





'''simple docstring'''






from collections import deque

from .hash_table import HashTable




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					List[str]     ,     *__snake_case     :					Dict     ,     **__snake_case     :					str    )							-> Dict:
            super().__init__(*__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					List[Any]     ,     __snake_case     :					Union[str, Any]    )							-> int:
            _lowerCAmelCase					=				deque([]    ) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(__snake_case    )
            _lowerCAmelCase					=				self.values[key]




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            return (
                sum(self.charge_factor - len(__snake_case    ) for slot in self.values    )
                / self.size_table
                * self.charge_factor
            )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Any=None    )							-> Optional[Any]:
            if not (
                len(self.values[key]    ) == self.charge_factor and self.values.count(__snake_case    ) == 0
            ):
                  return key
            return super()._collision_resolution(__snake_case     ,     __snake_case    )













'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  Any    ={
    '''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:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''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

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






