
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_a   :      Optional[int]       =				getLogger(__name__)


_a   :      Dict       =				"""cuda""" if torch.cuda.is_available() else """cpu"""
def 					a__ ( a :					List[str]							,  a :					str							,  a :					str							,  a :					int = 8							,  a :					str = DEFAULT_DEVICE							,  a :					Tuple=False							,  a :					Optional[Any]="summarization"							,  a :					Any=None							,  **a :					str							,  ):

  """simple docstring"""







  _snake_case  :    Optional[int]            = Path(a		).open("w"							,  encoding="utf-8"		)
  _snake_case  :    Union[str, Any]            = str(a		)
  _snake_case  :    int            = AutoModelForSeqaSeqLM.from_pretrained(a		).to(a		)
  if fpaa:
    _snake_case  :    Dict            = model.half()

  _snake_case  :    int            = AutoTokenizer.from_pretrained(a		)
  logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'		)  # if this is wrong, check config.model_type.

  _snake_case  :    Tuple            = time.time()
  # update config with task specific params
  use_task_specific_params(a							,  a		)
  if prefix is None:
    _snake_case  :    Dict            = prefix or getattr(model.config							,  "prefix"							,  ""		) or ""
  for examples_chunk in tqdm(list(chunks(a							,  a		)		)		):
    _snake_case  :    int            = [prefix + text for text in examples_chunk]
    _snake_case  :    List[str]            = tokenizer(a							,  return_tensors="pt"							,  truncation=a							,  padding="longest"		).to(a		)
    _snake_case  :    Union[str, Any]            = model.generate(
        input_ids=batch.input_ids							,  attention_mask=batch.attention_mask							,  **a							,  )
    _snake_case  :    int            = tokenizer.batch_decode(a							,  skip_special_tokens=a							,  clean_up_tokenization_spaces=a		)
    for hypothesis in dec:
      fout.write(hypothesis + "\n"		)
      fout.flush()
  fout.close()
  _snake_case  :    int            = int(time.time() - start_time		)  # seconds
  _snake_case  :    Dict            = len(a		)
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,  4		)}
def 					a__ ( ):

  """simple docstring"""







  return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"		)
def 					a__ ( a :					Union[str, Any]=True		):

  """simple docstring"""







  _snake_case  :    List[Any]            = argparse.ArgumentParser()
  parser.add_argument("model_name"							,  type=a							,  help="like facebook/bart-large-cnn,t5-base, etc."		)
  parser.add_argument("input_path"							,  type=a							,  help="like cnn_dm/test.source"		)
  parser.add_argument("save_path"							,  type=a							,  help="where to save summaries"		)
  parser.add_argument("--reference_path"							,  type=a							,  required=a							,  help="like cnn_dm/test.target"		)
  parser.add_argument("--score_path"							,  type=a							,  required=a							,  default="metrics.json"							,  help="where to save metrics"		)
  parser.add_argument("--device"							,  type=a							,  required=a							,  default=a							,  help="cuda, cuda:1, cpu etc."		)
  parser.add_argument(
      "--prefix"							,  type=a							,  required=a							,  default=a							,  help="will be added to the begininng of src examples"		)
  parser.add_argument("--task"							,  type=a							,  default="summarization"							,  help="used for task_specific_params + metrics"		)
  parser.add_argument("--bs"							,  type=a							,  default=8							,  required=a							,  help="batch size"		)
  parser.add_argument(
      "--n_obs"							,  type=a							,  default=-1							,  required=a							,  help="How many observations. Defaults to all."		)
  parser.add_argument("--fp16"							,  action="store_true"		)
  parser.add_argument("--dump-args"							,  action="store_true"							,  help="print the custom hparams with the results"		)
  parser.add_argument(
      "--info"							,  nargs="?"							,  type=a							,  const=datetime_now()							,  help=(
          "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
          " lang=en-ru. If no value is passed, the current datetime string will be used."
      )							,  )
  # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  _snake_case      ,       _snake_case  :    int            = parser.parse_known_args()
  _snake_case  :    Dict            = parse_numeric_n_bool_cl_kwargs(a		)
  if parsed_args and verbose:
    print(f'parsed the following generate kwargs: {parsed_args}'		)
  with open(args.input_path		) as f:
    _snake_case  :    str            = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    _snake_case  :    List[Any]            = examples[: args.n_obs]
  Path(args.save_path		).parent.mkdir(exist_ok=a		)
  if args.reference_path is None and Path(args.score_path		).exists():
    warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'		)
  _snake_case  :    Optional[Any]            = generate_summaries_or_translations(
      a							,  args.save_path							,  args.model_name							,  batch_size=args.bs							,  device=args.device							,  fpaa=args.fpaa							,  task=args.task							,  prefix=args.prefix							,  **a							,  )

  if args.reference_path is None:
    return {}

  # Compute scores
  _snake_case  :    Optional[int]            = calculate_bleu if "translation" in args.task else calculate_rouge
  _snake_case  :    Dict            = [x.rstrip() for x in open(args.save_path		).readlines()]
  _snake_case  :    Dict            = [x.rstrip() for x in open(args.reference_path		).readlines()][: len(a		)]
  _snake_case  :    List[str]            = score_fn(a							,  a		)
  scores.update(a		)

  if args.dump_args:
    scores.update(a		)
  if args.info:
    _snake_case  :    int            = args.info

  if verbose:
    print(a		)

  if args.score_path is not None:
    json.dump(a							,  open(args.score_path							,  "w"		)		)

  return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)





def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      list[tuple[int, int, int]]	)     ->	list[tuple[int, int, int]]:



	"""simple docstring"""






	a_     			=   sorted(UpperCamelCase     ,					key=lambda UpperCamelCase	: edge[2]	)

	a_     			=   list(range(UpperCamelCase	)	)

	def find_parent(UpperCamelCase		:      Any	):
		if i != parent[i]:
			a_     			=   find_parent(parent[i]	)
		return parent[i]

	a_     			=   0
	a_     			=   []

	for edge in edges:
		a_     			=   find_parent(edge[0]	)
		a_     			=   find_parent(edge[1]	)
		if parent_a != parent_b:
			minimum_spanning_tree_cost += edge[2]
			minimum_spanning_tree.append(UpperCamelCase	)
			a_     			=   parent_b

	return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
			_A     ,				_A							     =       list(map(int, input().strip().split()))
			_A							     =       []

			for _ in range(num_edges):
						_A     ,				_A     ,				_A							     =       (int(x) for x in input().strip().split())
						edges.append((nodea, nodea, cost))

			kruskal(num_nodes, edges)