





'''simple docstring'''






class 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					List[str]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					str  )     ->						int:
    _UpperCamelCase       			=name
    _UpperCamelCase       			=value
    _UpperCamelCase       			=weight


  def __repr__(				self    :					List[str]  )     ->						Optional[int]:
    return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[Any]:
    return self.value


  def   UpperCamelCase__      (				self    :					int  )     ->						Dict:
    return self.name


  def   UpperCamelCase__      (				self    :					str  )     ->						Tuple:
    return self.weight




  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[Any]:
    return self.value / self.weight




def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )     ->   Optional[int]:


  """simple docstring"""

  _UpperCamelCase       			=[]
  for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
    menu.append(Things(name[i]  ,      value[i]  ,      weight[i]   )   )
  return menu


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )     ->   List[Any]:


  """simple docstring"""

  _UpperCamelCase       			=sorted(__SCREAMING_SNAKE_CASE  ,      key=__SCREAMING_SNAKE_CASE  ,      reverse=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[]
  _UpperCamelCase			,     _UpperCamelCase       			=0.0, 0.0
  for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]   )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)


def 					_a   ()     ->   str:


  """simple docstring"""




if __name__ == "__main__":
      import doctest

      doctest.testmod()













'''simple docstring'''






from typing import TYPE_CHECKING

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


__lowerCamelCase						: str		=   {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: List[str]		=   ['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: str		=   ['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Optional[Any]		=   [
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Union[str, Any]		=   [
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Optional[int]		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






