






"""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


UpperCamelCase											=     getLogger(__name__)


UpperCamelCase											=     """cuda""" if torch.cuda.is_available() else """cpu"""






def 							lowerCAmelCase			(   UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				str  ,			UpperCamelCase_:				str  ,			UpperCamelCase_:				int = 8  ,			UpperCamelCase_:				str = DEFAULT_DEVICE  ,			UpperCamelCase_:				int=False  ,			UpperCamelCase_:				int="summarization"  ,			UpperCamelCase_:				Any=None  ,			**UpperCamelCase_:				Any  ,			)							->				Dict:







  '''simple docstring'''







  _a					      =						Path(UpperCamelCase_       ).open("w"  ,			encoding="utf-8"       )
  _a					      =						str(UpperCamelCase_       )
  _a					      =						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_       ).to(UpperCamelCase_       )
  if fpaa:
    _a					      =						model.half()

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

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






def 							lowerCAmelCase			(   )							->				List[Any]:







  '''simple docstring'''







  return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"       )






def 							lowerCAmelCase			(   UpperCamelCase_:				Tuple=True       )							->				str:







  '''simple docstring'''







  _a					      =						argparse.ArgumentParser()
  parser.add_argument("model_name"  ,			type=UpperCamelCase_  ,			help="like facebook/bart-large-cnn,t5-base, etc."       )
  parser.add_argument("input_path"  ,			type=UpperCamelCase_  ,			help="like cnn_dm/test.source"       )
  parser.add_argument("save_path"  ,			type=UpperCamelCase_  ,			help="where to save summaries"       )
  parser.add_argument("--reference_path"  ,			type=UpperCamelCase_  ,			required=UpperCamelCase_  ,			help="like cnn_dm/test.target"       )
  parser.add_argument("--score_path"  ,			type=UpperCamelCase_  ,			required=UpperCamelCase_  ,			default="metrics.json"  ,			help="where to save metrics"       )
  parser.add_argument("--device"  ,			type=UpperCamelCase_  ,			required=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="cuda, cuda:1, cpu etc."       )
  parser.add_argument(
      "--prefix"  ,			type=UpperCamelCase_  ,			required=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="will be added to the begininng of src examples"       )
  parser.add_argument("--task"  ,			type=UpperCamelCase_  ,			default="summarization"  ,			help="used for task_specific_params + metrics"       )
  parser.add_argument("--bs"  ,			type=UpperCamelCase_  ,			default=8  ,			required=UpperCamelCase_  ,			help="batch size"       )
  parser.add_argument(
      "--n_obs"  ,			type=UpperCamelCase_  ,			default=-1  ,			required=UpperCamelCase_  ,			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=UpperCamelCase_  ,			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
  _a      ,						_a					      =						parser.parse_known_args()
  _a					      =						parse_numeric_n_bool_cl_kwargs(UpperCamelCase_       )
  if parsed_args and verbose:
    print(f'''parsed the following generate kwargs: {parsed_args}'''       )
  with open(args.input_path       ) as f:
    _a					      =						[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    _a					      =						examples[: args.n_obs]
  Path(args.save_path       ).parent.mkdir(exist_ok=UpperCamelCase_       )
  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.'''       )
  _a					      =						generate_summaries_or_translations(
      UpperCamelCase_  ,			args.save_path  ,			args.model_name  ,			batch_size=args.bs  ,			device=args.device  ,			fpaa=args.fpaa  ,			task=args.task  ,			prefix=args.prefix  ,			**UpperCamelCase_  ,			)

  if args.reference_path is None:
    return {}

  # Compute scores
  _a					      =						calculate_bleu if "translation" in args.task else calculate_rouge
  _a					      =						[x.rstrip() for x in open(args.save_path       ).readlines()]
  _a					      =						[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(UpperCamelCase_       )]
  _a					      =						score_fn(UpperCamelCase_  ,			UpperCamelCase_       )
  scores.update(UpperCamelCase_       )

  if args.dump_args:
    scores.update(UpperCamelCase_       )
  if args.info:
    _a					      =						args.info

  if verbose:
    print(UpperCamelCase_       )

  if args.score_path is not None:
    json.dump(UpperCamelCase_  ,			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)










"""simple docstring"""




UpperCamelCase											=     [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				bool:







  '''simple docstring'''







  return 0 <= row < rows and 0 <= col < cols






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				set  ,			UpperCamelCase_:				list[list[int]]       )							->				int:







  '''simple docstring'''







  _a					      =						len(UpperCamelCase_       )
  _a					      =						len(mat[0]       )
  if is_safe(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       ) and (row, col) not in seen and mat[row][col] == 1:
    seen.add((row, col)       )
    return (
        1
        + depth_first_search(row + 1  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )
        + depth_first_search(row - 1  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )
        + depth_first_search(UpperCamelCase_  ,			col + 1  ,			UpperCamelCase_  ,			UpperCamelCase_       )
        + depth_first_search(UpperCamelCase_  ,			col - 1  ,			UpperCamelCase_  ,			UpperCamelCase_       )
    )
  else:
    return 0






def 							lowerCAmelCase			(   UpperCamelCase_:				list[list[int]]       )							->				int:







  '''simple docstring'''







  _a					      =						set()

  _a					      =						0
  for row, line in enumerate(UpperCamelCase_       ):
    for col, item in enumerate(UpperCamelCase_       ):
      if item == 1 and (row, col) not in seen:
        # Maximizing the area
        _a					      =						max(UpperCamelCase_  ,			depth_first_search(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )       )
  return max_area


if __name__ == "__main__":
     import doctest

     print(find_max_area(matrix))  # Output -> 6
     doctest.testmod()


