




'''simple docstring'''


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case   :   List[Any]     							=	logging.get_logger(__name__)

_snake_case   :   Optional[Any]     							=	'▁'

_snake_case   :   int     							=	{'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

_snake_case   :   Tuple     							=	{
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

_snake_case   :   Any     							=	{
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

_snake_case   :   Optional[Any]     							=	{
    'ernie-m-base': 514,
    'ernie-m-large': 514,
}

_snake_case   :   str     							=	{
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}

class     A					(	_a					):
							lowercase_           =       ['input_ids']

							lowercase_           =       VOCAB_FILES_NAMES
							lowercase_           =       PRETRAINED_INIT_CONFIGURATION
							lowercase_           =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_           =       PRETRAINED_VOCAB_FILES_MAP
							lowercase_           =       RESOURCE_FILES_NAMES







							def __init__(						self :						int							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						Tuple=None							,    lowerCAmelCase_ :						int=False							,    lowerCAmelCase_ :						Optional[int]="utf8"							,    lowerCAmelCase_ :						Union[str, Any]="[UNK]"							,    lowerCAmelCase_ :						Optional[int]="[SEP]"							,    lowerCAmelCase_ :						Tuple="[PAD]"							,    lowerCAmelCase_ :						Tuple="[CLS]"							,    lowerCAmelCase_ :						Union[str, Any]="[MASK]"							,    lowerCAmelCase_ :						Optional[Dict[str, Any]] = None							,    **lowerCAmelCase_ :						Tuple							,    )    ->							None:


														"""simple docstring"""


														_a			  =       {} if sp_model_kwargs is None else sp_model_kwargs
														super().__init__(
														    do_lower_case=lowerCAmelCase_							,    unk_token=lowerCAmelCase_							,    sep_token=lowerCAmelCase_							,    pad_token=lowerCAmelCase_							,    cls_token=lowerCAmelCase_							,    mask_token=lowerCAmelCase_							,    vocab_file=lowerCAmelCase_							,    encoding=lowerCAmelCase_							,    sp_model_kwargs=self.sp_model_kwargs							,    **lowerCAmelCase_							,    )
														_a			  =       do_lower_case
														_a			  =       sentencepiece_model_ckpt
														_a			  =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
														self.sp_model.Load(lowerCAmelCase_      )

														# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
														if vocab_file is not None:
																					_a			  =       self.load_vocab(filepath=lowerCAmelCase_      )
														else:
																					_a			  =       {self.sp_model.id_to_piece(lowerCAmelCase_      ): id for id in range(self.sp_model.get_piece_size()      )}
														_a			  =       {v: k for k, v in self.vocab.items()}







							def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						int      )    ->							Dict:


														"""simple docstring"""


														if text is None:
																					return None

														_a			  =       self.tokenize(lowerCAmelCase_      )
														_a  ,							_a			  =       '''''', []

														for i, ch in enumerate(lowerCAmelCase_      ):
																					if ch in self.SP_CHAR_MAPPING:
																												_a			  =       self.SP_CHAR_MAPPING.get(lowerCAmelCase_      )
																					else:
																												_a			  =       unicodedata.normalize('''NFKC'''							,    lowerCAmelCase_      )
																					if self.is_whitespace(lowerCAmelCase_      ):
																												continue
																					normalized_text += ch
																					char_mapping.extend([i] * len(lowerCAmelCase_      )      )

														_a  ,							_a  ,							_a			  =       normalized_text, [], 0

														if self.do_lower_case:
																					_a			  =       text.lower()

														for token in split_tokens:
																					if token[:1] == "▁":
																												_a			  =       token[1:]
																					_a			  =       text[offset:].index(lowerCAmelCase_      ) + offset
																					_a			  =       start + len(lowerCAmelCase_      )

																					token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)      )
																					_a			  =       end
														return token_mapping







							@property
							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														return len(self.vocab      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Any:


														"""simple docstring"""


														return dict(self.vocab							,    **self.added_tokens_encoder      )







							def __getstate__(						self :						List[Any]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.__dict__.copy()
														_a			  =       None
														return state







							def __setstate__(						self :						List[Any]							,    lowerCAmelCase_ :						int      )    ->							Any:


														"""simple docstring"""


														_a			  =       d

														# for backward compatibility
														if not hasattr(self							,    '''sp_model_kwargs'''      ):
																					_a			  =       {}

														_a			  =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
														self.sp_model.Load(self.sentencepiece_model_ckpt      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						Tuple      )    ->							Union[str, Any]:


														"""simple docstring"""


														return "".join((self.SP_CHAR_MAPPING.get(lowerCAmelCase_							,    lowerCAmelCase_      ) for c in text)      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						List[Any]=False							,    lowerCAmelCase_ :						List[Any]=64							,    lowerCAmelCase_ :						Optional[int]=0.1      )    ->							Optional[Any]:


														"""simple docstring"""


														if self.sp_model_kwargs.get('''enable_sampling'''      ) is True:
																					_a			  =       True
														if self.sp_model_kwargs.get('''alpha'''      ) is not None:
																					_a			  =       self.sp_model_kwargs.get('''alpha'''      )
														if self.sp_model_kwargs.get('''nbest_size'''      ) is not None:
																					_a			  =       self.sp_model_kwargs.get('''nbest_size'''      )

														if not enable_sampling:
																					_a			  =       self.sp_model.EncodeAsPieces(lowerCAmelCase_      )
														else:
																					_a			  =       self.sp_model.SampleEncodeAsPieces(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )
														_a			  =       []
														for pi, piece in enumerate(lowerCAmelCase_      ):
																					if piece == SPIECE_UNDERLINE:
																												if not pieces[pi + 1].startswith(lowerCAmelCase_      ) and pi != 0:
																																			new_pieces.append(lowerCAmelCase_      )
																																			continue
																												else:
																																			continue
																					_a			  =       0
																					for i, chunk in enumerate(lowerCAmelCase_      ):
																												if chunk == SPIECE_UNDERLINE:
																																			continue
																												if self.is_ch_char(lowerCAmelCase_      ) or self.is_punct(lowerCAmelCase_      ):
																																			if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																																										new_pieces.append(piece[lst_i:i]      )
																																			new_pieces.append(lowerCAmelCase_      )
																																			_a			  =       i + 1
																												elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
																																			if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																																										new_pieces.append(piece[lst_i:i]      )
																																			_a			  =       i
																												elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
																																			if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																																										new_pieces.append(piece[lst_i:i]      )
																																			_a			  =       i
																					if len(lowerCAmelCase_      ) > lst_i:
																												new_pieces.append(piece[lst_i:]      )
														return new_pieces







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						List[str]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       ''''''.join(lowerCAmelCase_      ).replace(lowerCAmelCase_							,    ''' '''      ).strip()
														return out_string







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Dict      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.convert_ids_to_tokens(lowerCAmelCase_      )
														_a			  =       ''''''.join(lowerCAmelCase_      ).replace(lowerCAmelCase_							,    ''' '''      ).strip()
														return out_string







							def      __lowerCAmelCase    (						self :						Tuple							,    lowerCAmelCase_ :						Optional[int]      )    ->							Optional[int]:


														"""simple docstring"""


														return self.vocab.get(lowerCAmelCase_							,    self.vocab.get(self.unk_token      )      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						List[str]      )    ->							Tuple:


														"""simple docstring"""


														return self.reverse_vocab.get(lowerCAmelCase_							,    self.unk_token      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						List[str]=None      )    ->							Union[str, Any]:


														"""simple docstring"""


														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_a			  =       [self.cls_token_id]
														_a			  =       [self.sep_token_id]
														return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Optional[int]=None      )    ->							List[str]:


														"""simple docstring"""


														if offset_mapping_a is None:
																					return [(0, 0)] + offset_mapping_a + [(0, 0)]

														return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						str=None							,    lowerCAmelCase_ :						List[str]=False      )    ->							Dict:


														"""simple docstring"""


														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''      )
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

														if token_ids_a is not None:
																					return [1] + ([0] * len(lowerCAmelCase_      )) + [1, 1] + ([0] * len(lowerCAmelCase_      )) + [1]
														return [1] + ([0] * len(lowerCAmelCase_      )) + [1]







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						List[int]							,    lowerCAmelCase_ :						Optional[List[int]] = None      )    ->							List[int]:


														"""simple docstring"""


														if token_ids_a is None:
																					# [CLS] X [SEP]
																					return (len(lowerCAmelCase_      ) + 2) * [0]

														# [CLS] A [SEP] [SEP] B [SEP]
														return [0] * (len(lowerCAmelCase_      ) + 1) + [1] * (len(lowerCAmelCase_      ) + 3)







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int      )    ->							Dict:


														"""simple docstring"""


														if "\u4e00" <= char <= "\u9fff":
																					return True
														return False







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						List[Any]      )    ->							Tuple:


														"""simple docstring"""


														if ("a" <= char <= "z") or ("A" <= char <= "Z"):
																					return True
														return False







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						Dict      )    ->							Any:


														"""simple docstring"""


														if char in ",;:.?!~，；：。？！《》【】":
																					return True
														return False







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							List[str]:


														"""simple docstring"""


														if char == " " or char == "\t" or char == "\n" or char == "\r":
																					return True
														if len(lowerCAmelCase_      ) == 1:
																					_a			  =       unicodedata.category(lowerCAmelCase_      )
																					if cat == "Zs":
																												return True
														return False







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						Tuple      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       {}
														with io.open(lowerCAmelCase_							,    '''r'''							,    encoding='''utf-8'''      ) as f:
																					for index, line in enumerate(lowerCAmelCase_      ):
																												_a			  =       line.rstrip('''\n'''      )
																												_a			  =       int(lowerCAmelCase_      )

														return token_to_idx







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						Optional[str] = None      )    ->							Tuple[str]:


														"""simple docstring"""


														_a			  =       0
														if os.path.isdir(lowerCAmelCase_      ):
																					_a			  =       os.path.join(
																					    lowerCAmelCase_							,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
														else:
																					_a			  =       (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
														with open(lowerCAmelCase_							,    '''w'''							,    encoding='''utf-8'''      ) as writer:
																					for token, token_index in sorted(self.vocab.items()							,    key=lambda lowerCAmelCase_      : kv[1]      ):
																												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(token + '''\n'''      )
																												index += 1

														_a			  =       os.path.join(lowerCAmelCase_							,    '''sentencepiece.bpe.model'''      )
														with open(lowerCAmelCase_							,    '''wb'''      ) as fi:
																					_a			  =       self.sp_model.serialized_model_proto()
																					fi.write(lowerCAmelCase_      )

														return (vocab_file,)







'''simple docstring'''


_snake_case   :   Any     							=	{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_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 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							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

