


from __future__ import annotations

from random import random


class 					lowerCAmelCase_ :




						def __init__(	self						:   Any   ,     __A						:   int | None = None						)      ->Any:






									"""simple docstring"""






									a__ :Optional[int]						  =    value
									a__ :Any						  =    random()
									a__ :Node | None						  =    None
									a__ :Node | None						  =    None




						def __repr__(	self						:   Tuple						)      ->str:






									"""simple docstring"""






									from pprint import pformat

									if self.left is None and self.right is None:
												return F'''\'{self.value}: {self.prior:.5}\''''
									else:
												return pformat(
												    {F'''{self.value}: {self.prior:.5}''': (self.left, self.right)}   ,     indent=1						)






						def __str__(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									a__ :Dict						  =    str(self.value						) + " "
									a__ :int						  =    str(self.left or ""						)
									a__ :Optional[int]						  =    str(self.right or ""						)
									return value + left + right






def 				lowerCamelCase__	(      a	:   Node | None	,  a	:   int				)    ->	tuple[Node | None, Node | None]:





			"""simple docstring"""
			if root is None:  # None tree is split into 2 Nones
						return None, None
			elif root.value is None:
						return None, None
			else:
						if value < root.value:
									a__  ,  a__ :Union[str, Any]						  =    split(root.left	,  a				)
									return left, root
						else:
									a__  ,  a__ :int						  =    split(root.right	,  a				)
									return root, right


def 				lowerCamelCase__	(      a	:   Node | None	,  a	:   Node | None				)    ->	Node | None:





			"""simple docstring"""
			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						a__ :Optional[int]						  =    merge(left.right	,  a				)
						return left
			else:
						a__ :Optional[Any]						  =    merge(a	,  right.left				)
						return right


def 				lowerCamelCase__	(      a	:   Node | None	,  a	:   int				)    ->	Node | None:





			"""simple docstring"""
			a__ :Optional[Any]						  =    Node(a				)
			a__  ,  a__ :Dict						  =    split(a	,  a				)
			return merge(merge(a	,  a				)	,  a				)


def 				lowerCamelCase__	(      a	:   Node | None	,  a	:   int				)    ->	Node | None:





			"""simple docstring"""
			a__  ,  a__ :Any						  =    split(a	,  value - 1				)
			a__  ,  a__ :Any						  =    split(a	,  a				)
			return merge(a	,  a				)


def 				lowerCamelCase__	(      a	:   Node | None				)    ->	None:





			"""simple docstring"""
			if not root:  # None
						return
			else:
						inorder(root.left				)
						print(root.value	,  end=","				)
						inorder(root.right				)


def 				lowerCamelCase__	(      a	:   Node | None	,  a	:   str				)    ->	Node | None:





			"""simple docstring"""
			for arg in args.split():
						if arg[0] == "+":
									a__ :List[str]						  =    insert(a	,  int(arg[1:]				)				)

						elif arg[0] == "-":
									a__ :List[str]						  =    erase(a	,  int(arg[1:]				)				)

						else:
									print("Unknown command"				)

			return root


def 				lowerCamelCase__	(      )    ->	None:





			"""simple docstring"""
			a__ :int						  =    None
			print(
			    "enter numbers to create a tree, + value to add value into treap, "
			    "- value to erase all nodes with value. 'q' to quit. "				)

			a__ :Optional[Any]						  =    input()
			while args != "q":
						a__ :Any						  =    interact_treap(a	,  a				)
						print(a				)
						a__ :Tuple						  =    input()

			print("good by!"				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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_ (       _a):
						lowerCamelCase_         =     'sew'




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






									"""simple docstring"""






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

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






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


