



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

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


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	snake_case__       :  Dict     		= None


snake_case__       :  Dict     		= logging.get_logger(__name__)

snake_case__       :  Optional[Any]     		= {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case__       :  Any     		= {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
snake_case__       :  Dict     		= {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

class 	_A (			A__						):


						'''simple docstring'''





						_snake_case      :  List[Any]   				=   VOCAB_FILES_NAMES
						_snake_case      :  Any   				=   PRETRAINED_VOCAB_FILES_MAP
						_snake_case      :  int   				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_snake_case      :  Optional[Any]   				=   ["""input_ids""", """attention_mask"""]
						_snake_case      :  Any   				=   TaTokenizer

						_snake_case      :  List[Any]   				=   []



						def __init__(						self						:				List[Any]				,   lowerCamelCase						:				List[Any]=None				,   lowerCamelCase						:				List[Any]=None				,   lowerCamelCase						:				List[Any]="</s>"				,   lowerCamelCase						:				Any="<unk>"				,   lowerCamelCase						:				Any="<pad>"				,   lowerCamelCase						:				Tuple=100				,   lowerCamelCase						:				Any=None				,   **lowerCamelCase						:				int				,   ):






							'''simple docstring'''



							if extra_ids > 0 and additional_special_tokens is None:
								__lowercase									=      [f"""<extra_id_{i}>""" for i in range(__snake_case						)]
							elif extra_ids > 0 and additional_special_tokens is not None:
								# Check that we have the right number of extra special tokens
								__lowercase									=      len(set(filter(lambda lowerCamelCase						: bool("extra_id_" in str(__snake_case						)						)				,   __snake_case						)						)						)
								if extra_tokens != extra_ids:
									raise ValueError(
									    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
									    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
									    " tokens"						)

							super().__init__(
							    __snake_case				,   tokenizer_file=__snake_case				,   eos_token=__snake_case				,   unk_token=__snake_case				,   pad_token=__snake_case				,   extra_ids=__snake_case				,   additional_special_tokens=__snake_case				,   **__snake_case				,   )

							__lowercase									=      vocab_file
							__lowercase									=      False if not self.vocab_file else True
							__lowercase									=      extra_ids



						@staticmethod
						def      _snake_case						(						lowerCamelCase						:				List[str]				,   lowerCamelCase						:				Tuple				,   lowerCamelCase						:				Any						):






							'''simple docstring'''



							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
								__lowercase									=      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
								if init_max_model_length is not None and init_max_model_length != max_model_length:
									return init_max_model_length
								elif init_max_model_length is None:
									warnings.warn(
									    "This tokenizer was incorrectly instantiated with a model max length of"
									    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
									    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
									    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
									    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
									    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
									    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
									    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
									    " instantiate this tokenizer with `model_max_length` set to your preferred value."				,   __snake_case				,   )

							return max_model_length



						def      _snake_case						(						self						:				str				,   lowerCamelCase						:				str				,   lowerCamelCase						:				Optional[str] = None						):






							'''simple docstring'''



							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(__snake_case						):
								logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
								return
							__lowercase									=      os.path.join(
							    __snake_case				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(__snake_case						):
								copyfile(self.vocab_file				,   __snake_case						)
								logger.info(f"""Copy vocab file to {out_vocab_file}"""						)

							return (out_vocab_file,)



						def      _snake_case						(						self						:				Tuple				,   lowerCamelCase						:				List[int]				,   lowerCamelCase						:				Optional[List[int]] = None						):






							'''simple docstring'''



							__lowercase									=      token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
								return self.prefix_tokens + token_ids_a
							else:
								__lowercase									=      token_ids_a + [self.eos_token_id]
								return self.prefix_tokens + token_ids_a + token_ids_a



						def      _snake_case						(						self						:				int				,   lowerCamelCase						:				List[int]				,   lowerCamelCase						:				Optional[List[int]] = None						):






							'''simple docstring'''



							__lowercase									=      [self.eos_token_id]

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



						def      _snake_case						(						self						:				List[str]						):






							'''simple docstring'''



							return list(
							    set(filter(lambda lowerCamelCase						: bool(re.search(R"<extra_id_\d+>"				,   __snake_case						)						) is not None				,   self.additional_special_tokens						)						)						)



						def      _snake_case						(						self						:				List[str]						):






							'''simple docstring'''



							return [self.convert_tokens_to_ids(__snake_case						) for token in self.get_sentinel_tokens()]










'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			set[int]   ,       __snake_case							:			Mapping[EdgeT, int]						)->	None:
        snake_case 						=   vertices
        snake_case 						=   {
            (min(__snake_case						), max(__snake_case						)): weight for edge, weight in edges.items()
        }



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	Graph:
        snake_case 						=   Graph({min(self.vertices						)}   ,       {}						)
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42

        while len(subgraph.vertices						) < len(self.vertices						):
           snake_case 						=   max(self.edges.values()						) + 1
           for edge, weight in self.edges.items():
              if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                 if weight < min_weight:
                    snake_case 						=   edge
                    snake_case 						=   weight

           subgraph.add_edge(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [line.split(""","""		) for line in data]

   for edgea in range(1      ,    len(__lowerCAmelCase		)		):
      for edgea in range(__lowerCAmelCase		):
         if adjaceny_matrix[edgea][edgea] != "-":
            snake_case 						=   int(adjaceny_matrix[edgea][edgea]		)

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   snake_case 						=   graph.prims_algorithm()

   snake_case 						=   sum(graph.edges.values()		)
   snake_case 						=   sum(subgraph.edges.values()		)

   return initial_total - optimal_total


if __name__ == "__main__":
 print(F"""{solution() = }""")

