
"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	str						=     None
     __snake_case:	Union[str, Any]						=     BloomTokenizerFast
     __snake_case:	Any						=     BloomTokenizerFast
     __snake_case:	List[Any]						=     True
     __snake_case:	Optional[Any]						=     False
     __snake_case:	str						=     'tokenizer_file'
     __snake_case:	List[str]						=     {'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         super().setUp()
         a__		=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'   )
         tokenizer.save_pretrained(self.tmpdirname   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     List[str]:







         """simple docstring"""



         kwargs.update(self.special_tokens_map   )
         return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         a__		=			self.get_rust_tokenizer()

         a__		=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
         a__		=			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

         a__		=			tokenizer.batch_encode_plus(__SCREAMING_SNAKE_CASE   )['input_ids']
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=6   )		->     List[Any]:







         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                 a__		=			self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )
                 # tokenizer_r.pad_token = None # Hotfixing padding = None
                 # Simple input
                 a__		=			'This is a simple input'
                 a__		=			['This is a simple input 1', 'This is a simple input 2']
                 a__		=			('This is a simple input', 'This is a pair')
                 a__		=			[
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 try:
                     tokenizer_r.encode(__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE   )
                     tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE   )

                     tokenizer_r.batch_encode_plus(__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE   )
                     tokenizer_r.encode(__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE   )
                     tokenizer_r.batch_encode_plus(__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE   )
                 except ValueError:
                     self.fail('Bloom Tokenizer should be able to deal with padding'   )

                 a__		=			None  # Hotfixing padding = None
                 self.assertRaises(__SCREAMING_SNAKE_CASE     ,				tokenizer_r.encode     ,				__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE     ,				padding='max_length'   )

                 # Simple input
                 self.assertRaises(__SCREAMING_SNAKE_CASE     ,				tokenizer_r.encode_plus     ,				__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE     ,				padding='max_length'   )

                 # Simple input
                 self.assertRaises(
                     __SCREAMING_SNAKE_CASE     ,				tokenizer_r.batch_encode_plus     ,				__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE     ,				padding='max_length'     ,				)

                 # Pair input
                 self.assertRaises(__SCREAMING_SNAKE_CASE     ,				tokenizer_r.encode     ,				__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE     ,				padding='max_length'   )

                 # Pair input
                 self.assertRaises(__SCREAMING_SNAKE_CASE     ,				tokenizer_r.encode_plus     ,				__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE     ,				padding='max_length'   )

                 # Pair input
                 self.assertRaises(
                     __SCREAMING_SNAKE_CASE     ,				tokenizer_r.batch_encode_plus     ,				__SCREAMING_SNAKE_CASE     ,				max_length=__SCREAMING_SNAKE_CASE     ,				padding='max_length'     ,				)



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.get_rust_tokenizer()
         a__		=			load_dataset('xnli'     ,				'all_languages'     ,				split='test'     ,				streaming=__SCREAMING_SNAKE_CASE   )

         a__		=			next(iter(__SCREAMING_SNAKE_CASE   )   )['premise']  # pick up one data
         a__		=			list(sample_data.values()   )

         a__		=			list(map(tokenizer.encode     ,				__SCREAMING_SNAKE_CASE   )   )
         a__		=			[tokenizer.decode(__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   ) for x in output_tokens]
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map   )     ,				1   )
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()   )[0]   )     ,				1   )




"""simple docstring"""




from __future__ import annotations

import os
from collections.abc import Mapping

a		:       Optional[Any]     						=       tuple[int, int]







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			vertices
         a__		=			{
             (min(__SCREAMING_SNAKE_CASE   ), max(__SCREAMING_SNAKE_CASE   )): weight for edge, weight in edges.items()
         }



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         self.vertices.add(edge[0]   )
         self.vertices.add(edge[1]   )
         a__		=			weight




     def 			lowercase__  (							self   )		->     Graph:







         """simple docstring"""



         a__		=			Graph({min(self.vertices   )}     ,				{}   )
         a__		=			42
         a__		=			42
         a__		=			42
         a__		=			42

         while len(subgraph.vertices   ) < len(self.vertices   ):
             a__		=			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:
                         a__		=			edge
                         a__		=			weight

             subgraph.add_edge(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         return subgraph




def     __magic_name__		(					UpperCamelCase     :		str = "p107_network.txt"     )     ->						int:
    a__		=			os.path.abspath(os.path.dirname(UpperCamelCase     )     )
    a__		=			os.path.join(UpperCamelCase							,		UpperCamelCase     )
    a__		=			{}
    a__		=			42
    a__		=			42
    a__		=			42

    with open(UpperCamelCase     ) as f:
        a__		=			f.read().strip().split('\n'     )

    a__		=			[line.split(','     ) for line in data]

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

    a__		=			Graph(set(range(len(UpperCamelCase     )     )     )							,		UpperCamelCase     )

    a__		=			graph.prims_algorithm()

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

    return initial_total - optimal_total


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


