


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 A  (       unittest.TestCase					):






      def __init__( self						:					Union[str, Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Optional[Any]=7      ,       lowercase_						:					Union[str, Any]=3      ,       lowercase_						:					Any=18      ,       lowercase_						:					Optional[int]=30      ,       lowercase_						:					int=400      ,       lowercase_						:					List[Any]=True      ,       lowercase_						:					List[str]=None      ,       lowercase_						:					Optional[Any]=True      ,       lowercase_						:					Tuple=None      ,       lowercase_						:					str=True      ,       lowercase_						:					Any=[0.48145466, 0.4578275, 0.40821073]      ,       lowercase_						:					Optional[int]=[0.26862954, 0.26130258, 0.27577711]      ,       lowercase_						:					Dict=True      ,       ) ->						str:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					int			) ->						Tuple:
             """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 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					Any=False      ,       lowercase_						:					str=False      ,       lowercase_						:					Union[str, Any]=False			) ->						Tuple:
             """simple docstring"""




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

             if equal_resolution:
                    _lowerCamelCase	:		int          =[]
                    for i in range(self.batch_size			):
                           image_inputs.append(
                               np.random.randint(
                                   255      ,       size=(self.num_channels, self.max_resolution, self.max_resolution)      ,       dtype=np.uinta			)			)
             else:
                    _lowerCamelCase	:		Dict          =[]
                    for i in range(self.batch_size			):
                           _lowerCamelCase     ,					_lowerCamelCase	:		str          =np.random.choice(np.arange(self.min_resolution      ,       self.max_resolution			)      ,       2			)
                           image_inputs.append(np.random.randint(255      ,       size=(self.num_channels, width, height)      ,       dtype=np.uinta			)			)

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

             if torchify:
                    _lowerCamelCase	:		Tuple          =[torch.from_numpy(lowercase_			) for x in image_inputs]

             return image_inputs




@require_torch
@require_vision
class A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  Tuple				=CLIPImageProcessor if is_vision_available() else None






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =CLIPImageProcessingTester(self			)






      @property
      def 	lowerCamelCase	( self						:					Tuple			) ->						Dict:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					int			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_convert_rgb'			)			)






      def 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =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}			)

             _lowerCamelCase	:		Optional[Any]          =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 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		Any          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       Image.Image			)

             # Test not batched input
             _lowerCamelCase	:		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
             _lowerCamelCase	:		Optional[Any]          =image_processing(lowercase_      ,       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 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.image_processing_class(**self.image_processor_dict			)
             # create random numpy tensors
             _lowerCamelCase	:		Tuple          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,       numpify=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       np.ndarray			)

             # Test not batched input
             _lowerCamelCase	:		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
             _lowerCamelCase	:		str          =image_processing(lowercase_      ,       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 	lowerCamelCase	( self						:					List[str]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.image_processing_class(**self.image_processor_dict			)
             # create random PyTorch tensors
             _lowerCamelCase	:		Dict          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,       torchify=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       torch.Tensor			)

             # Test not batched input
             _lowerCamelCase	:		List[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
             _lowerCamelCase	:		str          =image_processing(lowercase_      ,       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 A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  List[str]				=CLIPImageProcessor if is_vision_available() else None






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		int          =CLIPImageProcessingTester(self      ,       num_channels=4			)
             _lowerCamelCase	:		Any          =3






      @property
      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Tuple:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_convert_rgb'			)			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[int]:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					Any			) ->						Any:
             """simple docstring"""




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

             # Test not batched input
             _lowerCamelCase	:		Any          =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
             _lowerCamelCase	:		Optional[Any]          =image_processing(lowercase_      ,       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						 =	'\\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						 =	'\\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						 =	'\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 A  (       datasets.Metric					):






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

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

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


