


"""simple docstring"""




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      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				List[dict]		,      SCREAMING_SNAKE_CASE__ :				list		,      SCREAMING_SNAKE_CASE__ :				list		,      SCREAMING_SNAKE_CASE__ :				int		,      SCREAMING_SNAKE_CASE__ :				GradScaler = None		,      ):
       '''simple docstring'''





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




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[Any]		,      SCREAMING_SNAKE_CASE__ :				int		,      SCREAMING_SNAKE_CASE__ :				int		,      SCREAMING_SNAKE_CASE__ :				Dict=None		,      SCREAMING_SNAKE_CASE__ :				int=None		,      **SCREAMING_SNAKE_CASE__ :				Any		,      ):
       '''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:
              _lowerCamelCase	:		int          ="cpu"
       elif map_location == "on_device":
              _lowerCamelCase	:		Optional[int]          =PartialState().device
       # Model states
       for i, model in enumerate(_UpperCAmelCase					):
              _lowerCamelCase	:		Any          =F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
              _lowerCamelCase	:		Dict          =os.path.join(_UpperCAmelCase		,      _UpperCAmelCase					)
              models[i].load_state_dict(torch.load(_UpperCAmelCase		,      map_location=_UpperCAmelCase					)		,      **_UpperCAmelCase					)
       logger.info('All model weights loaded successfully'					)

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

       # Scheduler states
       for i, scheduler in enumerate(_UpperCAmelCase					):
              _lowerCamelCase	:		Optional[Any]          =F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
              _lowerCamelCase	:		Optional[Any]          =os.path.join(_UpperCAmelCase		,      _UpperCAmelCase					)
              scheduler.load_state_dict(torch.load(_UpperCAmelCase					)					)
       logger.info('All scheduler states loaded successfully'					)

       # GradScaler state
       if scaler is not None:
              _lowerCamelCase	:		Union[str, Any]          =os.path.join(_UpperCAmelCase		,      _UpperCAmelCase					)
              scaler.load_state_dict(torch.load(_UpperCAmelCase					)					)
              logger.info('GradScaler state loaded successfully'					)

       # Random states
       try:
              _lowerCamelCase	:		Dict          =torch.load(os.path.join(_UpperCAmelCase		,      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      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				int = 0					):
       '''simple docstring'''





       _lowerCamelCase	:		str          =Path(_UpperCAmelCase					) / F'''custom_checkpoint_{index}.pkl'''
       logger.info(F'''Saving the state of {get_pretty_name(_UpperCAmelCase					)} to {save_location}'''					)
       torch.save(obj.state_dict()		,      _UpperCAmelCase					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				int = 0					):
       '''simple docstring'''





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






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 A  (       datasets.Metric					):






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

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

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


