






"""simple docstring"""



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

import datasets


A_     =				'''\
@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}
}
'''

A_     =				'''\
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.
'''


A_     =				'''
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 				__SCREAMING_SNAKE_CASE    (					datasets.Metric		):


							def     _UpperCamelCase							(      self   :    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   :    Tuple      ,     snake_case   :    Optional[int]				):
													'''simple docstring'''





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


							def     _UpperCamelCase							(      self   :    Dict      ,     snake_case   :    Optional[Any]      ,     snake_case   :    int      ,     snake_case   :    Tuple=32      ,     snake_case   :    str=128      ,     snake_case   :    Union[str, Any]=None      ,     ):
													'''simple docstring'''





													assert len(snake_case				) == len(
													    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:
																			A__    :	Any			=	"""gpu""" if torch.cuda.is_available() else """cpu"""
													A__    :	List[str]			=	TrainingArguments(
													    """trainer"""      ,     fpaa=(device == """gpu""")      ,     per_device_eval_batch_size=snake_case      ,     report_to="""all"""      ,     no_cuda=(device == """cpu""")      ,     log_level="""warning"""      ,     )
													A__    :	List[Any]			=	{"""sentence1""": predictions, """sentence2""": references}
													A__    :	Dict			=	datasets.Dataset.from_dict(snake_case				)

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

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














"""simple docstring"""



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
						from .testing_utils import (
						    floats_tensor,
						    load_hf_numpy,
						    load_image,
						    load_numpy,
						    load_pt,
						    nightly,
						    parse_flag_from_env,
						    print_tensor_test,
						    require_torch_a,
						    require_torch_gpu,
						    skip_mps,
						    slow,
						    torch_all_close,
						    torch_device,
						)
						from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict   )		->Union[str, Any]:
						if version.parse(UpperCAmelCase__   ) < version.parse(UpperCAmelCase__   ):
												if "dev" in min_version:
																		A__    :	Optional[int]			=	(
																		    """This example requires a source install from HuggingFace diffusers (see """
																		    """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
																		)
												else:
																		A__    :	Union[str, Any]			=	f'This example requires a minimum version of {min_version},'
												error_message += f' but the version found is {__version__}.\n'
												raise ImportError(UpperCAmelCase__   )






