






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  List[Any]       )	->    Dict:

							'''simple docstring'''






							if isinstance(_a			,				_a       ):
														SCREAMING_SNAKE_CASE__       :      Dict     =						np.full((len(_a       ), sequence_length, 2)			,				_a       )
							else:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						np.full((len(_a       ), sequence_length)			,				_a       )

							for i, tensor in enumerate(_a       ):
														if padding_side == "right":
																					if isinstance(_a			,				_a       ):
																												SCREAMING_SNAKE_CASE__       :      str     =						tensor[:sequence_length]
																					else:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						tensor[:sequence_length]
														else:
																					if isinstance(_a			,				_a       ):
																												SCREAMING_SNAKE_CASE__       :      str     =						tensor[:sequence_length]
																					else:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						tensor[:sequence_length]

							return out_tensor.tolist()




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						ord(_a       )
							if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
														return True
							SCREAMING_SNAKE_CASE__       :      List[str]     =						unicodedata.category(_a       )
							if cat.startswith("P"       ):
														return True
							return False







@dataclass
class 						lowerCamelCase       (__snake_case      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   True
							UpperCAmelCase_      						=   None
							UpperCAmelCase_      						=   None
							UpperCAmelCase_      						=   -100
							UpperCAmelCase_      						=   "pt"






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														import torch

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"""label""" if """label""" in features[0].keys() else """labels"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.tokenizer.pad(
														    lowerCamelCase_,			padding=self.padding,			max_length=self.max_length,			pad_to_multiple_of=self.pad_to_multiple_of,			return_tensors="pt" if labels is None else None,			)

														if labels is None:
																					return batch

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor(batch["entity_ids"]						).shape[1]
														SCREAMING_SNAKE_CASE__       :      str     =						self.tokenizer.padding_side
														if padding_side == "right":
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
																					    list(lowerCamelCase_						) + [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase_						)) for label in labels
																					]
														else:
																					SCREAMING_SNAKE_CASE__       :      Any     =						[
																					    [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase_						)) + list(lowerCamelCase_						) for label in labels
																					]

														SCREAMING_SNAKE_CASE__       :      Dict     =						[feature["""ner_tags"""] for feature in features]
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						padding_tensor(lowerCamelCase_,			-1,			lowerCamelCase_,			lowerCamelCase_						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[feature["""original_entity_spans"""] for feature in features]
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						padding_tensor(lowerCamelCase_,			(-1, -1),			lowerCamelCase_,			lowerCamelCase_						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						{k: torch.tensor(lowerCamelCase_,			dtype=torch.intaa						) for k, v in batch.items()}

														return batch













from queue import PriorityQueue
from typing import Any

import numpy as np




def _a (  SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  set			,				SCREAMING_SNAKE_CASE__      :  set			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  PriorityQueue			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  float | int			,				)	->    float | int:

							'''simple docstring'''






							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						cst_fwd.get(SCREAMING_SNAKE_CASE__			,				np.inf       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)       )
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_cost_f
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-1

							SCREAMING_SNAKE_CASE__       :      List[str]     =						set()
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						set()
							SCREAMING_SNAKE_CASE__       :      int     =						{source: 0}
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{destination: 0}
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						{source: None}
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{destination: None}
							SCREAMING_SNAKE_CASE__       :      PriorityQueue[Any]     =						PriorityQueue()
							SCREAMING_SNAKE_CASE__       :      PriorityQueue[Any]     =						PriorityQueue()

							SCREAMING_SNAKE_CASE__       :      Dict     =						np.inf

							queue_forward.put((0, source)       )
							queue_backward.put((0, destination)       )

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						queue_forward.get()
														visited_forward.add(SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						queue_backward.get()
														visited_backward.add(SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      Any     =						pass_and_relaxation(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pass_and_relaxation(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE__       :      int     =						shortest_distance
							return shortest_path_distance


_lowerCamelCase :    Optional[Any]  							= {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
_lowerCamelCase :    Tuple  							= {
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()






