


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  """\n""".join(lowerCAmelCase__   )
   Path(lowerCAmelCase__   ).open("""w"""   ).writelines(lowerCAmelCase__   )


SCREAMING_SNAKE_CASE							    =							'patrickvonplaten/t5-tiny-random'
SCREAMING_SNAKE_CASE							    =							'sshleifer/bart-tiny-random'
SCREAMING_SNAKE_CASE							    =							'sshleifer/tiny-mbart'

SCREAMING_SNAKE_CASE							    =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
        __a					=  input_file_name.parent / """utest_output.txt"""
        assert not output_file_name.exists()
        __a					=  [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
        _dump_articles(__A  ,       __A    )

        __a					=  str(Path(self.get_auto_remove_tmp_dir()    ) / """scores.json"""    )
        __a					=  """translation_en_to_de""" if model == T5_TINY else """summarization"""
        __a					=  f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

        with patch.object(__A  ,       """argv"""  ,       __A    ):
           run_generate()
           assert Path(__A    ).exists()
           # os.remove(Path(output_file_name))


     def 	snake_case_  (	self    ):
        self.run_eval_tester(__A    )


     @parameterized.expand([BART_TINY, MBART_TINY]    )
     @slow
     def 	snake_case_  (	self  ,       __A    ):
        self.run_eval_tester(__A    )


     @parameterized.expand([T5_TINY, MBART_TINY]    )
     @slow
     def 	snake_case_  (	self  ,       __A    ):
        __a					=  Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
        __a					=  input_file_name.parent / """utest_output.txt"""
        assert not output_file_name.exists()

        __a					=  {
            """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
            """de""": [
                """Maschinelles Lernen ist großartig, oder?""",
                """Ich esse gerne Bananen""",
                """Morgen ist wieder ein toller Tag!""",
            ],
        }

        __a					=  Path(self.get_auto_remove_tmp_dir()    )
        __a					=  str(tmp_dir / """scores.json"""    )
        __a					=  str(tmp_dir / """val.target"""    )
        _dump_articles(__A  ,       text["""en"""]    )
        _dump_articles(__A  ,       text["""de"""]    )
        __a					=  """translation_en_to_de""" if model == T5_TINY else """summarization"""
        __a					=  f'''
            run_eval_search.py
            {model}
            {str(__A    )}
            {str(__A    )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
        testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""]    )

        with patch.object(__A  ,       """argv"""  ,       __A    ):
           with CaptureStdout() as cs:
              run_search()
           __a					=  [""" num_beams | length_penalty""", model, """Best score args"""]
           __a					=  ["""Info"""]
           if "translation" in task:
              expected_strings.append("""bleu"""    )
           else:
              expected_strings.extend(__A    )
           for w in expected_strings:
              assert w in cs.out
           for w in un_expected_strings:
              assert w not in cs.out
           assert Path(__A    ).exists()
           os.remove(Path(__A    )    )







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


SCREAMING_SNAKE_CASE							    =							getLogger(__name__)


SCREAMING_SNAKE_CASE							    =							'cuda' if torch.cuda.is_available() else 'cpu'




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 8		,				lowerCAmelCase__ = DEFAULT_DEVICE		,				lowerCAmelCase__=False		,				lowerCAmelCase__="summarization"		,				lowerCAmelCase__=None		,				**lowerCAmelCase__		,				):
   __a					=  Path(lowerCAmelCase__   ).open("""w"""		,				encoding="""utf-8"""   )
   __a					=  str(lowerCAmelCase__   )
   __a					=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__   ).to(lowerCAmelCase__   )
   if fpaa:
      __a					=  model.half()

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




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




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

   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.'''   )

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError("""Can't mix --fp16 and --device cpu"""   )

   __a					=  generate_summaries_or_translations(
       lowerCAmelCase__		,				args.save_path		,				args.model_name		,				batch_size=args.bs		,				device=args.device		,				fpaa=args.fpaa		,				task=args.task		,				prefix=args.prefix		,				**lowerCAmelCase__		,				)

   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(lowerCAmelCase__   )]
   __a					=  score_fn(lowerCAmelCase__		,				lowerCAmelCase__   )
   scores.update(lowerCAmelCase__   )

   if args.dump_args:
      scores.update(lowerCAmelCase__   )
   if args.info:
      __a					=  args.info

   if verbose:
      print(lowerCAmelCase__   )

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



