





_a						=   [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_a						=   [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_a						=   {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   str:







 '''simple docstring'''







 assert len(str(_SCREAMING_SNAKE_CASE							)							) > 2, "year should be in YYYY format"
 assert 1 <= month <= 12, "month should be between 1 to 12"
 assert 1 <= day <= 31, "day should be between 1 to 31"

 # Doomsday algorithm:
 lowerCamelCase__              =	year // 100
 lowerCamelCase__              =	(5 * (century % 4) + 2) % 7
 lowerCamelCase__              =	year % 100
 lowerCamelCase__              =	centurian % 12
 lowerCamelCase__              =	(
     (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
 ) % 7
 lowerCamelCase__              =	(
     DOOMSDAY_NOT_LEAP[month - 1]
     if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
     else DOOMSDAY_LEAP[month - 1]
 )
 lowerCamelCase__              =	(dooms_day + day - day_anchor) % 7
 return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
   import doctest

   doctest.testmod()










from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""ClapFeatureExtractor"""
			lowerCAmelCase_							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''sampling_rate''' ,						__lowerCAmelCase							)

				if text is None and audios is None:
					raise ValueError('''You have to specify either text or audios. Both cannot be none.'''							)

				if text is not None:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if audios is not None:
					lowerCamelCase__              =	self.feature_extractor(
					    __lowerCAmelCase ,						sampling_rate=__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if text is not None and audios is not None:
					lowerCamelCase__              =	audio_features.input_features
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**__lowerCAmelCase							) ,						tensor_type=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.tokenizer.model_input_names
				lowerCamelCase__              =	self.feature_extractor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)



