






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case     :     Tuple       =  logging.get_logger(__name__)

__snake_case     :     Tuple       =  {
    """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/config.json""",
    """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/config.json""",
    """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json""",
    """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json""",
    """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json""",
    """microsoft/deberta-xlarge-mnli""": """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json""",
}


class      __SCREAMING_SNAKE_CASE       ( __UpperCAmelCase):
						_SCREAMING_SNAKE_CASE						:							Dict				=       '''deberta'''






						def __init__(						self  ,    _UpperCamelCase=5_02_65  ,    _UpperCamelCase=7_68  ,    _UpperCamelCase=12  ,    _UpperCamelCase=12  ,    _UpperCamelCase=30_72  ,    _UpperCamelCase="gelu"  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=5_12  ,    _UpperCamelCase=0  ,    _UpperCamelCase=0.02  ,    _UpperCamelCase=1E-7  ,    _UpperCamelCase=False  ,    _UpperCamelCase=-1  ,    _UpperCamelCase=0  ,    _UpperCamelCase=True  ,    _UpperCamelCase=None  ,    _UpperCamelCase=0  ,    _UpperCamelCase="gelu"  ,    **_UpperCamelCase  ,    ):






												"""simple docstring"""
												super().__init__(**UpperCAmelCase_ )

												lowerCAmelCase__         =    hidden_size
												lowerCAmelCase__         =    num_hidden_layers
												lowerCAmelCase__         =    num_attention_heads
												lowerCAmelCase__         =    intermediate_size
												lowerCAmelCase__         =    hidden_act
												lowerCAmelCase__         =    hidden_dropout_prob
												lowerCAmelCase__         =    attention_probs_dropout_prob
												lowerCAmelCase__         =    max_position_embeddings
												lowerCAmelCase__         =    type_vocab_size
												lowerCAmelCase__         =    initializer_range
												lowerCAmelCase__         =    relative_attention
												lowerCAmelCase__         =    max_relative_positions
												lowerCAmelCase__         =    pad_token_id
												lowerCAmelCase__         =    position_biased_input

												# Backwards compatibility
												if type(UpperCAmelCase_ ) == str:
																		lowerCAmelCase__         =    [x.strip() for x in pos_att_type.lower().split('|' )]

												lowerCAmelCase__         =    pos_att_type
												lowerCAmelCase__         =    vocab_size
												lowerCAmelCase__         =    layer_norm_eps

												lowerCAmelCase__         =    kwargs.get('pooler_hidden_size'  ,    UpperCAmelCase_ )
												lowerCAmelCase__         =    pooler_dropout
												lowerCAmelCase__         =    pooler_hidden_act




class      __SCREAMING_SNAKE_CASE       ( __UpperCAmelCase):






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												if self.task == "multiple-choice":
																		lowerCAmelCase__         =    {0: 'batch', 1: 'choice', 2: 'sequence'}
												else:
																		lowerCAmelCase__         =    {0: 'batch', 1: 'sequence'}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
												else:
																		return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return 12







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = -1  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    _UpperCamelCase = 3  ,    _UpperCamelCase = 40  ,    _UpperCamelCase = 40  ,    _UpperCamelCase = None  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    super().generate_dummy_inputs(preprocessor=UpperCAmelCase_  ,    framework=UpperCAmelCase_ )
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs







"""simple docstring"""

from __future__ import annotations

from queue import Queue
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		a ^= b
		b ^= a
		a ^= b
		return a, b
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       1
		while (1 << j) < max_node:
				for i in range(1,		max_node + 1     ):
						lowerCamelCase	:     Tuple            =       parent[j - 1][parent[j - 1][i]]
				j += 1
		return parent
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_     ):





		'''simple docstring'''


		if level[u] < level[v]:
				lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       swap(a_,		a_     )
		# making depth of u same as depth of v
		for i in range(18,		-1,		-1     ):
				if level[u] - (1 << i) >= level[v]:
						lowerCamelCase	:     int            =       parent[i][u]
    # at the same depth if u==v that mean lca is found
		if u == v:
				return u
		# moving both nodes upwards till lca in found
		for i in range(18,		-1,		-1     ):
				if parent[i][u] not in [0, parent[i][v]]:
						lowerCamelCase      ,       lowerCamelCase	:     str            =       parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
		return parent[0][u]
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_,		a_ = 1,		):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       0
		lowerCamelCase	:     Queue[int]            =       Queue(maxsize=a_     )
		q.put(a_     )
		while q.qsize() != 0:
				lowerCamelCase	:     Tuple            =       q.get()
				for v in graph[u]:
						if level[v] == -1:
								lowerCamelCase	:     Optional[Any]            =       level[u] + 1
								q.put(a_     )
								lowerCamelCase	:     str            =       u
		return level, parent
def 						UpperCAmelCase   (						):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       13
		# initializing with 0
		lowerCamelCase	:     Dict            =       [[0 for _ in range(max_node + 10     )] for _ in range(20     )]
		# initializing with -1 which means every node is unvisited
		lowerCamelCase	:     List[Any]            =       [-1 for _ in range(max_node + 10     )]
		lowerCamelCase	:     dict[int, list[int]]            =       {
		    1: [2, 3, 4],
		    2: [5],
		    3: [6, 7],
		    4: [8],
		    5: [9, 10],
		    6: [11],
		    7: [],
		    8: [12, 13],
		    9: [],
		    10: [],
		    11: [],
		    12: [],
		    13: [],
		}
		lowerCamelCase      ,       lowerCamelCase	:     Dict            =       breadth_first_search(a_,		a_,		a_,		a_,		1     )
		lowerCamelCase	:     List[Any]            =       create_sparse(a_,		a_     )
		print('LCA of node 1 and 3 is: ',		lowest_common_ancestor(1,		3,		a_,		a_     )     )
		print('LCA of node 5 and 6 is: ',		lowest_common_ancestor(5,		6,		a_,		a_     )     )
		print('LCA of node 7 and 11 is: ',		lowest_common_ancestor(7,		11,		a_,		a_     )     )
		print('LCA of node 6 and 7 is: ',		lowest_common_ancestor(6,		7,		a_,		a_     )     )
		print('LCA of node 4 and 12 is: ',		lowest_common_ancestor(4,		12,		a_,		a_     )     )
		print('LCA of node 8 and 8 is: ',		lowest_common_ancestor(8,		8,		a_,		a_     )     )


if __name__ == "__main__":
					main()






