



import functools
import operator

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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Any      =  {
    "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      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[Any]				=	"""sew"""



	def __init__(       self			,	UpperCAmelCase__=32			,	UpperCAmelCase__=768			,	UpperCAmelCase__=12			,	UpperCAmelCase__=12			,	UpperCAmelCase__=3_072			,	UpperCAmelCase__=2			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__="group"			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)			,	UpperCAmelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)			,	UpperCAmelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)			,	UpperCAmelCase__=False			,	UpperCAmelCase__=128			,	UpperCAmelCase__=16			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0.05			,	UpperCAmelCase__=10			,	UpperCAmelCase__=2			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=10			,	UpperCAmelCase__=0			,	UpperCAmelCase__="mean"			,	UpperCAmelCase__=False			,	UpperCAmelCase__=False			,	UpperCAmelCase__=256			,	UpperCAmelCase__=0			,	UpperCAmelCase__=1			,	UpperCAmelCase__=2			,	**UpperCAmelCase__			,	):
							super().__init__(**UpperCAmelCase__			,	pad_token_id=UpperCAmelCase__			,	bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__		)
							A__							=       hidden_size
							A__							=       feat_extract_norm
							A__							=       feat_extract_activation
							A__							=       list(UpperCAmelCase__		)
							A__							=       list(UpperCAmelCase__		)
							A__							=       list(UpperCAmelCase__		)
							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 			__A							(       self		):
							return functools.reduce(operator.mul			,	self.conv_stride			,	1		)










from __future__ import annotations

from random import random






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__ = None		):
							A__							=       value
							A__							=       random()
							A__							=       None
							A__							=       None



	def __repr__(       self		):
							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		):
							A__							=       str(self.value		) + " "
							A__							=       str(self.left or ""		)
							A__							=       str(self.right or ""		)
							return value + left + right





def  UpperCamelCase						(       _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__							=       split(root.left			,							_A    )
																		return left, root
												else:
																		A__						,       A__							=       split(root.right			,							_A    )
																		return root, right
def  UpperCamelCase						(       _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__							=       merge(left.right			,							_A    )
												return left
						else:
												A__							=       merge(_A			,							right.left    )
												return right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__							=       Node(_A    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(merge(_A			,							_A    )			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__						,       A__							=       split(_A			,							value - 1    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None    )->       None:





						"""simple docstring"""





						if not root:  # None
												return
						else:
												inorder(root.left    )
												print(root.value			,							end=","    )
												inorder(root.right    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				str    )->       Node | None:





						"""simple docstring"""





						for arg in args.split():
												if arg[0] == "+":
																		A__							=       insert(_A			,							int(arg[1:]    )    )

												elif arg[0] == "-":
																		A__							=       erase(_A			,							int(arg[1:]    )    )

												else:
																		print("Unknown command"    )

						return root
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       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__							=       input()
						while args != "q":
												A__							=       interact_treap(_A			,							_A    )
												print(_A    )
												A__							=       input()

						print("good by!"    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





