
def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   List[str]  )   ->			int:






						'''simple docstring'''

						def count_of_possible_combinations(_UpperCAmelCase  :   Optional[Any]  ) -> int:
												if target < 0:
																		return 0
												if target == 0:
																		return 1
												return sum(count_of_possible_combinations(target - item  ) for item in array  )

						return count_of_possible_combinations(SCREAMING_SNAKE_CASE_  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   List[Any] ,			_UpperCAmelCase  :   Any  )   ->			int:






						'''simple docstring'''

						def count_of_possible_combinations_with_dp_array(
						    _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   List[Any]  ) -> int:
												if target < 0:
																		return 0
												if target == 0:
																		return 1
												if dp_array[target] != -1:
																		return dp_array[target]
												_lowercase       :	Dict           =   sum(
												    count_of_possible_combinations_with_dp_array(target - item ,			SCREAMING_SNAKE_CASE_  )
												    for item in array  )
												_lowercase       :	Optional[Any]           =   answer
												return answer

						_lowercase       :	int           =   [-1] * (target + 1)
						return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   Any  )   ->			int:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   [0] * (target + 1)
						_lowercase       :	List[Any]           =   1

						for i in range(1 ,			target + 1  ):
												for j in range(SCREAMING_SNAKE_CASE_  ):
																		if i - array[j] >= 0:
																								dp_array[i] += dp_array[i - array[j]]

						return dp_array[target]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							UpperCamelCase_						:     Union[str, Any]	       =							3
							UpperCamelCase_						:     Optional[Any]	       =							5
							UpperCamelCase_						:     Optional[int]	       =							[1, 2, 5]
							print(combination_sum_iv(n, array, target))







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:Union[str, 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:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		: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

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
