



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

import datasets


snake_case     :    Dict						=    '\\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'

snake_case     :    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'


snake_case     :    List[str]						=    '\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 			_lowercase      (							self   :  Tuple):
											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 			_lowercase      (							self   :  Dict      ,		_A   :  List[str]):
											if self.config_name == "default":
																	A__						:				Optional[int]         =			"moussaKam/frugalscore_tiny_bert-base_bert-score"
											else:
																	A__						:				str         =			self.config_name
											A__						:				str         =			AutoModelForSequenceClassification.from_pretrained(_A)
											A__						:				Union[str, Any]         =			AutoTokenizer.from_pretrained(_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  List[str]      ,		_A   :  List[Any]      ,		_A   :  Dict=32      ,		_A   :  Optional[Any]=128      ,		_A   :  Dict=None      ,		):
											assert len(_A) == len(
											    _A), "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:
																	A__						:				str         =			"gpu" if torch.cuda.is_available() else "cpu"
											A__						:				Optional[int]         =			TrainingArguments(
											    "trainer"      ,		fpaa=(device == "gpu")      ,		per_device_eval_batch_size=_A      ,		report_to="all"      ,		no_cuda=(device == "cpu")      ,		log_level="warning"      ,		)
											A__						:				Optional[int]         =			{"sentence1": predictions, "sentence2": references}
											A__						:				Dict         =			datasets.Dataset.from_dict(_A)

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

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




import unittest

import numpy as np
from datasets import load_dataset

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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor




class 				lowerCAmelCase__	(						unittest.TestCase    ):







					def __init__(							self   :  int      ,		_A   :  Optional[Any]      ,		_A   :  Optional[int]=7      ,		_A   :  str=3      ,		_A   :  Optional[int]=30      ,		_A   :  List[str]=400      ,		_A   :  Tuple=True      ,		_A   :  List[Any]=None      ,		_A   :  Dict=True      ,		_A   :  Tuple=[0.5, 0.5, 0.5]      ,		_A   :  List[str]=[0.5, 0.5, 0.5]      ,		_A   :  int=False      ,		):
											A__						:				Tuple         =			size if size is not None else {"height": 30, "width": 30}
											A__						:				Tuple         =			parent
											A__						:				str         =			batch_size
											A__						:				int         =			num_channels
											A__						:				Union[str, Any]         =			min_resolution
											A__						:				List[str]         =			max_resolution
											A__						:				int         =			do_resize
											A__						:				Tuple         =			size
											A__						:				Union[str, Any]         =			do_normalize
											A__						:				Optional[int]         =			image_mean
											A__						:				List[Any]         =			image_std
											A__						:				Dict         =			do_reduce_labels







					def 			_lowercase      (							self   :  Optional[int]):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_reduce_labels": self.do_reduce_labels,
											}




def 						snake_case__							(       )		->		Tuple:







						"""simple docstring"""

						A__						:				Union[str, Any]         =			load_dataset("hf-internal-testing/fixtures_ade20k"				,  split="test"							)

						A__						:				Any         =			Image.open(dataset[0]["file"]							)
						A__						:				Any         =			Image.open(dataset[1]["file"]							)

						return image, map
def 						snake_case__							(       )		->		List[str]:







						"""simple docstring"""

						A__						:				Tuple         =			load_dataset("hf-internal-testing/fixtures_ade20k"				,  split="test"							)

						A__						:				Tuple         =			Image.open(dataset[0]["file"]							)
						A__						:				int         =			Image.open(dataset[1]["file"]							)
						A__						:				str         =			Image.open(dataset[2]["file"]							)
						A__						:				List[Any]         =			Image.open(dataset[3]["file"]							)

						return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     int     =    SegformerImageProcessor if is_vision_available() else None







					def 			_lowercase      (							self   :  int):
											A__						:				int         =			SegformerImageProcessingTester(self)







					@property
					def 			_lowercase      (							self   :  str):
											return self.image_processor_tester.prepare_image_processor_dict()







					def 			_lowercase      (							self   :  Optional[Any]):
											A__						:				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_normalize"))
											self.assertTrue(hasattr(_A      ,		"image_mean"))
											self.assertTrue(hasattr(_A      ,		"image_std"))
											self.assertTrue(hasattr(_A      ,		"do_reduce_labels"))







					def 			_lowercase      (							self   :  Tuple):
											A__						:				Union[str, Any]         =			self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size      ,		{"height": 30, "width": 30})
											self.assertEqual(image_processor.do_reduce_labels      ,		_A)

											A__						:				Union[str, Any]         =			self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42      ,		reduce_labels=_A)
											self.assertEqual(image_processor.size      ,		{"height": 42, "width": 42})
											self.assertEqual(image_processor.do_reduce_labels      ,		_A)







					def 			_lowercase      (							self   :  Union[str, Any]):
											pass







					def 			_lowercase      (							self   :  Dict):
											# Initialize image_processing
											A__						:				Tuple         =			self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											A__						:				Union[str, Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		Image.Image)

											# Test not batched input
											A__						:				Dict         =			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.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)

											# Test batched
											A__						:				List[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.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)







					def 			_lowercase      (							self   :  Optional[int]):
											# Initialize image_processing
											A__						:				Tuple         =			self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											A__						:				Tuple         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		np.ndarray)

											# Test not batched input
											A__						:				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.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)

											# Test batched
											A__						:				Optional[int]         =			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.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)







					def 			_lowercase      (							self   :  Tuple):
											# Initialize image_processing
											A__						:				str         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				str         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)

											# Test not batched input
											A__						:				int         =			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.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)

											# Test batched
											A__						:				List[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.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)







					def 			_lowercase      (							self   :  Optional[int]):
											# Initialize image_processing
											A__						:				str         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				Optional[Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											A__						:				List[str]         =			[]
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)
																	maps.append(torch.zeros(image.shape[-2:]).long())

											# Test not batched input
											A__						:				Any         =			image_processing(image_inputs[0]      ,		maps[0]      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        1,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)

											# Test batched
											A__						:				int         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)

											# Test not batched input (PIL images)
											A__     ,							A__						:				List[str]         =			prepare_semantic_single_inputs()

											A__						:				Any         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        1,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)

											# Test batched input (PIL images)
											A__     ,							A__						:				Union[str, Any]         =			prepare_semantic_batch_inputs()

											A__						:				str         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        2,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        2,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)







					def 			_lowercase      (							self   :  Union[str, Any]):
											# Initialize image_processing
											A__						:				List[str]         =			self.image_processing_class(**self.image_processor_dict)

											# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
											A__     ,							A__						:				Tuple         =			prepare_semantic_single_inputs()
											A__						:				Dict         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 150)

											A__						:				str         =			True
											A__						:				int         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)