





"""simple docstring"""
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCamelCase_          =	"\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n"

lowerCamelCase_          =	"\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n"


lowerCamelCase_          =	"\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric(\"frugalscore\")\n    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])\n    >>> print([round(s, 3) for s in results[\"scores\"]])\n    [0.631, 0.645]\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,			_KWARGS_DESCRIPTION						)
class       _SCREAMING_SNAKE_CASE(     datasets.Metric						):




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             return datasets.MetricInfo(
                 description=_DESCRIPTION	,citation=_CITATION	,inputs_description=_KWARGS_DESCRIPTION	,features=datasets.Features(
                     {
                         '''predictions''': datasets.Value('''string'''							),
                         '''references''': datasets.Value('''string'''							),
                     }							)	,homepage='''https://github.com/moussaKam/FrugalScore'''	,)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             if self.config_name == "default":
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''moussaKam/frugalscore_tiny_bert-base_bert-score'''
             else:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.config_name
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Any	       =						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=32	,SCREAMING_SNAKE_CASE__=1_28	,SCREAMING_SNAKE_CASE__=None	,)    ->   str:





             """simple docstring"""






             assert len(SCREAMING_SNAKE_CASE__							) == len(
                 SCREAMING_SNAKE_CASE__							), "predictions and references should have the same number of sentences."
             if device is not None:
                   assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                   __SCREAMING_SNAKE_CASE     :int	       =						'''gpu''' if torch.cuda.is_available() else '''cpu'''
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						TrainingArguments(
                 '''trainer'''	,fpaa=(device == '''gpu''')	,per_device_eval_batch_size=SCREAMING_SNAKE_CASE__	,report_to='''all'''	,no_cuda=(device == '''cpu''')	,log_level='''warning'''	,)
             __SCREAMING_SNAKE_CASE     :str	       =						{'''sentence1''': predictions, '''sentence2''': references}
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE__							)

             def tokenize_function(SCREAMING_SNAKE_CASE__							):
                   return self.tokenizer(
                       data['''sentence1''']	,data['''sentence2''']	,max_length=SCREAMING_SNAKE_CASE__	,truncation=SCREAMING_SNAKE_CASE__	,padding=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :Tuple	       =						raw_datasets.map(SCREAMING_SNAKE_CASE__	,batched=SCREAMING_SNAKE_CASE__							)
             tokenized_datasets.remove_columns(['''sentence1''', '''sentence2''']							)
             __SCREAMING_SNAKE_CASE     :int	       =						Trainer(self.model	,SCREAMING_SNAKE_CASE__	,tokenizer=self.tokenizer							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						trainer.predict(SCREAMING_SNAKE_CASE__							)
             return {"scores": list(predictions.predictions.squeeze(-1							)							)}






"""simple docstring"""
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_          =	logging.getLogger(__name__)



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
      parser.add_argument('''--model_name'''				,     type=a_				,     default='''transfo-xl-wt103'''				,     help='''pretrained model name'''							)
      parser.add_argument(
          '''--split'''				,     type=a_				,     default='''test'''				,     choices=['''all''', '''valid''', '''test''']				,     help='''which split to evaluate'''							)
      parser.add_argument('''--batch_size'''				,     type=a_				,     default=10				,     help='''batch size'''							)
      parser.add_argument('''--tgt_len'''				,     type=a_				,     default=1_28				,     help='''number of tokens to predict'''							)
      parser.add_argument('''--ext_len'''				,     type=a_				,     default=0				,     help='''length of the extended context'''							)
      parser.add_argument('''--mem_len'''				,     type=a_				,     default=16_00				,     help='''length of the retained previous heads'''							)
      parser.add_argument('''--clamp_len'''				,     type=a_				,     default=10_00				,     help='''max positional embedding index'''							)
      parser.add_argument('''--no_cuda'''				,     action='''store_true'''				,     help='''Do not use CUDA even though CUA is available'''							)
      parser.add_argument('''--work_dir'''				,     type=a_				,     required=a_				,     help='''path to the work_dir'''							)
      parser.add_argument('''--no_log'''				,     action='''store_true'''				,     help='''do not log the eval result'''							)
      parser.add_argument('''--same_length'''				,     action='''store_true'''				,     help='''set same length attention with masking'''							)
      parser.add_argument('''--server_ip'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      parser.add_argument('''--server_port'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						parser.parse_args()
      assert args.ext_len >= 0, "extended context length must be non-negative"

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print('''Waiting for debugger attach'''							)
            ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,     redirect_output=a_							)
            ptvsd.wait_for_attach()

      __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
      logger.info('''device: {}'''.format(a_							)							)

      # Load a pre-processed dataset
      # You can also build the corpus yourself using TransfoXLCorpus methods
      # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
      # and tokenizing the dataset
      # The pre-processed corpus is a convertion (using the conversion script )
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						TransfoXLCorpus.from_pretrained(args.model_name							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''valid'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''test'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)

      # Load a pre-trained model
      __SCREAMING_SNAKE_CASE     :int	       =						TransfoXLLMHeadModel.from_pretrained(args.model_name							)
      model.to(a_							)

      logger.info(
          '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
              args.batch_size				,     args.tgt_len				,     args.ext_len				,     args.mem_len				,     args.clamp_len							)							)

      model.reset_memory_length(args.mem_len							)
      if args.clamp_len > 0:
            __SCREAMING_SNAKE_CASE     :Dict	       =						args.clamp_len
      if args.same_length:
            __SCREAMING_SNAKE_CASE     :int	       =						True

      ###############################################################################
      # Evaluation code
      ###############################################################################
      def evaluate(a_  :	Optional[int]							):
            # Turn on evaluation mode which disables dropout.
            model.eval()
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						0, 0.0
            __SCREAMING_SNAKE_CASE     :Tuple	       =						time.time()
            with torch.no_grad():
                  __SCREAMING_SNAKE_CASE     :Any	       =						None
                  for idx, (data, target, seq_len) in enumerate(a_							):
                        __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model(a_				,     lm_labels=a_				,     mems=a_							)
                        __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						ret
                        __SCREAMING_SNAKE_CASE     :Tuple	       =						loss.mean()
                        total_loss += seq_len * loss.item()
                        total_len += seq_len
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						time.time() - start_time
            logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(a_				,     10_00 * total_time / (idx + 1)							)							)
            return total_loss / total_len

      # Run on test data.
      if args.split == "all":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Any	       =						evaluate(a_							)
      elif args.split == "valid":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						None
      elif args.split == "test":
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						None

      def format_log(a_  :	int				,     a_  :	List[Any]							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(a_				,     a_				,     math.exp(a_							)							)
            return log_str

      __SCREAMING_SNAKE_CASE     :Tuple	       =						''''''
      if valid_loss is not None:
            log_str += format_log(a_				,     '''valid'''							)
      if test_loss is not None:
            log_str += format_log(a_				,     '''test'''							)

      logger.info('''=''' * 1_00							)
      logger.info(a_							)
      logger.info('''=''' * 1_00							)


if __name__ == "__main__":
  main()