






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE__   :      List[str]				=		50_00_00

SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		os.path.split(__file__)
SCREAMING_SNAKE_CASE__   :      Optional[int]				=		os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))

@get_duration
def   __lowercase       (			snake_case,	**snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   dataset.map(**snake_case			)

@get_duration
def   __lowercase       (			snake_case,	**snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   dataset.filter(**snake_case			)

def   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:Any   =   {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								__magic_name__		:Tuple   =   datasets.Features({'''text''': datasets.Value('''string'''			), '''numbers''': datasets.Value('''float32'''			)}			)
								__magic_name__		:Union[str, Any]   =   generate_example_dataset(
								    os.path.join(snake_case,	'''dataset.arrow'''			),	snake_case,	num_examples=snake_case			)

								__magic_name__		:int   =   transformers.AutoTokenizer.from_pretrained('''bert-base-cased''',	use_fast=snake_case			)

								def tokenize(snake_case			):
												return tokenizer(examples['''text''']			)

								__magic_name__		:str   =   map(snake_case			)

								__magic_name__		:Tuple   =   map(snake_case,	batched=snake_case			)

								__magic_name__		:Optional[int]   =   map(snake_case,	function=lambda snake_case			: None,	batched=snake_case			)

								with dataset.formatted_as(type='''numpy'''			):
												__magic_name__		:str   =   map(snake_case,	function=lambda snake_case			: None,	batched=snake_case			)

								with dataset.formatted_as(type='''pandas'''			):
												__magic_name__		:Any   =   map(snake_case,	function=lambda snake_case			: None,	batched=snake_case			)

								with dataset.formatted_as(type='''torch''',	columns='''numbers'''			):
												__magic_name__		:List[Any]   =   map(snake_case,	function=lambda snake_case			: None,	batched=snake_case			)

								with dataset.formatted_as(type='''tensorflow''',	columns='''numbers'''			):
												__magic_name__		:Optional[Any]   =   map(snake_case,	function=lambda snake_case			: None,	batched=snake_case			)

								__magic_name__		:str   =   map(snake_case,	function=snake_case,	batched=snake_case			)

								__magic_name__		:Tuple   =   filter(snake_case			)

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(snake_case,	'''wb'''			) as f:
								f.write(json.dumps(snake_case			).encode('''utf-8'''			)			)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_map_filter()













'''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() = }""")

