


'''simple docstring'''

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

import datasets


lowercase__					  =		"\\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"

lowercase__					  =		"\\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"


lowercase__					  =		"\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     __SCREAMING_SNAKE_CASE       (   datasets.Metric			):






     def 			a_       (      self		)							->     Optional[Any]:
          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  ,				__UpperCamelCase		)							->     List[str]:
          if self.config_name == "default":
               _a            =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
          else:
               _a            =		self.config_name
          _a            =		AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase		)
          _a            =		AutoTokenizer.from_pretrained(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase=32  ,				__UpperCamelCase=128  ,				__UpperCamelCase=None  ,				)							->     List[Any]:
          assert len(__UpperCamelCase		) == len(
              __UpperCamelCase		), "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            =		"gpu" if torch.cuda.is_available() else "cpu"
          _a            =		TrainingArguments(
              "trainer"  ,				fpaa=(device == "gpu")  ,				per_device_eval_batch_size=__UpperCamelCase  ,				report_to="all"  ,				no_cuda=(device == "cpu")  ,				log_level="warning"  ,				)
          _a            =		{"sentence1": predictions, "sentence2": references}
          _a            =		datasets.Dataset.from_dict(__UpperCamelCase		)

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

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





'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

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

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






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






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     List[Any]:
          # 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		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

