"""simple docstring"""


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

import datasets


a							='\\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'

a							='\\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'


a							='\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        __UpperCAmelCase						(     datasets.Metric     ):

				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     ,			_lowerCamelCase				):
										if self.config_name == "default":
																lowerCamelCase__ ="moussaKam/frugalscore_tiny_bert-base_bert-score"
										else:
																lowerCamelCase__ =self.config_name
										lowerCamelCase__ =AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase				)
										lowerCamelCase__ =AutoTokenizer.from_pretrained(_lowerCamelCase				)






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=32     ,			_lowerCamelCase=128     ,			_lowerCamelCase=None     ,			):
										assert len(_lowerCamelCase				) == len(
										    _lowerCamelCase				), "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__ ="gpu" if torch.cuda.is_available() else "cpu"
										lowerCamelCase__ =TrainingArguments(
										    "trainer"     ,			fpaa=(device == "gpu")     ,			per_device_eval_batch_size=_lowerCamelCase     ,			report_to="all"     ,			no_cuda=(device == "cpu")     ,			log_level="warning"     ,			)
										lowerCamelCase__ ={"sentence1": predictions, "sentence2": references}
										lowerCamelCase__ =datasets.Dataset.from_dict(_lowerCamelCase				)

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

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



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


