


"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def __init__(self							,			a_							,			a_=7							,			a_=3							,			a_=18							,			a_=30							,			a_=4_00							,			a_=True							,			a_=None							,			a_=True							,			a_=None							,			a_=True							,			a_=[0.4814_5466, 0.457_8275, 0.4082_1073]							,			a_=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,			a_=True							,			):





			'''simple docstring'''

			__snake_case      :		Any         =							size if size is not None else {'''shortest_edge''': 20}
			__snake_case      :		Optional[int]         =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			__snake_case      :		Dict         =							parent
			__snake_case      :		List[Any]         =							batch_size
			__snake_case      :		str         =							num_channels
			__snake_case      :		Optional[int]         =							image_size
			__snake_case      :		Any         =							min_resolution
			__snake_case      :		Tuple         =							max_resolution
			__snake_case      :		Optional[int]         =							do_resize
			__snake_case      :		int         =							size
			__snake_case      :		int         =							do_center_crop
			__snake_case      :		Optional[Any]         =							crop_size
			__snake_case      :		Union[str, Any]         =							do_normalize
			__snake_case      :		Optional[int]         =							image_mean
			__snake_case      :		Optional[Any]         =							image_std
			__snake_case      :		Optional[int]         =							do_convert_rgb



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_convert_rgb": self.do_convert_rgb,
			}



		def 							SCREAMING_SNAKE_CASE    (self							,			a_=False							,			a_=False							,			a_=False   ):





			'''simple docstring'''

			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			if equal_resolution:
				__snake_case      :		Dict         =							[]
				for i in range(self.batch_size   ):
					image_inputs.append(
					    np.random.randint(
					        2_55							,			size=(self.num_channels, self.max_resolution, self.max_resolution)							,			dtype=np.uinta   )   )
			else:
				__snake_case      :		List[str]         =							[]
				for i in range(self.batch_size   ):
					__snake_case      ,				__snake_case      :		Dict         =							np.random.choice(np.arange(self.min_resolution							,			self.max_resolution   )							,			2   )
					image_inputs.append(np.random.randint(2_55							,			size=(self.num_channels, width, height)							,			dtype=np.uinta   )   )

			if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				__snake_case      :		Dict         =							[Image.fromarray(np.moveaxis(a_							,			0							,			-1   )   ) for x in image_inputs]

			if torchify:
				__snake_case      :		Dict         =							[torch.from_numpy(a_   ) for x in image_inputs]

			return image_inputs



@require_torch
@require_vision
class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =CLIPImageProcessor if is_vision_available() else None



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							CLIPImageProcessingTester(self   )



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.image_processor_tester.prepare_image_processor_dict()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(a_							,			'''do_resize'''   )   )
			self.assertTrue(hasattr(a_							,			'''size'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_normalize'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_mean'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_std'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_convert_rgb'''   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							self.image_processing_class.from_dict(self.image_processor_dict   )
			self.assertEqual(image_processor.size							,			{'''shortest_edge''': 20}   )
			self.assertEqual(image_processor.crop_size							,			{'''height''': 18, '''width''': 18}   )

			__snake_case      :		int         =							self.image_processing_class.from_dict(self.image_processor_dict							,			size=42							,			crop_size=84   )
			self.assertEqual(image_processor.size							,			{'''shortest_edge''': 42}   )
			self.assertEqual(image_processor.crop_size							,			{'''height''': 84, '''width''': 84}   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__snake_case      :		Tuple         =							self.image_processor_tester.prepare_inputs(equal_resolution=a_   )
			for image in image_inputs:
				self.assertIsInstance(a_							,			Image.Image   )

			# Test not batched input
			__snake_case      :		Union[str, Any]         =							image_processing(image_inputs[0]							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		str         =							image_processing(a_							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			__snake_case      :		int         =							self.image_processor_tester.prepare_inputs(equal_resolution=a_							,			numpify=a_   )
			for image in image_inputs:
				self.assertIsInstance(a_							,			np.ndarray   )

			# Test not batched input
			__snake_case      :		Optional[Any]         =							image_processing(image_inputs[0]							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		Union[str, Any]         =							image_processing(a_							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__snake_case      :		List[Any]         =							self.image_processor_tester.prepare_inputs(equal_resolution=a_							,			torchify=a_   )
			for image in image_inputs:
				self.assertIsInstance(a_							,			torch.Tensor   )

			# Test not batched input
			__snake_case      :		str         =							image_processing(image_inputs[0]							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		Any         =							image_processing(a_							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)



@require_torch
@require_vision
class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =CLIPImageProcessor if is_vision_available() else None



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							CLIPImageProcessingTester(self							,			num_channels=4   )
			__snake_case      :		Optional[Any]         =							3



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.image_processor_tester.prepare_image_processor_dict()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(a_							,			'''do_resize'''   )   )
			self.assertTrue(hasattr(a_							,			'''size'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_normalize'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_mean'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_std'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_convert_rgb'''   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__snake_case      :		Optional[Any]         =							self.image_processor_tester.prepare_inputs(equal_resolution=a_   )
			for image in image_inputs:
				self.assertIsInstance(a_							,			Image.Image   )

			# Test not batched input
			__snake_case      :		int         =							image_processing(image_inputs[0]							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        1,
			        self.expected_encoded_image_num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		Optional[int]         =							image_processing(a_							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        self.image_processor_tester.batch_size,
			        self.expected_encoded_image_num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)










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 	snake_case_			(datasets.Metric	):




       """simple docstring"""






       def 			A_ (  self):

             """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 			A_ (  self  ,lowercase):

             """simple docstring"""





             if self.config_name == "default":
                   UpperCAmelCase_	:						str					   =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
             else:
                   UpperCAmelCase_	:						Optional[int]					   =		self.config_name
             UpperCAmelCase_	:						Dict					   =		AutoModelForSequenceClassification.from_pretrained(lowercase)
             UpperCAmelCase_	:						str					   =		AutoTokenizer.from_pretrained(lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase=32  ,lowercase=128  ,lowercase=None  ,):

             """simple docstring"""





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

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

             UpperCAmelCase_	:						int					   =		raw_datasets.map(lowercase  ,batched=lowercase)
             tokenized_datasets.remove_columns(["sentence1", "sentence2"])
             UpperCAmelCase_	:						Union[str, Any]					   =		Trainer(self.model  ,lowercase  ,tokenizer=self.tokenizer)
             UpperCAmelCase_	:						Tuple					   =		trainer.predict(lowercase)
             return {"scores": list(predictions.predictions.squeeze(-1))}
