





def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   if len(_lowerCAmelCase     ) != len(_lowerCAmelCase     ):
      raise ValueError("""The length of profit and weight must be same."""     )
   if max_weight <= 0:
      raise ValueError("""max_weight must greater than zero."""     )
   if any(p < 0 for p in profit     ):
      raise ValueError("""Profit can not be negative."""     )
   if any(w < 0 for w in weight     ):
      raise ValueError("""Weight can not be negative."""     )

   # List created to store profit gained for the 1kg in case of each weight
   # respectively.  Calculate and append profit/weight for each element.
   A_     :				Union[str, Any]			      =	[p / w for p, w in zip(_lowerCAmelCase  ,_lowerCAmelCase     )]

   # Creating a copy of the list and sorting profit/weight in ascending order
   A_     :				Tuple			      =	sorted(_lowerCAmelCase     )

   # declaring useful variables
   A_     :				int			      =	len(_lowerCAmelCase     )
   A_     :				List[Any]			      =	0
   A_     :				int			      =	0
   A_     :				Tuple			      =	0

   # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
   while limit <= max_weight and i < length:
      # flag value for encountered greatest element in sorted_profit_by_weight
      A_     :				str			      =	sorted_profit_by_weight[length - i - 1]
      A_     :				Any			      =	profit_by_weight.index(_lowerCAmelCase     )
      A_     :				str			      =	-1

      # check if the weight encountered is less than the total weight
      # encountered before.
      if max_weight - limit >= weight[index]:
         limit += weight[index]
         # Adding profit gained for the given weight 1 ===
         # weight[index]/weight[index]
         gain += 1 * profit[index]
      else:
         # Since the weight encountered is greater than limit, therefore take the
         # required number of remaining kgs and calculate profit for it.
         # weight remaining / weight[index]
         gain += (max_weight - limit) / weight[index] * profit[index]
         break
      i += 1
   return gain


if __name__ == "__main__":
      print(
          """Input profits, weights, and then max_weight (all positive ints) separated by """
          """spaces."""
      )

      _lowerCAmelCase 				=     [int(x) for x in input("""Input profits separated by spaces: """).split()]
      _lowerCAmelCase 				=     [int(x) for x in input("""Input weights separated by spaces: """).split()]
      _lowerCAmelCase 				=     int(input("""Max weight allowed: """))

      # Function Call
      calc_profit(profit, weight, max_weight)













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__)






