




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


__magic_name__     :     Tuple  							=					getLogger(__name__)


__magic_name__     :     Optional[int]  							=					"""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      ,   ):
  lowerCAmelCase__								=			Path(__lowerCAmelCase							).open('''w'''      ,   encoding='''utf-8'''							)
  lowerCAmelCase__								=			str(__lowerCAmelCase							)
  lowerCAmelCase__								=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)
  if fpaa:
    lowerCAmelCase__								=			model.half()

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

  lowerCAmelCase__								=			time.time()
  # update config with task specific params
  use_task_specific_params(__lowerCAmelCase      ,   __lowerCAmelCase							)
  if prefix is None:
    lowerCAmelCase__								=			prefix or getattr(model.config      ,   '''prefix'''      ,   ''''''							) or ''''''
  for examples_chunk in tqdm(list(chunks(__lowerCAmelCase      ,   __lowerCAmelCase							)							)							):
    lowerCAmelCase__								=			[prefix + text for text in examples_chunk]
    lowerCAmelCase__								=			tokenizer(__lowerCAmelCase      ,   return_tensors='''pt'''      ,   truncation=__lowerCAmelCase      ,   padding='''longest'''							).to(__lowerCAmelCase							)
    lowerCAmelCase__								=			model.generate(
        input_ids=batch.input_ids      ,   attention_mask=batch.attention_mask      ,   **__lowerCAmelCase      ,   )
    lowerCAmelCase__								=			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()
  lowerCAmelCase__								=			int(time.time() - start_time							)  # seconds
  lowerCAmelCase__								=			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							):
  lowerCAmelCase__								=			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
  lowerCAmelCase__							,			lowerCAmelCase__								=			parser.parse_known_args()
  lowerCAmelCase__								=			parse_numeric_n_bool_cl_kwargs(__lowerCAmelCase							)
  if parsed_args and verbose:
    print(F"""parsed the following generate kwargs: {parsed_args}"""							)
  with open(args.input_path							) as f:
    lowerCAmelCase__								=			[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    lowerCAmelCase__								=			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."""							)
  lowerCAmelCase__								=			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
  lowerCAmelCase__								=			calculate_bleu if '''translation''' in args.task else calculate_rouge
  lowerCAmelCase__								=			[x.rstrip() for x in open(args.save_path							).readlines()]
  lowerCAmelCase__								=			[x.rstrip() for x in open(args.reference_path							).readlines()][: len(__lowerCAmelCase							)]
  lowerCAmelCase__								=			score_fn(__lowerCAmelCase      ,   __lowerCAmelCase							)
  scores.update(__lowerCAmelCase							)

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







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


__magic_name__     :     str  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Any					,	__lowerCamelCase  :		Any					,	__lowerCamelCase  :		Optional[int]=None	):



       """simple docstring"""
       lowerCAmelCase__								=			language
       lowerCAmelCase__								=			train_language
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.language if self.train_language is None else self.train_language
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			F"""train-{i}"""
         lowerCAmelCase__								=			line[0]
         lowerCAmelCase__								=			line[1]
         lowerCAmelCase__								=			'''contradiction''' if line[2] == '''contradictory''' else line[2]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	'''XNLI-1.0/xnli.test.tsv'''	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			line[0]
         if language != self.language:
           continue
         lowerCAmelCase__								=			F"""test-{i}"""
         lowerCAmelCase__								=			line[6]
         lowerCAmelCase__								=			line[7]
         lowerCAmelCase__								=			line[1]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples



     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       return ["contradiction", "entailment", "neutral"]



__magic_name__     :     Tuple  							=					{
    """xnli""": XnliProcessor,
}

__magic_name__     :     List[Any]  							=					{
    """xnli""": """classification""",
}

__magic_name__     :     str  							=					{
    """xnli""": 3,
}

