






'''simple docstring'''







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


_A	:			Optional[Any]         =logging.get_logger(__name__)

_A	:			int         ={
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}

class _lowercase	(				_lowercase		):
							a				      =       """deberta-v2"""




							def __init__(							self:				Optional[int]			,							UpperCamelCase__:				Union[str, Any]=128_100			,							UpperCamelCase__:				str=1_536			,							UpperCamelCase__:				List[Any]=24			,							UpperCamelCase__:				Any=24			,							UpperCamelCase__:				str=6_144			,							UpperCamelCase__:				int="gelu"			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				Dict=0.1			,							UpperCamelCase__:				Optional[Any]=512			,							UpperCamelCase__:				Optional[int]=0			,							UpperCamelCase__:				Optional[int]=0.02			,							UpperCamelCase__:				List[str]=1e-7			,							UpperCamelCase__:				List[Any]=False			,							UpperCamelCase__:				Any=-1			,							UpperCamelCase__:				Union[str, Any]=0			,							UpperCamelCase__:				Tuple=True			,							UpperCamelCase__:				Optional[int]=None			,							UpperCamelCase__:				Union[str, Any]=0			,							UpperCamelCase__:				Tuple="gelu"			,							**UpperCamelCase__:				List[str]			,							):
														super().__init__(**UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       hidden_size
														lowerCamelCase__					:	Tuple         =       num_hidden_layers
														lowerCamelCase__					:	int         =       num_attention_heads
														lowerCamelCase__					:	Dict         =       intermediate_size
														lowerCamelCase__					:	List[Any]         =       hidden_act
														lowerCamelCase__					:	List[str]         =       hidden_dropout_prob
														lowerCamelCase__					:	Optional[int]         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Dict         =       max_position_embeddings
														lowerCamelCase__					:	Tuple         =       type_vocab_size
														lowerCamelCase__					:	Union[str, Any]         =       initializer_range
														lowerCamelCase__					:	Any         =       relative_attention
														lowerCamelCase__					:	Any         =       max_relative_positions
														lowerCamelCase__					:	Any         =       pad_token_id
														lowerCamelCase__					:	List[str]         =       position_biased_input

														# Backwards compatibility
														if type(UpperCamelCase__     ) == str:
																					lowerCamelCase__					:	Union[str, Any]         =       [x.strip() for x in pos_att_type.lower().split("""|"""     )]

														lowerCamelCase__					:	Tuple         =       pos_att_type
														lowerCamelCase__					:	List[str]         =       vocab_size
														lowerCamelCase__					:	int         =       layer_norm_eps

														lowerCamelCase__					:	Tuple         =       kwargs.get("""pooler_hidden_size"""			,							UpperCamelCase__     )
														lowerCamelCase__					:	Optional[int]         =       pooler_dropout
														lowerCamelCase__					:	str         =       pooler_hidden_act



class _lowercase	(				_lowercase		):




							@property
							def 		lowerCamelCase_				(							self:				Any     ):
														if self.task == "multiple-choice":
																					lowerCamelCase__					:	Tuple         =       {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					lowerCamelCase__					:	Any         =       {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 		lowerCamelCase_				(							self:				Dict     ):
														return 12




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,							UpperCamelCase__:				int = -1			,							UpperCamelCase__:				int = -1			,							UpperCamelCase__:				int = -1			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				Optional["TensorType"] = None			,							UpperCamelCase__:				int = 3			,							UpperCamelCase__:				int = 40			,							UpperCamelCase__:				int = 40			,							UpperCamelCase__:				"PreTrainedTokenizerBase" = None			,							):
														lowerCamelCase__					:	List[str]         =       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 heapq import heappop, heappush

import numpy as np





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       tuple[float | int, list[tuple[int, int]]]:
							lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       grid.shape
							lowerCamelCase__					:	List[str]         =       [-1, 1, 0, 0]
							lowerCamelCase__					:	Dict         =       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       [(0, source)], set()
							lowerCamelCase__					:	Tuple         =       np.full((rows, cols)				,			np.inf      )
							lowerCamelCase__					:	List[str]         =       0
							lowerCamelCase__					:	Optional[int]         =       np.empty((rows, cols)				,			dtype=UpperCamelCase      )
							lowerCamelCase__					:	str         =       None

							while queue:
														((lowerCamelCase__)  ,		(lowerCamelCase__))					:	List[str]         =       heappop(UpperCamelCase      )
														if (x, y) in visited:
																					continue
														visited.add((x, y)      )

														if (x, y) == destination:
																					lowerCamelCase__					:	Optional[int]         =       []
																					while (x, y) != source:
																												path.append((x, y)      )
																												lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       predecessors[x, y]
																					path.append(UpperCamelCase      )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(UpperCamelCase      )      ):
																					lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												lowerCamelCase__					:	Any         =       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(UpperCamelCase				,			(dist + 1, (nx, ny))      )
																																			lowerCamelCase__					:	Union[str, Any]         =       dist + 1
																																			lowerCamelCase__					:	List[str]         =       (x, y)

							return np.inf, []


if __name__ == "__main__":
			import doctest

			doctest.testmod()

