
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_UpperCAmelCase					: Any      =       logging.get_logger(__name__)


def       A       (					lowercase=None    ,       lowercase=None )   ->  Optional[int]:
		'''simple docstring'''

		return field(default_factory=lambda: default    ,       metadata=lowercase )


@dataclass
class 				lowercase					:
		__lowercase       :       List[str]	     =				list_field(
		    default=[]  ,   metadata={
		        "help": (
		            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
		            " of all available models"
		        )
		    }  ,   )

		__lowercase       :       List[int]	     =				list_field(
		    default=[8]  ,   metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

		__lowercase       :       List[int]	     =				list_field(
		    default=[8, 32, 128, 512]  ,   metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}  ,   )

		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}  ,   )
		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}  ,   )
		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Use FP16 to accelerate inference."}   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Benchmark training of model"}   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Verbose memory tracing"}   )
		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}  ,   )
		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={
		        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
		    }  ,   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Trace memory line by line"}   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Save result to a CSV file"}   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Save all print statements in a log file"}   )
		__lowercase       :       bool	     =				field(default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Whether to print environment information"}   )
		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={
		        "help": (
		            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
		            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
		            " for debugging / testing and on TPU."
		        )
		    }  ,   )
		__lowercase       :       str	     =				field(
		    default=f"inference_time_{round(time()   )}.csv"  ,   metadata={"help": "CSV filename used if saving time results to csv."}  ,   )
		__lowercase       :       str	     =				field(
		    default=f"inference_memory_{round(time()   )}.csv"  ,   metadata={"help": "CSV filename used if saving memory results to csv."}  ,   )
		__lowercase       :       str	     =				field(
		    default=f"train_time_{round(time()   )}.csv"  ,   metadata={"help": "CSV filename used if saving time results to csv for training."}  ,   )
		__lowercase       :       str	     =				field(
		    default=f"train_memory_{round(time()   )}.csv"  ,   metadata={"help": "CSV filename used if saving memory results to csv for training."}  ,   )
		__lowercase       :       str	     =				field(
		    default=f"env_info_{round(time()   )}.csv"  ,   metadata={"help": "CSV filename used if saving environment information."}  ,   )
		__lowercase       :       str	     =				field(
		    default=f"log_{round(time()   )}.csv"  ,   metadata={"help": "Log filename used if print statements are saved in log."}  ,   )
		__lowercase       :       int	     =				field(default=3  ,   metadata={"help": "Times an experiment will be run."}   )
		__lowercase       :       bool	     =				field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={
		        "help": (
		            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
		            " model weights."
		        )
		    }  ,   )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				warnings.warn(
				    F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
				    ' are deprecated in general and it is advised to use external Benchmarking libraries '
				    ' to benchmark Transformer models.'				,      A_				,      )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				return json.dumps(dataclasses.asdict(self       )				,      indent=2       )



		@property
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				if len(self.models       ) <= 0:
						raise ValueError(
						    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
						    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'       )
				return self.models



		@property
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				if not self.multi_process:
						return False
				elif self.is_tpu:
						logger.info('Multiprocessing is currently not possible on TPU.'       )
						return False
				else:
						return True


from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCAmelCase					: Tuple      =       tuple[int, int]


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     vertices
				UpperCamelCase		=     {
				    (min(A_       ), max(A_       )): weight for edge, weight in edges.items()
				}



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				self.vertices.add(edge[0]       )
				self.vertices.add(edge[1]       )
				UpperCamelCase		=     weight



		def __UpperCamelCase ( self       )  ->					Graph:
				"""simple docstring"""
				UpperCamelCase		=     Graph({min(self.vertices       )}				,      {}       )
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42

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

						subgraph.add_edge(A_				,      A_       )

				return subgraph






def       A       (					lowercase = "p107_network.txt" )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     os.path.abspath(os.path.dirname(lowercase ) )
		UpperCamelCase		=     os.path.join(lowercase    ,       lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     42
		UpperCamelCase		=     42
		UpperCamelCase		=     42

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

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

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

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

		UpperCamelCase		=     graph.prims_algorithm()

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

		return initial_total - optimal_total


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