
def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						List[Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[0 for i in range(r + 1       )]
			# nc0 = 1
			lowerCAmelCase_      			=			1
			for i in range(1					,							n + 1       ):
						# to compute current row from previous row.
						lowerCAmelCase_      			=			min(__lowerCAmelCase					,							__lowerCAmelCase       )
						while j > 0:
									c[j] += c[j - 1]
									j -= 1
			return c[r]
print(binomial_coefficient(n=10, r=5))





from ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )

							if audio is not None and text is not None:
										raise ValueError(
										    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
							if audio_target is not None and text_target is not None:
										raise ValueError(
										    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
							if audio is None and audio_target is None and text is None and text_target is None:
										raise ValueError(
										    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

							if audio is not None:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if audio_target is not None:
										lowerCAmelCase_      			=			self.feature_extractor(audio_target=_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_values"]
							elif text_target is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_ids"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )

							if input_values is not None and input_ids is not None:
										raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
							if input_values is None and input_ids is None and labels is None:
										raise ValueError(
										    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

							if input_values is not None:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							elif input_ids is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if labels is not None:
										if "input_ids" in labels or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and "input_ids" in labels[0]):
													lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			targets["input_ids"]
										else:
													lowerCAmelCase_      			=			self.feature_extractor.feature_size
													lowerCAmelCase_      			=			self.feature_extractor.num_mel_bins
													lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			feature_size_hack
													lowerCAmelCase_      			=			targets["input_values"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



