'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Optional[int]     		= IFInpaintingPipeline
						A_			:							int     		= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
						A_			:							List[str]     		= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						A_			:							Union[str, Any]     		= PipelineTesterMixin.required_optional_params - {'latents'}




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				str:
													return self._get_dummy_components()




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Tuple=0      )     ->				List[str]:
													if str(UpperCamelCase_      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :str						    = torch.manual_seed(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = floats_tensor((1, 3, 32, 32)							,							rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = floats_tensor((1, 3, 32, 32)							,							rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'image': image,
													    'mask_image': mask_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      __lowerCamelCase       (    self :       List[Any]      )     ->				List[str]:
													self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Dict:
													self._test_save_load_optional_components()




						@unittest.skipIf(torch_device != 'cuda'							,							reason='float16 requires CUDA'      )
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Dict:
													# 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      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[str]:
													self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Optional[int]:
													self._test_save_load_local()




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







'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

UpperCamelCase_      	=	'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCamelCase_      	=	'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCamelCase_      	=	'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,						_KWARGS_DESCRIPTION					)
class _SCREAMING_SNAKE_CASE(							datasets.Metric					):




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							homepage='https://unbabel.github.io/COMET/html/index.html'							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
													        {
													            'sources': datasets.Value('string'							,							id='sequence'      ),
													            'predictions': datasets.Value('string'							,							id='sequence'      ),
													            'references': datasets.Value('string'							,							id='sequence'      ),
													        }      )							,							codebase_urls=['https://github.com/Unbabel/COMET']							,							reference_urls=[
													        'https://github.com/Unbabel/COMET',
													        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
													        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
													    ]							,							)




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       List[str]      )     ->				Dict:
													if self.config_name == "default":
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model(self.config_name      )      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Optional[Any]=False      )     ->				Any:
													if gpus is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = 1 if torch.cuda.is_available() else 0
													SCREAMING_SNAKE_CASE__     :Dict						    = {'src': sources, 'mt': predictions, 'ref': references}
													SCREAMING_SNAKE_CASE__     :List[str]						    = [dict(zip(UpperCamelCase_							,							UpperCamelCase_      )      ) for t in zip(*data.values()      )]
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scorer.predict(UpperCamelCase_							,							gpus=UpperCamelCase_							,							progress_bar=UpperCamelCase_      )
													return {"mean_score": mean_score, "scores": scores}






