

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


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

lowerCamelCase									= '''\
FrugalScore 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.
'''


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







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

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   int:






     """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,   lowercase_	)							->   int:






     """simple docstring"""






     if self.config_name == "default":
       a__  ='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
     else:
       a__  =self.config_name
     a__  =AutoModelForSequenceClassification.from_pretrained(lowercase_	)
     a__  =AutoTokenizer.from_pretrained(lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_=32,   lowercase_=128,   lowercase_=None,   )							->   Optional[Any]:






     """simple docstring"""






     assert len(lowercase_	) == len(
         lowercase_	), "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:
       a__  ='''gpu''' if torch.cuda.is_available() else '''cpu'''
     a__  =TrainingArguments(
         '''trainer''',   fpaa=(device == '''gpu'''),   per_device_eval_batch_size=lowercase_,   report_to='''all''',   no_cuda=(device == '''cpu'''),   log_level='''warning''',   )
     a__  ={'''sentence1''': predictions, '''sentence2''': references}
     a__  =datasets.Dataset.from_dict(lowercase_	)

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

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



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase									= get_logger(__name__)




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			List[dict]     ,				_A				:			list     ,				_A				:			list     ,				_A				:			int     ,				_A				:			GradScaler = None     ,				):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__  =opt.state_dict()
    a__  =F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =scheduler.state_dict()
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__  =scaler.state_dict()
    a__  =os.path.join(_A     ,				_A							)
    torch.save(_A     ,				_A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__  ={}
  a__  =F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__  =random.getstate()
  a__  =np.random.get_state()
  a__  =torch.get_rng_state()
  if is_xpu_available():
    a__  =torch.xpu.get_rng_state_all()
  else:
    a__  =torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__  =xm.get_rng_state()
  a__  =os.path.join(_A     ,				_A							)
  torch.save(_A     ,				_A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			List[str]=None     ,				_A				:			Dict=None     ,				**_A				:			Tuple     ,				):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__  ='''cpu'''
  elif map_location == "on_device":
    a__  =PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    models[i].load_state_dict(torch.load(_A     ,				map_location=_A							)     ,				**_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__  =F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    a__  =torch.load(_A     ,				map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__  =os.path.join(_A     ,				_A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__  =torch.load(os.path.join(_A     ,				F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()     ,				_A							)




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A     ,				map_location='''cpu'''							)							)
