

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 						lowercase     (					datasets.Metric				):


		"""simple docstring"""




		def 		A__       (					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 		A__       (					self						,							__snake_case):
			if self.config_name == "default":
				_UpperCamelCase :		Tuple								=    'moussaKam/frugalscore_tiny_bert-base_bert-score'
			else:
				_UpperCamelCase :		str								=    self.config_name
			_UpperCamelCase :		Optional[Any]								=    AutoModelForSequenceClassification.from_pretrained(__snake_case)
			_UpperCamelCase :		List[Any]								=    AutoTokenizer.from_pretrained(__snake_case)






		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case=32						,							__snake_case=1_28						,							__snake_case=None						,							):
			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:
				_UpperCamelCase :		List[str]								=    'gpu' if torch.cuda.is_available() else 'cpu'
			_UpperCamelCase :		Union[str, Any]								=    TrainingArguments(
			    'trainer'						,							fpaa=(device == 'gpu')						,							per_device_eval_batch_size=__snake_case						,							report_to='all'						,							no_cuda=(device == 'cpu')						,							log_level='warning'						,							)
			_UpperCamelCase :		Dict								=    {'sentence1': predictions, 'sentence2': references}
			_UpperCamelCase :		List[Any]								=    datasets.Dataset.from_dict(__snake_case)

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__             =		{"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ViTHybridForImageClassification""",
							    """ViTHybridModel""",
							    """ViTHybridPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




