





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_albert import AlbertTokenizer
else:
					UpperCAmelCase_									=       None

UpperCAmelCase_									=       logging.get_logger(__name__)
UpperCAmelCase_									=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_									=       {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase_									=       {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

UpperCAmelCase_									=       """▁"""
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		AlbertTokenizer




					def __init__(						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=True			,  lowerCAmelCase_=True			,  lowerCAmelCase_=False			,  lowerCAmelCase_="[CLS]"			,  lowerCAmelCase_="[SEP]"			,  lowerCAmelCase_="<unk>"			,  lowerCAmelCase_="[SEP]"			,  lowerCAmelCase_="<pad>"			,  lowerCAmelCase_="[CLS]"			,  lowerCAmelCase_="[MASK]"			,  **lowerCAmelCase_			,  )						->							Dict:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										_snake_case													=			(
										    AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_			,  normalized=lowerCAmelCase_					)
										    if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					)
										    else mask_token
										)

										super().__init__(
										    lowerCAmelCase_			,  tokenizer_file=lowerCAmelCase_			,  do_lower_case=lowerCAmelCase_			,  remove_space=lowerCAmelCase_			,  keep_accents=lowerCAmelCase_			,  bos_token=lowerCAmelCase_			,  eos_token=lowerCAmelCase_			,  unk_token=lowerCAmelCase_			,  sep_token=lowerCAmelCase_			,  pad_token=lowerCAmelCase_			,  cls_token=lowerCAmelCase_			,  mask_token=lowerCAmelCase_			,  **lowerCAmelCase_			,  )

										_snake_case													=			do_lower_case
										_snake_case													=			remove_space
										_snake_case													=			keep_accents
										_snake_case													=			vocab_file
										_snake_case													=			False if not self.vocab_file else True




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										_snake_case													=			[self.sep_token_id]
										_snake_case													=			[self.cls_token_id]
										if token_ids_a is None:
															return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										_snake_case													=			[self.sep_token_id]
										_snake_case													=			[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					) * [0] + len(token_ids_a + sep					) * [1]






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							Tuple[str]:
										if not self.can_save_slow_tokenizer:
															raise ValueError(
															    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
															    'tokenizer.'					)

										if not os.path.isdir(lowerCAmelCase_					):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
										_snake_case													=			os.path.join(
										    lowerCAmelCase_			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

										if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					):
															copyfile(self.vocab_file			,  lowerCAmelCase_					)

										return (out_vocab_file,)










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			9, 14  # noqa: F841
					_snake_case													=			[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_snake_case													=			defaultdict(UpperCamelCase__  )
					for nodea, nodea, cost in edges:
										adjancency[nodea].append([nodea, cost]  )
										adjancency[nodea].append([nodea, cost]  )

					_snake_case													=			mst(UpperCamelCase__  )

					_snake_case													=			[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					for answer in expected:
										_snake_case													=			tuple(answer[:2]  )
										_snake_case													=			tuple(edge[::-1]  )
										assert edge in result or reverse in result



