



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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Any:







						"""simple docstring"""

						if isinstance(__lowercase							,     __lowercase       ):
												A__	:							Tuple						    = np.full((len(__lowercase       ), sequence_length, 2)							,     __lowercase       )
						else:
												A__	:							str						    = np.full((len(__lowercase       ), sequence_length)							,     __lowercase       )

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

						return out_tensor.tolist()
def 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Optional[Any]						    = ord(__lowercase       )
						if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
												return True
						A__	:							Dict						    = unicodedata.category(__lowercase       )
						if cat.startswith("P"       ):
												return True
						return False




@dataclass
class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    PreTrainedTokenizerBase
					__A     :    Union[bool, str, PaddingStrategy]           =						True
					__A     :    Optional[int]           =						None
					__A     :    Optional[int]           =						None
					__A     :    int           =						-1_00
					__A     :    str           =						"pt"







					def  _lowercase			(       self      :     List[str]			,		_A      :     int):
											import torch

											A__	:							List[str]						    = "label" if "label" in features[0].keys() else "labels"
											A__	:							Tuple						    = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
											A__	:							Dict						    = self.tokenizer.pad(
											    _A			,		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

											A__	:							Any						    = torch.tensor(batch["entity_ids"]).shape[1]
											A__	:							Dict						    = self.tokenizer.padding_side
											if padding_side == "right":
																	A__	:							List[str]						    = [
																	    list(_A) + [self.label_pad_token_id] * (sequence_length - len(_A)) for label in labels
																	]
											else:
																	A__	:							List[str]						    = [
																	    [self.label_pad_token_id] * (sequence_length - len(_A)) + list(_A) for label in labels
																	]

											A__	:							Optional[Any]						    = [feature["ner_tags"] for feature in features]
											A__	:							int						    = padding_tensor(_A			,		-1			,		_A			,		_A)
											A__	:							Union[str, Any]						    = [feature["original_entity_spans"] for feature in features]
											A__	:							Optional[Any]						    = padding_tensor(_A			,		(-1, -1)			,		_A			,		_A)
											A__	:							Optional[Any]						    = {k: torch.tensor(_A			,		dtype=torch.intaa) for k, v in batch.items()}

											return batch




from __future__ import annotations

snake_case    :						Optional[Any]  =						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}




class 							lowerCAmelCase__  :







					def __init__(       self      :     Optional[Any]			,		_A      :     dict[str, list[str]]			,		_A      :     str):
											A__	:							Optional[Any]						    = graph
											# mapping node to its parent in resulting breadth first tree
											A__	:							dict[str, str | None]						    = {}
											A__	:							List[str]						    = source_vertex







					def  _lowercase			(       self      :     List[Any]):
											A__	:							str						    = {self.source_vertex}
											A__	:							List[str]						    = None
											A__	:							List[str]						    = [self.source_vertex]  # first in first out queue

											while queue:
																	A__	:							int						    = queue.pop(0)
																	for adjacent_vertex in self.graph[vertex]:
																							if adjacent_vertex not in visited:
																													visited.add(_A)
																													A__	:							Any						    = vertex
																													queue.append(_A)







					def  _lowercase			(       self      :     int			,		_A      :     str):
											if target_vertex == self.source_vertex:
																	return self.source_vertex

											A__	:							List[Any]						    = self.parent.get(_A)
											if target_vertex_parent is None:
																	A__	:							Union[str, Any]						    = (
																	    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																	)
																	raise ValueError(_A)

											return self.shortest_path(_A) + F'->{target_vertex}'


if __name__ == "__main__":
		snake_case    :						Any  =						Graph(graph, 'G')
		g.breath_first_search()
		print(g.shortest_path('D'))
		print(g.shortest_path('G'))
		print(g.shortest_path('Foo'))