





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


   A_     :				Optional[int]			      =	int(_lowerCAmelCase     )

   # Initialize Result
   A_     :				Tuple			      =	[]

   # Traverse through all denomination
   for denomination in reversed(_lowerCAmelCase     ):
      # Find denominations
      while int(_lowerCAmelCase     ) >= int(_lowerCAmelCase     ):
         total_value -= int(_lowerCAmelCase     )
         answer.append(_lowerCAmelCase     )  # Append the "answers" array

   return answer


# Driver Code
if __name__ == "__main__":
      _lowerCAmelCase 				=     []
      _lowerCAmelCase 				=     """0"""

      if (
          input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
          == "y"
      ):
            _lowerCAmelCase 				=     int(input("""Enter the number of denominations you want to add: """).strip())

            for i in range(0, n):
                  denominations.append(int(input(f'''Denomination {i}: ''').strip()))
            _lowerCAmelCase 				=     input("""Enter the change you want to make in Indian Currency: """).strip()
      else:
            # All denominations of Indian Currency if user does not enter
            _lowerCAmelCase 				=     [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
            _lowerCAmelCase 				=     input("""Enter the change you want to make: """).strip()

      if int(value) == 0 or int(value) < 0:
            print("""The total value cannot be zero or negative.""")

      else:
            print(f'''Following is minimal change for {value}: ''')
            _lowerCAmelCase 				=     find_minimum_change(denominations, value)
            # Print result
            for i in range(len(answer)):
                  print(answer[i], end=""" """)













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__)






