






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  							=  getLogger(__name__)


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







def 			lowercase__  (				A_:	str     ,			A_:	List[Any]     ,			A_:	List[Any]     ,			A_:	Union[str, Any] = 8     ,			A_:	Optional[Any] = DEFAULT_DEVICE     ,			A_:	Dict=False     ,			A_:	Tuple="summarization"     ,			A_:	Optional[int]=None     ,			**A_:	int     ,			)  ->							Dict:







       """simple docstring"""






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

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

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







def 			lowercase__  (				)  ->							List[Any]:







       """simple docstring"""






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







def 			lowercase__  (				A_:	int=True		)  ->							int:







       """simple docstring"""






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

       if args.reference_path is None:
              return {}

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

       if args.dump_args:
              scores.update(snake_case_		)
       if args.info:
              __UpperCAmelCase    							=args.info

       if verbose:
              print(snake_case_		)

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









import math as m







def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:







       """simple docstring"""






       return (f(a + h		) - f(a - h		)) / (2 * h)







def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase    							=xa  # set the initial guess
       __UpperCAmelCase    							=[a]
       __UpperCAmelCase    							=abs(f(A_		)		)
       __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
       for _ in range(A_		):
              if fa(A_		) == 0:
                     raise ValueError("""No converging solution found"""		)
              __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
              if logsteps:
                     steps.append(A_		)
              if error < maxerror:
                     break
       else:
              raise ValueError("""Iteration limit reached, no converging solution found"""		)
       if logsteps:
              # If logstep is true, then log intermediate steps
              return a, error, steps
       return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   __A							,    __A							,    __A  							=  newton_raphson(
       f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel("step")
   plt.ylabel("error")
   plt.show()
   print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")




