





import sys






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				snake_case_		:       Union[str, Any]           =    len(_UpperCamelCase					)
				snake_case_		:       List[Any]           =    [[0 for x in range(_UpperCamelCase					)] for x in range(_UpperCamelCase					)]
				snake_case_		:       List[Any]           =    [[0 for x in range(_UpperCamelCase					)] for x in range(_UpperCamelCase					)]

				for chain_length in range(2					,							_UpperCamelCase					):
								for a in range(1					,							n - chain_length + 1					):
												snake_case_		:       Dict           =    a + chain_length - 1

												snake_case_		:       Tuple           =    sys.maxsize
												for c in range(_UpperCamelCase					,							_UpperCamelCase					):
																snake_case_		:       Dict           =    (
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				snake_case_		:       List[str]           =    cost
																				snake_case_		:       int           =    c
				return matrix, sol






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		Any:






				"""simple docstring"""


				if i == j:
								print('''A''' + str(_UpperCamelCase					)					,							end=''' '''					)
				else:
								print('''('''					,							end=''' '''					)
								print_optiomal_solution(_UpperCamelCase					,							_UpperCamelCase					,							optimal_solution[i][j]					)
								print_optiomal_solution(_UpperCamelCase					,							optimal_solution[i][j] + 1					,							_UpperCamelCase					)
								print(''')'''					,							end=''' '''					)






def 	lowerCamelCase_		(							)						->		Union[str, Any]:






				"""simple docstring"""


				snake_case_		:       Tuple           =    [30, 35, 15, 5, 10, 20, 25]
				snake_case_		:       str           =    len(_UpperCamelCase					)
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				snake_case_     ,				snake_case_		:       Optional[Any]           =    matrix_chain_order(_UpperCamelCase					)

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


if __name__ == "__main__":
	main()


'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




