__       ).to(sample.device       )
         A_     :				Dict			      =	sigma + gamma * sigma
         A_     :				Any			      =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__ = True      ,       ):
         A_     :				Tuple			      =	sample_hat + sigma_hat * model_output
         A_     :				Tuple			      =	(sample_hat - pred_original_sample) / sigma_hat
         A_     :				Tuple			      =	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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__ = True      ,       ):
         A_     :				int			      =	sample_prev + sigma_prev * model_output
         A_     :				int			      =	(sample_prev - pred_original_sample) / sigma_prev
         A_     :				Dict			      =	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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
         raise NotImplementedError()













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SpeechT5ForSpeechToText""",
          """SpeechT5ForSpeechToSpeech""",
          """SpeechT5ForTextToSpeech""",
          """SpeechT5Model""",
          """SpeechT5PreTrainedModel""",
          """SpeechT5HifiGan""",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






