
import functools
import operator

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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "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       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'sew'







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







								super().__init__(**snake_case__      ,				pad_token_id=snake_case__      ,				bos_token_id=snake_case__      ,				eos_token_id=snake_case__ )
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							feat_extract_norm
								__lowerCAmelCase         =							feat_extract_activation
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							conv_bias
								__lowerCAmelCase         =							num_conv_pos_embeddings
								__lowerCAmelCase         =							num_conv_pos_embedding_groups
								__lowerCAmelCase         =							len(self.conv_dim )
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							squeeze_factor
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							hidden_dropout
								__lowerCAmelCase         =							attention_dropout
								__lowerCAmelCase         =							activation_dropout
								__lowerCAmelCase         =							feat_proj_dropout
								__lowerCAmelCase         =							final_dropout
								__lowerCAmelCase         =							layerdrop
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							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
								__lowerCAmelCase         =							apply_spec_augment
								__lowerCAmelCase         =							mask_time_prob
								__lowerCAmelCase         =							mask_time_length
								__lowerCAmelCase         =							mask_time_min_masks
								__lowerCAmelCase         =							mask_feature_prob
								__lowerCAmelCase         =							mask_feature_length
								__lowerCAmelCase         =							mask_feature_min_masks

								# ctc loss
								__lowerCAmelCase         =							ctc_loss_reduction
								__lowerCAmelCase         =							ctc_zero_infinity

								# sequence classification
								__lowerCAmelCase         =							use_weighted_layer_sum
								__lowerCAmelCase         =							classifier_proj_size







							@property
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







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








from __future__ import annotations

from math import ceil, floor, sqrt






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 2_0_0_0_0_0_0  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[0]
	__lowerCAmelCase         =							42

	for idx in range(1			,							ceil(sqrt(target * 2  ) * 1.1  )  ):
		triangle_numbers.append(triangle_numbers[-1] + idx  )

	# we want this to be as close as possible to target
	__lowerCAmelCase         =							0
	# the area corresponding to the grid that gives the product closest to target
	__lowerCAmelCase         =							0
	# an estimate of b, using the quadratic formula
	__lowerCAmelCase         =							42
	# the largest integer less than b_estimate
	__lowerCAmelCase         =							42
	# the largest integer less than b_estimate
	__lowerCAmelCase         =							42
	# the triangle number corresponding to b_floor
	__lowerCAmelCase         =							42
	# the triangle number corresponding to b_ceil
	__lowerCAmelCase         =							42

	for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,							1  ):
		__lowerCAmelCase         =							(-1 + sqrt(1 + 8 * target / triangle_a  )) / 2
		__lowerCAmelCase         =							floor(UpperCamelCase  )
		__lowerCAmelCase         =							ceil(UpperCamelCase  )
		__lowerCAmelCase         =							triangle_numbers[b_floor]
		__lowerCAmelCase         =							triangle_numbers[b_ceil]

		if abs(target - triangle_b_first_guess * triangle_a  ) < abs(
		    target - best_product  ):
			__lowerCAmelCase         =							triangle_b_first_guess * triangle_a
			__lowerCAmelCase         =							idx_a * b_floor

		if abs(target - triangle_b_second_guess * triangle_a  ) < abs(
		    target - best_product  ):
			__lowerCAmelCase         =							triangle_b_second_guess * triangle_a
			__lowerCAmelCase         =							idx_a * b_ceil

	return area


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






