


'''simple docstring'''
__SCREAMING_SNAKE_CASE					:				Tuple									=		{
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		set()
							# keep track of all the paths to be checked
							A_			    =		[[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														A_			    =		queue.pop(0 )
														# get the last node from the path
														A_			    =		path[-1]
														if node not in explored:
																					A_			    =		graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												A_			    =		list(UpperCamelCase_ )
																												new_path.append(UpperCamelCase_ )
																												queue.append(UpperCamelCase_ )
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(UpperCamelCase_ )

    # in case there's no path between the 2 nodes
							return []







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							A_			    =		[start]
							A_			    =		set(UpperCamelCase_ )
							# Keep tab on distances from `start` node.
							A_			    =		{start: 0, target: -1}
							while queue:
														A_			    =		queue.pop(0 )
														if node == target:
																					A_			    =		(
																					    dist[node] if dist[target] == -1 else min(dist[target]						,  dist[node] )
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(UpperCamelCase_ )
																												queue.append(UpperCamelCase_ )
																												A_			    =		dist[node] + 1
							return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4









'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__SCREAMING_SNAKE_CASE					:				str									=		{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

__SCREAMING_SNAKE_CASE					:				List[Any]									=		{
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							with open(UpperCamelCase_						,  "r"						,  encoding="utf-8" ) as f:
														A_			    =		json.loads(f.read() )

							A_			    =		collections.OrderedDict()
							A_			    =		collections.OrderedDict()
							A_			    =		collections.OrderedDict()
							with open(UpperCamelCase_						,  "r"						,  encoding="utf-8" ) as f:
														A_			    =		f.readlines()
							A_			    =		[[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
							for idx, b in enumerate(UpperCamelCase_ ):
														A_			    =		b
														A_			    =		idx
														for wd in b:
																					A_			    =		idx

							return vocab, raw_vocab, ids_to_tokens, emoji






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       str   						=VOCAB_FILES_NAMES
						_UpperCAmelCase		:       str   						=PRETRAINED_VOCAB_FILES_MAP
						_UpperCAmelCase		:       Dict   						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCAmelCase		:       List[str]   						=["input_ids", "attention_mask"]






						def __init__(  self							:      str ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      List[Any]="<|endoftext|>" ,  lowerCAmelCase							:      Optional[int]="<|endoftext|>" ,  lowerCAmelCase							:      Tuple="<|startoftext|>" ,  lowerCAmelCase							:      Union[str, Any]="<|endoftext|>" ,  lowerCAmelCase							:      List[Any]=False ,  **lowerCAmelCase							:      Optional[Any] ,  ):
													super().__init__(
													    unk_token=lowerCAmelCase ,  pad_token=lowerCAmelCase ,  bos_token=lowerCAmelCase ,  eos_token=lowerCAmelCase ,  do_clean_text=lowerCAmelCase ,  **lowerCAmelCase ,  )
													if not os.path.isfile(lowerCAmelCase	):
																				raise ValueError(
																				    F"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
																				    " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
													if not os.path.isfile(lowerCAmelCase	):
																				raise ValueError(
																				    F"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
																				    " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
													A_			    =		do_clean_text
													A_    ,					A_    ,					A_    ,					A_			    =		load_vocab_and_emoji(lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		SubWordJapaneseTokenizer(
													    vocab=self.vocab ,  ids_to_tokens=self.ids_to_tokens ,  emoji=self.emoji	)






						@property
						def 				_UpperCAmelCase       (  self							:      str	):
													# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
													return len(self.raw_vocab	)






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													return dict(self.raw_vocab ,  **self.added_tokens_encoder	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Tuple	):
													return self.subword_tokenizer.tokenize(lowerCAmelCase ,  clean=self.do_clean_text	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      int	):
													return self.vocab.get(lowerCAmelCase ,  self.vocab.get(self.unk_token	)	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      List[str]	):
													return self.subword_tokenizer.convert_id_to_token(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		"".join(lowerCAmelCase	).strip()
													return out_string






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      "Conversation"	):
													A_			    =		[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(lowerCAmelCase ,  add_special_tokens=lowerCAmelCase	) + [self.eos_token_id]	)

													if len(lowerCAmelCase	) > self.model_max_length:
																				A_			    =		input_ids[-self.model_max_length :]
													return input_ids






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Optional[str] = None	):
													A_			    =		0
													if os.path.isdir(lowerCAmelCase	):
																				A_			    =		os.path.join(
																				    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
																				A_			    =		os.path.join(
																				    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]	)
													else:
																				A_			    =		(
																				    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
																				)
																				A_			    =		(
																				    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
																				)
													with open(lowerCAmelCase ,  "w" ,  encoding="utf-8"	) as writer:
																				for token_index, token in self.ids_to_tokens.items():
																											if index != token_index:
																																		logger.warning(
																																		    F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																																		    " Please check that the vocabulary is not corrupted!"	)
																																		A_			    =		token_index
																											writer.write(",".join(lowerCAmelCase	) + "\n"	)
																											index += 1
													with open(lowerCAmelCase ,  "w" ,  encoding="utf-8"	) as writer:
																				json.dump(self.emoji ,  lowerCAmelCase	)
													return vocab_file, emoji_file






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      List[Any]	):
													A_			    =		vocab  # same as swe
													A_			    =		ids_to_tokens  # same as bpe
													A_			    =		emoji
													A_			    =		np.max([len(lowerCAmelCase	) for w in self.vocab.keys()]	)
													A_			    =		re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"	)
													A_			    =		re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"	)
													A_			    =		re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"	)
													A_			    =		re.compile(
													    r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
													A_			    =		re.compile(
													    r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
													A_			    =		re.compile(
													    r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"	)
													A_			    =		"─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
													A_			    =		"▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
													A_			    =		str.maketrans({k: "<BLOCK>" for k in keisen + blocks}	)






						def __len__(  self							:      Union[str, Any]	):
													return len(self.ids_to_tokens	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      int	):
													A_			    =		self.content_repattera.sub("<URL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<EMAIL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<TEL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<PRICE>" ,  lowerCAmelCase	)
													A_			    =		content.translate(self.content_transa	)
													while "<BLOCK><BLOCK>" in content:
																				A_			    =		content.replace("<BLOCK><BLOCK>" ,  "<BLOCK>"	)
													return content






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Tuple=False	):
													A_			    =		text.replace(" " ,  "<SP>"	)
													A_			    =		text.replace("　" ,  "<SP>"	)
													A_			    =		text.replace("\r\n" ,  "<BR>"	)
													A_			    =		text.replace("\n" ,  "<BR>"	)
													A_			    =		text.replace("\r" ,  "<BR>"	)
													A_			    =		text.replace("\t" ,  "<TAB>"	)
													A_			    =		text.replace("—" ,  "ー"	)
													A_			    =		text.replace("−" ,  "ー"	)
													for k, v in self.emoji["emoji"].items():
																				if k in text:
																											A_			    =		text.replace(lowerCAmelCase ,  lowerCAmelCase	)
													if clean:
																				A_			    =		self.clean_text(lowerCAmelCase	)

													def check_simbol(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(lowerCAmelCase	) == 1 and len(lowerCAmelCase	) == 2:
																											A_			    =		(int(e[0]	) << 8) + int(e[1]	)
																											if (
																											    (c >= 0xC2A1 and c <= 0xC2BF)
																											    or (c >= 0xC780 and c <= 0xC783)
																											    or (c >= 0xCAB9 and c <= 0xCBBF)
																											    or (c >= 0xCC80 and c <= 0xCDA2)
																											):
																																		return True
																				return False

													def checkuae(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(lowerCAmelCase	) == 1 and len(lowerCAmelCase	) == 3:
																											A_			    =		(int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
																											if c >= 0xE2_8080 and c <= 0xE2_B07F:
																																		return True
																				return False

													A_			    =		0
													A_			    =		[]
													while pos < len(lowerCAmelCase	):
																				A_			    =		min(len(lowerCAmelCase	) ,  pos + self.maxlen + 1	) if text[pos] == "<" else pos + 3
																				A_			    =		[]  # (token_id, token, pos)
																				for e in range(lowerCAmelCase ,  lowerCAmelCase ,  -1	):
																											A_			    =		text[pos:e]
																											if wd in self.vocab:
																																		if wd[0] == "<" and len(lowerCAmelCase	) > 2:
																																									A_			    =		[(self.vocab[wd], wd, e)]
																																									break
																																		else:
																																									candidates.append((self.vocab[wd], wd, e)	)
																				if len(lowerCAmelCase	) > 0:
																											# the smallest token_id is adopted
																											A_    ,					A_    ,					A_			    =		sorted(lowerCAmelCase ,  key=lambda lowerCAmelCase	: x[0]	)[0]
																											result.append(lowerCAmelCase	)
																											A_			    =		e
																				else:
																											A_			    =		pos + 1
																											A_			    =		text[pos:end]
																											if check_simbol(lowerCAmelCase	):
																																		result.append("<KIGOU>"	)
																											elif checkuae(lowerCAmelCase	):
																																		result.append("<U2000U2BFF>"	)
																											else:
																																		for i in wd.encode("utf-8"	):
																																									result.append("<|byte%d|>" % i	)
																											A_			    =		end
													return result






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Union[str, Any]="\n"	):
													A_			    =		[]
													A_			    =		[]
													A_			    =		self.ids_to_tokens[index][0]
													if word[:6] == "<|byte" and word[-2:] == "|>":
																				byte_tokens.append(int(word[6:-2]	)	)
													else:
																				if len(lowerCAmelCase	) > 0:
																											words.append(bytearray(lowerCAmelCase	).decode("utf-8" ,  errors="replace"	)	)
																											A_			    =		[]
																				if word[:7] == "<|emoji" and word[-2:] == "|>":
																											words.append(self.emoji["emoji_inv"][word]	)
																				elif word == "<SP>":
																											words.append(" "	)
																				elif word == "<BR>":
																											words.append(lowerCAmelCase	)
																				elif word == "<TAB>":
																											words.append("\t"	)
																				elif word == "<BLOCK>":
																											words.append("▀"	)
																				elif word == "<KIGOU>":
																											words.append("ǀ"	)
																				elif word == "<U2000U2BFF>":
																											words.append("‖"	)
																				else:
																											words.append(lowerCAmelCase	)
													if len(lowerCAmelCase	) > 0:
																				words.append(bytearray(lowerCAmelCase	).decode("utf-8" ,  errors="replace"	)	)
													A_			    =		"".join(lowerCAmelCase	)
													return text





