





"""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	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE = None	,						):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None	,						**_SCREAMING_SNAKE_CASE	,						):




		"""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:
				UpperCamelCase =						"cpu"
		elif map_location == "on_device":
				UpperCamelCase =						PartialState().device
		# Model states
		for i, model in enumerate(_SCREAMING_SNAKE_CASE     ):
				UpperCamelCase =						F"{MODEL_NAME}.bin" if i == 0 else F"{MODEL_NAME}_{i}.bin"
				UpperCamelCase =						os.path.join(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )
				models[i].load_state_dict(torch.load(_SCREAMING_SNAKE_CASE	,						map_location=_SCREAMING_SNAKE_CASE     )	,						**_SCREAMING_SNAKE_CASE     )
		logger.info("All model weights loaded successfully"     )

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

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

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

		# Random states
		try:
				UpperCamelCase =						torch.load(os.path.join(_SCREAMING_SNAKE_CASE	,						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	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE = 0     ):




		"""simple docstring"""


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






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE = 0     ):




		"""simple docstring"""


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













"""simple docstring"""





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 							_lowerCamelCase   (							datasets.Metric			):


						def      snake_case_ (self		)			-> Tuple:
								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      snake_case_ (self ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "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:
										UpperCamelCase =						"gpu" if torch.cuda.is_available() else "cpu"
								UpperCamelCase =						TrainingArguments(
								    "trainer" ,				fpaa=(device == "gpu") ,				per_device_eval_batch_size=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

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

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






