
"""simple docstring"""





import functools
import operator

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


UpperCamelCase_					:						Dict				= logging.get_logger(__name__)

UpperCamelCase_					:						Dict				= {
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"sew"




			def __init__(				self						:					Dict       ,   _snake_case						:					List[str]=32       ,   _snake_case						:					Any=768       ,   _snake_case						:					int=12       ,   _snake_case						:					Optional[int]=12       ,   _snake_case						:					Any=3_072       ,   _snake_case						:					int=2       ,   _snake_case						:					int="gelu"       ,   _snake_case						:					Optional[int]=0.1       ,   _snake_case						:					int=0.1       ,   _snake_case						:					int=0.1       ,   _snake_case						:					Optional[int]=0.0       ,   _snake_case						:					List[Any]=0.1       ,   _snake_case						:					List[str]=0.1       ,   _snake_case						:					List[str]=0.0_2       ,   _snake_case						:					Union[str, Any]=1e-5       ,   _snake_case						:					int="group"       ,   _snake_case						:					Optional[int]="gelu"       ,   _snake_case						:					Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,   _snake_case						:					Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,   _snake_case						:					Optional[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,   _snake_case						:					Dict=False       ,   _snake_case						:					str=128       ,   _snake_case						:					List[Any]=16       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Optional[Any]=0.0_5       ,   _snake_case						:					Tuple=10       ,   _snake_case						:					Tuple=2       ,   _snake_case						:					List[str]=0.0       ,   _snake_case						:					Dict=10       ,   _snake_case						:					Union[str, Any]=0       ,   _snake_case						:					Tuple="mean"       ,   _snake_case						:					List[Any]=False       ,   _snake_case						:					Optional[int]=False       ,   _snake_case						:					List[str]=256       ,   _snake_case						:					int=0       ,   _snake_case						:					Any=1       ,   _snake_case						:					int=2       ,   **_snake_case						:					Dict       ,   )      -> Any:







								"""simple docstring"""
								super().__init__(**_snake_case       ,   pad_token_id=_snake_case       ,   bos_token_id=_snake_case       ,   eos_token_id=_snake_case	)
								A_				 =						hidden_size
								A_				 =						feat_extract_norm
								A_				 =						feat_extract_activation
								A_				 =						list(_snake_case	)
								A_				 =						list(_snake_case	)
								A_				 =						list(_snake_case	)
								A_				 =						conv_bias
								A_				 =						num_conv_pos_embeddings
								A_				 =						num_conv_pos_embedding_groups
								A_				 =						len(self.conv_dim	)
								A_				 =						num_hidden_layers
								A_				 =						intermediate_size
								A_				 =						squeeze_factor
								A_				 =						hidden_act
								A_				 =						num_attention_heads
								A_				 =						hidden_dropout
								A_				 =						attention_dropout
								A_				 =						activation_dropout
								A_				 =						feat_proj_dropout
								A_				 =						final_dropout
								A_				 =						layerdrop
								A_				 =						layer_norm_eps
								A_				 =						initializer_range
								A_				 =						vocab_size

								if (
								    (len(self.conv_stride	) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
								    or (len(self.conv_dim	) != self.num_feat_extract_layers)
								):
													raise ValueError(
													    "Configuration for convolutional layers is incorrect."
													    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
													    F'but is `len(config.conv_dim) = {len(self.conv_dim	)}`, `len(config.conv_stride)'
													    F'= {len(self.conv_stride	)}`, `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'	)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								A_				 =						apply_spec_augment
								A_				 =						mask_time_prob
								A_				 =						mask_time_length
								A_				 =						mask_time_min_masks
								A_				 =						mask_feature_prob
								A_				 =						mask_feature_length
								A_				 =						mask_feature_min_masks

								# ctc loss
								A_				 =						ctc_loss_reduction
								A_				 =						ctc_zero_infinity

								# sequence classification
								A_				 =						use_weighted_layer_sum
								A_				 =						classifier_proj_size






			@property
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[int]:







								"""simple docstring"""
								return functools.reduce(operator.mul       ,   self.conv_stride       ,   1	)






"""simple docstring"""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0] * n for i in range(__a				)]
					for i in range(__a				):
										A_				 =						y_points[i]

					for i in range(2				,   __a				):
										for j in range(__a				,   __a				):
															A_				 =						(
															    (xa - x_points[j - i + 1]) * q[j][i - 1]
															    - (xa - x_points[j]) * q[j - 1][i - 1]
															) / (x_points[j] - x_points[j - i + 1])

					return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




