




from ....configuration_utils import PretrainedConfig
from ....utils import logging


_snake_case				:				Any       							=		logging.get_logger(__name__)

_snake_case				:				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       A   (		_lowerCAmelCase   ):
							lowercase_          =    'mctct'







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict=80_65							,      lowerCAmelCase_	:  int=15_36							,      lowerCAmelCase_	:  List[str]=36							,      lowerCAmelCase_	:  Any=61_44							,      lowerCAmelCase_	:  Optional[Any]=4							,      lowerCAmelCase_	:  int=3_84							,      lowerCAmelCase_	:  Any=9_20							,      lowerCAmelCase_	:  int=1e-5							,      lowerCAmelCase_	:  List[Any]=0.3							,      lowerCAmelCase_	:  Optional[Any]="relu"							,      lowerCAmelCase_	:  Tuple=0.0_2							,      lowerCAmelCase_	:  Union[str, Any]=0.3							,      lowerCAmelCase_	:  int=0.3							,      lowerCAmelCase_	:  Any=1							,      lowerCAmelCase_	:  str=0							,      lowerCAmelCase_	:  Optional[Any]=2							,      lowerCAmelCase_	:  Union[str, Any]=1							,      lowerCAmelCase_	:  int=0.3							,      lowerCAmelCase_	:  str=1							,      lowerCAmelCase_	:  Dict=(7,)							,      lowerCAmelCase_	:  Optional[int]=(3,)							,      lowerCAmelCase_	:  Dict=80							,      lowerCAmelCase_	:  Optional[int]=1							,      lowerCAmelCase_	:  List[Any]=None							,      lowerCAmelCase_	:  Tuple="sum"							,      lowerCAmelCase_	:  Any=False							,      **lowerCAmelCase_	:  List[str]							,      )      ->							int:


														"""simple docstring"""


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

														# prevents config testing fail with exporting to json
														_a     					=    list(SCREAMING_SNAKE_CASE_ )
														_a     					=    list(SCREAMING_SNAKE_CASE_ )

														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}`.' )







'''simple docstring'''


from __future__ import annotations
def 		snake_case_   (UpperCamelCase	:  list[int]				):





							'''simple docstring'''


							if not nums:
														return 0
							_a     					=    nums[0]
							_a     					=    0
							for num in nums[1:]:
														_a					,							_a     					=    (
														    max_excluding + num,
														    max(UpperCamelCase			,						UpperCamelCase				),
														)
							return max(UpperCamelCase			,						UpperCamelCase				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

