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

import datasets


lowerCamelCase_				:							int       =    "\\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_				:							Optional[Any]       =    "\\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_				:							Optional[Any]       =    "\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  _lowerCamelCase			(datasets.Metric					):

     def 						__lowerCamelCase				(  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 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            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(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=None							,       ):
            assert len(SCREAMING_SNAKE_CASE_    ) == len(
                SCREAMING_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:
                   __snake_case						       =   'gpu' if torch.cuda.is_available() else 'cpu'
            __snake_case						       =   TrainingArguments(
                'trainer'							,       fpaa=(device == 'gpu')							,       per_device_eval_batch_size=SCREAMING_SNAKE_CASE_							,       report_to='all'							,       no_cuda=(device == 'cpu')							,       log_level='warning'							,       )
            __snake_case						       =   {'sentence1': predictions, 'sentence2': references}
            __snake_case						       =   datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE_    )

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

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


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCamelCase_				:							List[str]       =    logging.get_logger(__name__)







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			["""pixel_values"""]

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1 / 255							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = True							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   size if size is not None else {'height': 346, 'width': 346}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else {'height': 289, 'width': 289}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

            __snake_case						       =   do_resize
            __snake_case						       =   size
            __snake_case						       =   resample
            __snake_case						       =   do_center_crop
            __snake_case						       =   crop_size
            __snake_case						       =   do_rescale
            __snake_case						       =   rescale_factor
            __snake_case						       =   rescale_offset
            __snake_case						       =   do_normalize
            __snake_case						       =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __snake_case						       =   image_std if image_std is not None else IMAGENET_STANDARD_STD
            __snake_case						       =   include_top

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
            return resize(
                SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       resample=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
            return center_crop(SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            if offset:
                   __snake_case						       =   (image - 1_2_7.5) * scale
                   if data_format is not None:
                          __snake_case						       =   to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   rescaled_image.astype(np.floataa    )
            else:
                   __snake_case						       =   rescale(SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
            return rescaled_image

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            return normalize(SCREAMING_SNAKE_CASE_							,       mean=SCREAMING_SNAKE_CASE_							,       std=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   do_resize if do_resize is not None else self.do_resize
            __snake_case						       =   resample if resample is not None else self.resample
            __snake_case						       =   do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case						       =   do_rescale if do_rescale is not None else self.do_rescale
            __snake_case						       =   rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case						       =   rescale_offset if rescale_offset is not None else self.rescale_offset
            __snake_case						       =   do_normalize if do_normalize is not None else self.do_normalize
            __snake_case						       =   image_mean if image_mean is not None else self.image_mean
            __snake_case						       =   image_std if image_std is not None else self.image_std
            __snake_case						       =   include_top if include_top is not None else self.include_top

            __snake_case						       =   size if size is not None else self.size
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else self.crop_size
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

            __snake_case						       =   make_list_of_images(SCREAMING_SNAKE_CASE_    )

            if not valid_images(SCREAMING_SNAKE_CASE_    ):
                   raise ValueError(
                       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                       'torch.Tensor, tf.Tensor or jax.ndarray.'    )

            if do_resize and size is None or resample is None:
                   raise ValueError('Size and resample must be specified if do_resize is True.'    )

            if do_center_crop and crop_size is None:
                   raise ValueError('Crop size must be specified if do_center_crop is True.'    )

            if do_rescale and rescale_factor is None:
                   raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

            if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

            # All transformations expect numpy arrays.
            __snake_case						       =   [to_numpy_array(SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_resize:
                   __snake_case						       =   [self.resize(image=SCREAMING_SNAKE_CASE_							,       size=SCREAMING_SNAKE_CASE_							,       resample=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_center_crop:
                   __snake_case						       =   [self.center_crop(image=SCREAMING_SNAKE_CASE_							,       size=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_rescale:
                   __snake_case						       =   [self.rescale(image=SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       offset=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_normalize:
                   __snake_case						       =   [self.normalize(image=SCREAMING_SNAKE_CASE_							,       mean=SCREAMING_SNAKE_CASE_							,       std=SCREAMING_SNAKE_CASE_    ) for image in images]

            if include_top:
                   __snake_case						       =   [self.normalize(image=SCREAMING_SNAKE_CASE_							,       mean=[0, 0, 0]							,       std=SCREAMING_SNAKE_CASE_    ) for image in images]

            __snake_case						       =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) for image in images]

            __snake_case						       =   {'pixel_values': images}
            return BatchFeature(data=SCREAMING_SNAKE_CASE_							,       tensor_type=SCREAMING_SNAKE_CASE_    )
