


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

import datasets


UpperCAmelCase			=    """\
@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}
}
"""

UpperCAmelCase			=    """\
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.
"""


UpperCAmelCase			=    """
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       ):

 '''simple docstring'''

 def 						UpperCamelCase__     (	self					):
       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		,      _UpperCAmelCase					):
       if self.config_name == "default":
             snake_case_       =      '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
       else:
             snake_case_       =      self.config_name
       snake_case_       =      AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase					)
       snake_case_       =      AutoTokenizer.from_pretrained(_UpperCAmelCase					)





 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=32		,      _UpperCAmelCase=1_28		,      _UpperCAmelCase=None		,      ):
       assert len(_UpperCAmelCase					) == len(
           _UpperCAmelCase					), "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:
             snake_case_       =      '''gpu''' if torch.cuda.is_available() else '''cpu'''
       snake_case_       =      TrainingArguments(
           '''trainer'''		,      fpaa=(device == '''gpu''')		,      per_device_eval_batch_size=_UpperCAmelCase		,      report_to='''all'''		,      no_cuda=(device == '''cpu''')		,      log_level='''warning'''		,      )
       snake_case_       =      {'''sentence1''': predictions, '''sentence2''': references}
       snake_case_       =      datasets.Dataset.from_dict(_UpperCAmelCase					)

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

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



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


UpperCAmelCase			=    get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE					) < version.parse(SCREAMING_SNAKE_CASE					):
            if "dev" in min_version:
                  snake_case_       =      (
                      '''This example requires a source install from HuggingFace diffusers (see '''
                      '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                  )
            else:
                  snake_case_       =      f'''This example requires a minimum version of {min_version},'''
            error_message += f''' but the version found is {__version__}.\n'''
            raise ImportError(SCREAMING_SNAKE_CASE					)