
"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class 							__magic_name__     (		UpperCAmelCase_   ):
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   'xmod'





					def __init__(				self   :							Tuple							,						snake_case_   :							str=30522							,						snake_case_   :							Any=768							,						snake_case_   :							Union[str, Any]=12							,						snake_case_   :							List[Any]=12							,						snake_case_   :							Any=3072							,						snake_case_   :							Optional[int]="gelu"							,						snake_case_   :							Any=0.1							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							Tuple=512							,						snake_case_   :							Optional[Any]=2							,						snake_case_   :							List[str]=0.02							,						snake_case_   :							List[Any]=1e-12							,						snake_case_   :							List[str]=1							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							List[str]=2							,						snake_case_   :							Any="absolute"							,						snake_case_   :							Tuple=True							,						snake_case_   :							List[str]=None							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Union[str, Any]=2							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Optional[int]=True							,						snake_case_   :							Tuple=True							,						snake_case_   :							Optional[Any]=("en_XX",)							,						snake_case_   :							int=None							,						**snake_case_   :							Optional[Any]							,						):
												super().__init__(pad_token_id=_lowercase							,						bos_token_id=_lowercase							,						eos_token_id=_lowercase							,						**_lowercase    )

												__snake_case     			=       vocab_size
												__snake_case     			=       hidden_size
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_act
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_dropout_prob
												__snake_case     			=       attention_probs_dropout_prob
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       type_vocab_size
												__snake_case     			=       initializer_range
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       position_embedding_type
												__snake_case     			=       use_cache
												__snake_case     			=       classifier_dropout
												__snake_case     			=       pre_norm
												__snake_case     			=       adapter_reduction_factor
												__snake_case     			=       adapter_layer_norm
												__snake_case     			=       adapter_reuse_layer_norm
												__snake_case     			=       ln_before_adapter
												__snake_case     			=       list(_lowercase    )
												__snake_case     			=       default_language







class 							__magic_name__     (		UpperCAmelCase_   ):





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												if self.task == "multiple-choice":
																			__snake_case     			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
												else:
																			__snake_case     			=       {0: 'batch', 1: 'sequence'}
												return OrderedDict(
												    [
												        ("input_ids", dynamic_axis),
												        ("attention_mask", dynamic_axis),
												    ]    )


"""simple docstring"""







from heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

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

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
