

import sys
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[Any]       )					->				int:
	__A :			Union[str, Any]		    =			len(a__       )
	__A :			Any		    =			[[0 for x in range(a__       )] for x in range(a__       )]
	__A :			Optional[Any]		    =			[[0 for x in range(a__       )] for x in range(a__       )]

	for chain_length in range(2			,a__       ):
		for a in range(1			,n - chain_length + 1       ):
			__A :			Tuple		    =			a + chain_length - 1

			__A :			List[str]		    =			sys.maxsize
			for c in range(a__			,a__       ):
				__A :			List[str]		    =			(
				    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
				)
				if cost < matrix[a][b]:
					__A :			Any		    =			cost
					__A :			Any		    =			c
	return matrix, sol
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Optional[Any]			,a__    :   Any       )					->				Any:
	if i == j:
		print("""A""" + str(a__       )			,end=""" """       )
	else:
		print("""("""			,end=""" """       )
		print_optiomal_solution(a__			,a__			,optimal_solution[i][j]       )
		print_optiomal_solution(a__			,optimal_solution[i][j] + 1			,a__       )
		print(""")"""			,end=""" """       )
def 							__SCREAMING_SNAKE_CASE     (   )					->				List[str]:
	__A :			Optional[int]		    =			[30, 35, 15, 5, 10, 20, 25]
	__A :			Optional[int]		    =			len(a__       )
	# Size of matrix created from above array will be
	# 30*35 35*15 15*5 5*10 10*20 20*25
	__A    ,				__A :			Optional[int]		    =			matrix_chain_order(a__       )

	print("""No. of Operation required: """ + str(matrix[1][n - 1]       )       )
	print_optiomal_solution(a__			,1			,n - 1       )


if __name__ == "__main__":
			main()








from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				'''EncodecFeatureExtractor'''
	_lowercase		:   Any     					=				('''T5Tokenizer''', '''T5TokenizerFast''')




	def __init__(     self						:     List[Any]					,      __A						:     Any					,      __A						:     Tuple							):
		super().__init__(__A					,      __A							)
		__A :			Dict		    =			self.feature_extractor
		__A :			List[str]		    =			False




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str=None					,      __A						:     Tuple=None					,      __A						:     Dict=True							):
		return self.tokenizer.get_decoder_prompt_ids(task=__A					,      language=__A					,      no_timestamps=__A							)




	def __call__(     self						:     Optional[Any]					,      *__A						:     Tuple					,      **__A						:     Tuple							):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor(*__A					,      **__A							)

		__A :			str		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			Optional[Any]		    =			kwargs.pop("""sampling_rate"""					,      __A							)
		__A :			int		    =			kwargs.pop("""text"""					,      __A							)
		if len(__A							) > 0:
			__A :			int		    =			args[0]
			__A :			Dict		    =			args[1:]

		if audio is None and text is None:
			raise ValueError("""You need to specify either an `audio` or `text` input to process."""							)

		if text is not None:
			__A :			Dict		    =			self.tokenizer(__A					,      **__A							)

		if audio is not None:
			__A :			Optional[int]		    =			self.feature_extractor(__A					,      *__A					,      sampling_rate=__A					,      **__A							)

		if audio is None:
			return inputs

		elif text is None:
			return audio_inputs

		else:
			__A :			List[Any]		    =			audio_inputs["""input_values"""]
			if "padding_mask" in audio_inputs:
				__A :			int		    =			audio_inputs["""padding_mask"""]
			return inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     int					,      **__A						:     Tuple							):
		__A :			Optional[int]		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			List[str]		    =			kwargs.pop("""padding_mask"""					,      __A							)

		if len(__A							) > 0:
			__A :			Dict		    =			args[0]
			__A :			Optional[int]		    =			args[1:]

		if audio_values is not None:
			return self._decode_audio(__A					,      padding_mask=__A							)
		else:
			return self.tokenizer.batch_decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      *__A						:     Dict					,      **__A						:     Any							):
		return self.tokenizer.decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[str, Any]					,      __A						:     Optional = None							):
		__A :			List[str]		    =			to_numpy(__A							)
		__A    ,				__A    ,				__A :			Tuple		    =			audio_values.shape

		if padding_mask is None:
			return list(__A							)

		__A :			Union[str, Any]		    =			to_numpy(__A							)

		# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
		# token (so that the generated audio values are **not** treated as padded tokens)
		__A :			List[str]		    =			seq_len - padding_mask.shape[-1]
		__A :			Tuple		    =			1 - self.feature_extractor.padding_value
		__A :			Optional[int]		    =			np.pad(__A					,      ((0, 0), (0, difference))					,      """constant"""					,      constant_values=__A							)

		__A :			int		    =			audio_values.tolist()
		for i in range(__A							):
			__A :			str		    =			np.asarray(audio_values[i]							)[
			    padding_mask[i][None, :] != self.feature_extractor.padding_value
			]
			__A :			List[Any]		    =			sliced_audio.reshape(__A					,      -1							)

		return audio_values





