




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

UpperCamelCase__    			=			logging.get_logger(__name__)


UpperCamelCase__    			=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCamelCase__    			=			{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

UpperCamelCase__    			=			{"""facebook/blenderbot-3B""": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	_a      (						):
					__lowerCAmelCase				   =       (
					    list(range(ord("!"     )      ,      ord("~"     ) + 1     )     ) + list(range(ord("¡"     )      ,      ord("¬"     ) + 1     )     ) + list(range(ord("®"     )      ,      ord("ÿ"     ) + 1     )     )
					)
					__lowerCAmelCase				   =       bs[:]
					__lowerCAmelCase				   =       0
					for b in range(2**8     ):
										if b not in bs:
															bs.append(SCREAMING_SNAKE_CASE_     )
															cs.append(2**8 + n     )
															n += 1
					__lowerCAmelCase				   =       [chr(SCREAMING_SNAKE_CASE_     ) for n in cs]
					return dict(zip(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]     ):
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)     )
										__lowerCAmelCase				   =       char
					return pairs


class 			a__    (					snake_case__      ):
						_a							:    str					=    VOCAB_FILES_NAMES
						_a							:    Union[str, Any]					=    PRETRAINED_VOCAB_FILES_MAP
						_a							:    List[Any]					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:    Union[str, Any]					=    ["""input_ids""", """attention_mask"""]






						def __init__(   self		,			_A		,			_A		,			_A="replace"		,			_A="<s>"		,			_A="</s>"		,			_A="</s>"		,			_A="<s>"		,			_A="<unk>"		,			_A="<pad>"		,			_A="<mask>"		,			_A=False		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else bos_token
											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else eos_token
											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else sep_token
											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else cls_token
											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else unk_token
											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCAmelCase				   =       AddedToken(_A		,			lstrip=_A		,			rstrip=_A			) if isinstance(_A		,			_A			) else mask_token

											super().__init__(
											    errors=_A		,			bos_token=_A		,			eos_token=_A		,			unk_token=_A		,			sep_token=_A		,			cls_token=_A		,			pad_token=_A		,			mask_token=_A		,			add_prefix_space=_A		,			**_A		,			)

											with open(_A		,			encoding="utf-8"			) as vocab_handle:
																__lowerCAmelCase				   =       json.load(_A			)
											__lowerCAmelCase				   =       {v: k for k, v in self.encoder.items()}
											__lowerCAmelCase				   =       errors  # how to handle errors in decoding
											__lowerCAmelCase				   =       bytes_to_unicode()
											__lowerCAmelCase				   =       {v: k for k, v in self.byte_encoder.items()}
											with open(_A		,			encoding="utf-8"			) as merges_handle:
																__lowerCAmelCase				   =       merges_handle.read().split("\n"			)[1:-1]
											__lowerCAmelCase				   =       [tuple(merge.split()			) for merge in bpe_merges]
											__lowerCAmelCase				   =       dict(zip(_A		,			range(len(_A			)			)			)			)
											__lowerCAmelCase				   =       {}
											__lowerCAmelCase				   =       add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											__lowerCAmelCase				   =       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"			)






						@property
						# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return len(self.encoder			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											if token in self.cache:
																return self.cache[token]
											__lowerCAmelCase				   =       tuple(_A			)
											__lowerCAmelCase				   =       get_pairs(_A			)

											if not pairs:
																return token

											while True:
																__lowerCAmelCase				   =       min(_A		,			key=lambda _A			: self.bpe_ranks.get(_A		,			float("inf"			)			)			)
																if bigram not in self.bpe_ranks:
																					break
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       bigram
																__lowerCAmelCase				   =       []
																__lowerCAmelCase				   =       0
																while i < len(_A			):
																					try:
																										__lowerCAmelCase				   =       word.index(_A		,			_A			)
																					except ValueError:
																										new_word.extend(word[i:]			)
																										break
																					else:
																										new_word.extend(word[i:j]			)
																										__lowerCAmelCase				   =       j

																					if word[i] == first and i < len(_A			) - 1 and word[i + 1] == second:
																										new_word.append(first + second			)
																										i += 2
																					else:
																										new_word.append(word[i]			)
																										i += 1
																__lowerCAmelCase				   =       tuple(_A			)
																__lowerCAmelCase				   =       new_word
																if len(_A			) == 1:
																					break
																else:
																					__lowerCAmelCase				   =       get_pairs(_A			)
											__lowerCAmelCase				   =       " ".join(_A			)
											__lowerCAmelCase				   =       word
											return word






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											for token in re.findall(self.pat		,			_A			):
																__lowerCAmelCase				   =       "".join(
																    self.byte_encoder[b] for b in token.encode("utf-8"			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A			).split(" "			)			)
											return bpe_tokens






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return self.encoder.get(_A		,			self.encoder.get(self.unk_token			)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return self.decoder.get(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "".join(_A			)
											__lowerCAmelCase				   =       bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"		,			errors=self.errors			)
											return text






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											if not os.path.isdir(_A			):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
																return
											__lowerCAmelCase				   =       os.path.join(
											    _A		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
											__lowerCAmelCase				   =       os.path.join(
											    _A		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

											with open(_A		,			"w"		,			encoding="utf-8"			) as f:
																f.write(json.dumps(self.encoder		,			indent=2		,			sort_keys=_A		,			ensure_ascii=_A			) + "\n"			)

											__lowerCAmelCase				   =       0
											with open(_A		,			"w"		,			encoding="utf-8"			) as writer:
																writer.write("#version: 0.2\n"			)
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,			key=lambda _A			: kv[1]			):
																					if index != token_index:
																										logger.warning(
																										    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																										    " Please check that the tokenizer is not corrupted!"			)
																										__lowerCAmelCase				   =       token_index
																					writer.write(" ".join(_A			) + "\n"			)
																					index += 1

											return vocab_file, merge_file






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = False			):




											"""simple docstring"""



											if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_A		,			token_ids_a=_A		,			already_has_special_tokens=_A			)

											if token_ids_a is None:
																return [1] + ([0] * len(_A			)) + [1]
											return [1] + ([0] * len(_A			)) + [1, 1] + ([0] * len(_A			)) + [1]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [self.sep_token_id]
											__lowerCAmelCase				   =       [self.cls_token_id]

											if token_ids_a is None:
																return len(cls + token_ids_a + sep			) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=False		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       kwargs.pop("add_prefix_space"		,			self.add_prefix_space			)
											if (is_split_into_words or add_prefix_space) and (len(_A			) > 0 and not text[0].isspace()):
																__lowerCAmelCase				   =       " " + text
											return (text, kwargs)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											return token_ids_a + [self.eos_token_id]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											for is_user, text in conversation.iter_texts():
																if is_user:
																					# We need to space prefix as it's being done within blenderbot
																					inputs.append(" " + text			)
																else:
																					# Generated responses should contain them already.
																					inputs.append(_A			)

											__lowerCAmelCase				   =       "  ".join(_A			)
											__lowerCAmelCase				   =       self.encode(_A			)
											if len(_A			) > self.model_max_length:
																__lowerCAmelCase				   =       input_ids[-self.model_max_length :]
																logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""			)
											return input_ids










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      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       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     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       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():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       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_      ,      )

										__lowerCAmelCase				   =       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:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """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()




