

"""simple docstring"""




import functools
import operator

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


lowerCAmelCase							:  Tuple            =						logging.get_logger(__name__)

lowerCAmelCase							:  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   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Tuple      							=					'''sew'''



			def __init__(			self  :					Tuple       ,						lowerCAmelCase__  :					Tuple=32       ,						lowerCAmelCase__  :					List[Any]=768       ,						lowerCAmelCase__  :					Dict=12       ,						lowerCAmelCase__  :					str=12       ,						lowerCAmelCase__  :					List[str]=3072       ,						lowerCAmelCase__  :					Union[str, Any]=2       ,						lowerCAmelCase__  :					Any="gelu"       ,						lowerCAmelCase__  :					List[Any]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=0.1       ,						lowerCAmelCase__  :					Tuple=0.1       ,						lowerCAmelCase__  :					Optional[int]=0.0       ,						lowerCAmelCase__  :					Any=0.1       ,						lowerCAmelCase__  :					List[Any]=0.1       ,						lowerCAmelCase__  :					int=0.02       ,						lowerCAmelCase__  :					Dict=1E-5       ,						lowerCAmelCase__  :					Dict="group"       ,						lowerCAmelCase__  :					List[Any]="gelu"       ,						lowerCAmelCase__  :					int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,						lowerCAmelCase__  :					Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,						lowerCAmelCase__  :					List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,						lowerCAmelCase__  :					Any=False       ,						lowerCAmelCase__  :					List[str]=128       ,						lowerCAmelCase__  :					str=16       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Dict=0.05       ,						lowerCAmelCase__  :					Dict=10       ,						lowerCAmelCase__  :					Dict=2       ,						lowerCAmelCase__  :					Union[str, Any]=0.0       ,						lowerCAmelCase__  :					Any=10       ,						lowerCAmelCase__  :					str=0       ,						lowerCAmelCase__  :					Any="mean"       ,						lowerCAmelCase__  :					Any=False       ,						lowerCAmelCase__  :					Dict=False       ,						lowerCAmelCase__  :					Optional[int]=256       ,						lowerCAmelCase__  :					int=0       ,						lowerCAmelCase__  :					Union[str, Any]=1       ,						lowerCAmelCase__  :					Any=2       ,						**lowerCAmelCase__  :					Optional[Any]       ,						):
									super().__init__(**lowerCAmelCase__       ,						pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			str  		=	feat_extract_norm
									SCREAMING_SNAKE_CASE_:			Any  		=	feat_extract_activation
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	list(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	list(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	list(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	conv_bias
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_conv_pos_embeddings
									SCREAMING_SNAKE_CASE_:			List[str]  		=	num_conv_pos_embedding_groups
									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(self.conv_dim)
									SCREAMING_SNAKE_CASE_:			str  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Dict  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	squeeze_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	hidden_dropout
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	attention_dropout
									SCREAMING_SNAKE_CASE_:			Tuple  		=	activation_dropout
									SCREAMING_SNAKE_CASE_:			int  		=	feat_proj_dropout
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	final_dropout
									SCREAMING_SNAKE_CASE_:			Tuple  		=	layerdrop
									SCREAMING_SNAKE_CASE_:			Tuple  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			int  		=	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
									SCREAMING_SNAKE_CASE_:			List[str]  		=	apply_spec_augment
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	mask_time_prob
									SCREAMING_SNAKE_CASE_:			Any  		=	mask_time_length
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	mask_time_min_masks
									SCREAMING_SNAKE_CASE_:			List[str]  		=	mask_feature_prob
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	mask_feature_length
									SCREAMING_SNAKE_CASE_:			Dict  		=	mask_feature_min_masks

									# ctc loss
									SCREAMING_SNAKE_CASE_:			int  		=	ctc_loss_reduction
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	ctc_zero_infinity

									# sequence classification
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	use_weighted_layer_sum
									SCREAMING_SNAKE_CASE_:			str  		=	classifier_proj_size




			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									return functools.reduce(operator.mul       ,						self.conv_stride       ,						1)









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






