



from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


a_			=					logging.get_logger(__name__)


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset

  from transformers import Trainer



  class  __lowerCAmelCase     (	lowerCAmelCase__   ):




   def __init__(						self	,				__UpperCAmelCase = 101				):



      '''simple docstring'''
      __lowerCamelCase													=    length




   def __len__(						self				):



      '''simple docstring'''
      return self.length




   def __getitem__(						self	,				__UpperCAmelCase				):



      '''simple docstring'''
      return i



  class  __lowerCAmelCase     :




   def __call__(						self	,				__UpperCAmelCase				):



      '''simple docstring'''
      return {"input_ids": torch.tensor(__UpperCAmelCase				), "labels": torch.tensor(__UpperCAmelCase				)}



  class  __lowerCAmelCase     (	nn.Module   ):




   def __init__(						self				):



      '''simple docstring'''
      super().__init__()
      # Add some (unused) params otherwise DDP will complain.
      __lowerCamelCase													=    nn.Linear(120	,				80				)




   def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None				):



      '''simple docstring'''
      if labels is not None:
         return torch.tensor(0.0	,				device=input_ids.device				), input_ids
      else:
         return input_ids



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 @require_torch_neuroncore
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
    __lowerCamelCase													=    self.get_auto_remove_tmp_dir()
    __lowerCamelCase													=    F"""--output_dir {output_dir}""".split()
    __lowerCamelCase													=    ['''torchrun'''] + distributed_args + args
    execute_subprocess_async(__UpperCAmelCase	,				env=self.get_env()				)
    # successful return here == success - any errors would have caused an error in the sub-call



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 @require_torch_multi_gpu
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
    __lowerCamelCase													=    self.get_auto_remove_tmp_dir()
    __lowerCamelCase													=    F"""--output_dir {output_dir}""".split()
    __lowerCamelCase													=    ['''torchrun'''] + distributed_args + args
    execute_subprocess_async(__UpperCAmelCase	,				env=self.get_env()				)
    # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
  # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
  #
  # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

  a_			=					HfArgumentParser((TrainingArguments,))
  a_			=					parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
      f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
  )

  # Essentially, what we want to verify in the distributed case is that we get all samples back,
  # in the right order. (this is crucial for prediction for instance)
  for dataset_length in [101, 40, 7]:
    a_			=					DummyDataset(dataset_length)





    def 		a__			(		_UpperCamelCase			: EvalPrediction							):
       __lowerCamelCase													=    list(range(len(_UpperCamelCase							)							)							)
       __lowerCamelCase													=    p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
       if not success and training_args.local_rank == 0:
          logger.warning(
              '''Predictions and/or labels do not match expected results:\n  - predictions: '''
              F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""							)
       return {"success": success}

    a_			=					Trainer(
        model=DummyModel(),
        args=training_args,
        data_collator=DummyDataCollator(),
        eval_dataset=dataset,
        compute_metrics=compute_metrics,
    )
    a_			=					trainer.evaluate()
    logger.info(metrics)
    if metrics["eval_success"] is not True:
      logger.error(metrics)
      exit(1)

    a_			=					trainer.predict(dataset)
    logger.info(p.metrics)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics)
      exit(1)

    a_			=					2

    a_			=					trainer.evaluate()
    logger.info(metrics)
    if metrics["eval_success"] is not True:
      logger.error(metrics)
      exit(1)

    a_			=					trainer.predict(dataset)
    logger.info(p.metrics)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics)
      exit(1)





    a_			=					None











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 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: str ,_UpperCamelCase			: str ,_UpperCamelCase			: int = 8 ,_UpperCamelCase			: str = DEFAULT_DEVICE ,_UpperCamelCase			: Dict=False ,_UpperCamelCase			: Dict="summarization" ,_UpperCamelCase			: Optional[int]=None ,**_UpperCamelCase			: Dict ,):
   __lowerCamelCase													=    Path(_UpperCamelCase							).open('''w''' ,encoding='''utf-8'''							)
   __lowerCamelCase													=    str(_UpperCamelCase							)
   __lowerCamelCase													=    AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase							).to(_UpperCamelCase							)
   if fpaa:
      __lowerCamelCase													=    model.half()

   __lowerCamelCase													=    AutoTokenizer.from_pretrained(_UpperCamelCase							)
   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(_UpperCamelCase ,_UpperCamelCase							)
   if prefix is None:
      __lowerCamelCase													=    prefix or getattr(model.config ,'''prefix''' ,''''''							) or ''''''
   for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase							)							)							):
      __lowerCamelCase													=    [prefix + text for text in examples_chunk]
      __lowerCamelCase													=    tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest'''							).to(_UpperCamelCase							)
      __lowerCamelCase													=    model.generate(
          input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
      __lowerCamelCase													=    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()
   __lowerCamelCase													=    int(time.time() - start_time							)  # seconds
   __lowerCamelCase													=    len(_UpperCamelCase							)
   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__			(		_UpperCamelCase			: Union[str, Any]=True							):
   __lowerCamelCase													=    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
   __lowerCamelCase							,__lowerCamelCase													=    parser.parse_known_args()
   __lowerCamelCase													=    parse_numeric_n_bool_cl_kwargs(_UpperCamelCase							)
   if parsed_args and verbose:
      print(F"""parsed the following generate kwargs: {parsed_args}"""							)
   __lowerCamelCase													=    [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
   if args.n_obs > 0:
      __lowerCamelCase													=    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."""							)

   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'''							)

   __lowerCamelCase													=    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
   __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(_UpperCamelCase							)]
   __lowerCamelCase													=    score_fn(_UpperCamelCase ,_UpperCamelCase							)
   scores.update(_UpperCamelCase							)

   if args.dump_args:
      scores.update(_UpperCamelCase							)
   if args.info:
      __lowerCamelCase													=    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)






