


import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

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


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew-d'




						def __init__(	self						:   Optional[Any]   ,     __A						:   List[Any]=32   ,     __A						:   int=768   ,     __A						:   Optional[int]=12   ,     __A						:   int=12   ,     __A						:   Tuple=3072   ,     __A						:   Dict=2   ,     __A						:   int=512   ,     __A						:   Union[str, Any]=256   ,     __A						:   int=True   ,     __A						:   str=True   ,     __A						:   List[Any]=("p2c", "c2p")   ,     __A						:   Dict="layer_norm"   ,     __A						:   Dict="gelu_python"   ,     __A						:   List[str]=0.1   ,     __A						:   Tuple=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   str=0.1   ,     __A						:   List[str]=0.02   ,     __A						:   Optional[int]=1E-7   ,     __A						:   Tuple=1E-5   ,     __A						:   List[Any]="group"   ,     __A						:   Any="gelu"   ,     __A						:   Optional[int]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[Any]=False   ,     __A						:   List[Any]=128   ,     __A						:   Optional[Any]=16   ,     __A						:   List[str]=True   ,     __A						:   List[str]=0.05   ,     __A						:   Dict=10   ,     __A						:   List[Any]=2   ,     __A						:   Optional[int]=0.0   ,     __A						:   List[Any]=10   ,     __A						:   Union[str, Any]=0   ,     __A						:   int="mean"   ,     __A						:   Union[str, Any]=False   ,     __A						:   List[Any]=False   ,     __A						:   List[str]=256   ,     __A						:   List[str]=0   ,     __A						:   List[Any]=1   ,     __A						:   Union[str, Any]=2   ,     **__A						:   Tuple   ,     )      ->Dict:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :Optional[int]						  =    hidden_size
									a__ :int						  =    feat_extract_norm
									a__ :Optional[Any]						  =    feat_extract_activation
									a__ :Tuple						  =    list(__A						)
									a__ :Tuple						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Optional[Any]						  =    conv_bias
									a__ :str						  =    num_conv_pos_embeddings
									a__ :Optional[int]						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[str]						  =    num_hidden_layers
									a__ :Dict						  =    intermediate_size
									a__ :Union[str, Any]						  =    squeeze_factor
									a__ :str						  =    max_position_embeddings
									a__ :int						  =    position_buckets
									a__ :List[Any]						  =    share_att_key
									a__ :Optional[int]						  =    relative_attention
									a__ :List[Any]						  =    norm_rel_ebd
									a__ :List[Any]						  =    list(__A						)
									a__ :List[str]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :int						  =    hidden_dropout
									a__ :Optional[int]						  =    attention_dropout
									a__ :Union[str, Any]						  =    activation_dropout
									a__ :int						  =    feat_proj_dropout
									a__ :int						  =    final_dropout
									a__ :Any						  =    layer_norm_eps
									a__ :str						  =    feature_layer_norm_eps
									a__ :List[str]						  =    initializer_range
									a__ :Tuple						  =    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__ :Tuple						  =    apply_spec_augment
									a__ :Dict						  =    mask_time_prob
									a__ :Any						  =    mask_time_length
									a__ :Optional[Any]						  =    mask_time_min_masks
									a__ :int						  =    mask_feature_prob
									a__ :int						  =    mask_feature_length
									a__ :str						  =    mask_feature_min_masks

									# ctc loss
									a__ :Union[str, Any]						  =    ctc_loss_reduction
									a__ :Optional[Any]						  =    ctc_zero_infinity

									# sequence classification
									a__ :List[str]						  =    use_weighted_layer_sum
									a__ :str						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   List[str]						)      ->Tuple:






									"""simple docstring"""






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






def 				lowerCamelCase__	(      a	:   int	,  a	:   int				)    ->	Any:





			"""simple docstring"""
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			a__ :Optional[int]						  =    (boundary[1] - boundary[0]) / steps
			a__ :str						  =    boundary[0]
			a__ :Optional[int]						  =    boundary[1]
			a__ :str						  =    make_points(a	,  a	,  a				)
			a__ :Any						  =    0.0
			y += (h / 2.0) * f(a				)
			for i in x_i:
						# print(i)
						y += h * f(a				)
			y += (h / 2.0) * f(a				)
			return y


def 				lowerCamelCase__	(      a	:   Any	,  a	:   str	,  a	:   Dict				)    ->	int:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    a + h
			while x < (b - h):
						yield x
						a__ :str						  =    x + h


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	List[Any]:  # enter your function here





			"""simple docstring"""
			a__ :Tuple						  =    (x - 0) * (x - 0)
			return y


def 				lowerCamelCase__	(      )    ->	List[str]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    0.0  # Lower bound of integration
			a__ :Optional[Any]						  =    1.0  # Upper bound of integration
			a__ :List[str]						  =    1_0.0  # define number of steps or resolution
			a__ :int						  =    [a, b]  # define boundary of integration
			a__ :str						  =    method_a(a	,  a				)
			print(F'''y = {y}'''				)


if __name__ == "__main__":
		main()


