


"""simple docstring"""






import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import YolosForObjectDetection, YolosModel
	from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=13       ,		UpperCAmelCase__=[30, 30]       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=32       ,		UpperCAmelCase__=5       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=37       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=10       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=8       ,		UpperCAmelCase__=10       ,		):
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				batch_size
								SCREAMING_SNAKE_CASE__     							=				image_size
								SCREAMING_SNAKE_CASE__     							=				patch_size
								SCREAMING_SNAKE_CASE__     							=				num_channels
								SCREAMING_SNAKE_CASE__     							=				is_training
								SCREAMING_SNAKE_CASE__     							=				use_labels
								SCREAMING_SNAKE_CASE__     							=				hidden_size
								SCREAMING_SNAKE_CASE__     							=				num_hidden_layers
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads
								SCREAMING_SNAKE_CASE__     							=				intermediate_size
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				hidden_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				type_sequence_label_size
								SCREAMING_SNAKE_CASE__     							=				initializer_range
								SCREAMING_SNAKE_CASE__     							=				num_labels
								SCREAMING_SNAKE_CASE__     							=				scope
								SCREAMING_SNAKE_CASE__     							=				n_targets
								SCREAMING_SNAKE_CASE__     							=				num_detection_tokens
								# we set the expected sequence length (which is used in several tests)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
								SCREAMING_SNAKE_CASE__     							=				(image_size[1] // patch_size) * (image_size[0] // patch_size)
								SCREAMING_SNAKE_CASE__     							=				num_patches + 1 + self.num_detection_tokens



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]     )

								SCREAMING_SNAKE_CASE__     							=				None
								if self.use_labels:
												# labels is a list of Dict (each Dict being the labels for a given example in the batch)
												SCREAMING_SNAKE_CASE__     							=				[]
												for i in range(self.batch_size     ):
																SCREAMING_SNAKE_CASE__     							=				{}
																SCREAMING_SNAKE_CASE__     							=				torch.randint(
																    high=self.num_labels       ,		size=(self.n_targets,)       ,		device=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				torch.rand(self.n_targets       ,		4       ,		device=UpperCAmelCase__     )
																labels.append(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				self.get_config()

								return config, pixel_values, labels



				def   lowerCAmelCase__		(						self     ):
								return YolosConfig(
								    image_size=self.image_size       ,		patch_size=self.patch_size       ,		num_channels=self.num_channels       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		intermediate_size=self.intermediate_size       ,		hidden_act=self.hidden_act       ,		hidden_dropout_prob=self.hidden_dropout_prob       ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,		is_decoder=UpperCAmelCase__       ,		initializer_range=self.initializer_range       ,		num_detection_tokens=self.num_detection_tokens       ,		num_labels=self.num_labels       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				YolosModel(config=UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,		(self.batch_size, self.expected_seq_len, self.hidden_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				YolosForObjectDetection(UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()

								SCREAMING_SNAKE_CASE__     							=				model(pixel_values=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )

								self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_detection_tokens, self.num_labels + 1)     )
								self.parent.assertEqual(result.pred_boxes.shape       ,		(self.batch_size, self.num_detection_tokens, 4)     )

								SCREAMING_SNAKE_CASE__     							=				model(pixel_values=UpperCAmelCase__       ,		labels=UpperCAmelCase__     )

								self.parent.assertEqual(result.loss.shape       ,		()     )
								self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_detection_tokens, self.num_labels + 1)     )
								self.parent.assertEqual(result.pred_boxes.shape       ,		(self.batch_size, self.num_detection_tokens, 4)     )






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				config_and_inputs
								SCREAMING_SNAKE_CASE__     							=				{"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class 			lowerCamelCase_		(			lowercase       ,  lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Dict      							=					(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
				_lowerCAmelCase      :      List[str]      							=					(
				    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
				)

				_lowerCAmelCase      :      int      							=					False
				_lowerCAmelCase      :      Union[str, Any]      							=					False
				_lowerCAmelCase      :      Union[str, Any]      							=					False
				_lowerCAmelCase      :      List[Any]      							=					False



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=False     ):
								SCREAMING_SNAKE_CASE__     							=				super()._prepare_for_class(UpperCAmelCase__       ,		UpperCAmelCase__       ,		return_labels=UpperCAmelCase__     )

								if return_labels:
												if model_class.__name__ == "YolosForObjectDetection":
																SCREAMING_SNAKE_CASE__     							=				[]
																for i in range(self.model_tester.batch_size     ):
																				SCREAMING_SNAKE_CASE__     							=				{}
																				SCREAMING_SNAKE_CASE__     							=				torch.ones(
																				    size=(self.model_tester.n_targets,)       ,		device=UpperCAmelCase__       ,		dtype=torch.long     )
																				SCREAMING_SNAKE_CASE__     							=				torch.ones(
																				    self.model_tester.n_targets       ,		4       ,		device=UpperCAmelCase__       ,		dtype=torch.float     )
																				labels.append(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				labels

								return inputs_dict



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				YolosModelTester(self     )
								SCREAMING_SNAKE_CASE__     							=				ConfigTester(self       ,		config_class=UpperCAmelCase__       ,		has_text_modality=UpperCAmelCase__       ,		hidden_size=37     )



				def   lowerCAmelCase__		(						self     ):
								self.config_tester.run_common_tests()



				def   lowerCAmelCase__		(						self     ):
								# YOLOS does not use inputs_embeds
								pass



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												self.assertIsInstance(model.get_input_embeddings()       ,		(nn.Module)     )
												SCREAMING_SNAKE_CASE__     							=				model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCAmelCase__       ,		nn.Linear     )     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				inspect.signature(model.forward     )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												SCREAMING_SNAKE_CASE__     							=				[*signature.parameters.keys()]

												SCREAMING_SNAKE_CASE__     							=				["pixel_values"]
												self.assertListEqual(arg_names[:1]       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE__     							=				True

								# in YOLOS, the seq_len is different
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.expected_seq_len
								for model_class in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				False
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												model.to(UpperCAmelCase__     )
												model.eval()
												with torch.no_grad():
																SCREAMING_SNAKE_CASE__     							=				model(**self._prepare_for_class(UpperCAmelCase__       ,		UpperCAmelCase__     )     )
												SCREAMING_SNAKE_CASE__     							=				outputs.attentions
												self.assertEqual(len(UpperCAmelCase__     )       ,		self.model_tester.num_hidden_layers     )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												model.to(UpperCAmelCase__     )
												model.eval()
												with torch.no_grad():
																SCREAMING_SNAKE_CASE__     							=				model(**self._prepare_for_class(UpperCAmelCase__       ,		UpperCAmelCase__     )     )
												SCREAMING_SNAKE_CASE__     							=				outputs.attentions
												self.assertEqual(len(UpperCAmelCase__     )       ,		self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]     )       ,		[self.model_tester.num_attention_heads, seq_len, seq_len]       ,		)
												SCREAMING_SNAKE_CASE__     							=				len(UpperCAmelCase__     )

												# Check attention is always last and order is fine
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												model.to(UpperCAmelCase__     )
												model.eval()
												with torch.no_grad():
																SCREAMING_SNAKE_CASE__     							=				model(**self._prepare_for_class(UpperCAmelCase__       ,		UpperCAmelCase__     )     )

												SCREAMING_SNAKE_CASE__     							=				1
												self.assertEqual(out_len + added_hidden_states       ,		len(UpperCAmelCase__     )     )

												SCREAMING_SNAKE_CASE__     							=				outputs.attentions

												self.assertEqual(len(UpperCAmelCase__     )       ,		self.model_tester.num_hidden_layers     )
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]     )       ,		[self.model_tester.num_attention_heads, seq_len, seq_len]       ,		)



				def   lowerCAmelCase__		(						self     ):
								def check_hidden_states_output(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     )
												model.to(UpperCAmelCase__     )
												model.eval()

												with torch.no_grad():
																SCREAMING_SNAKE_CASE__     							=				model(**self._prepare_for_class(UpperCAmelCase__       ,		UpperCAmelCase__     )     )

												SCREAMING_SNAKE_CASE__     							=				outputs.hidden_states

												SCREAMING_SNAKE_CASE__     							=				getattr(
												    self.model_tester       ,		"expected_num_hidden_layers"       ,		self.model_tester.num_hidden_layers + 1     )
												self.assertEqual(len(UpperCAmelCase__     )       ,		UpperCAmelCase__     )

												# YOLOS has a different seq_length
												SCREAMING_SNAKE_CASE__     							=				self.model_tester.expected_seq_len

												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]     )       ,		[seq_length, self.model_tester.hidden_size]       ,		)

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				True
												check_hidden_states_output(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												SCREAMING_SNAKE_CASE__     							=				True

												check_hidden_states_output(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_object_detection(*UpperCAmelCase__     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												SCREAMING_SNAKE_CASE__     							=				YolosModel.from_pretrained(UpperCAmelCase__     )
												self.assertIsNotNone(UpperCAmelCase__     )


def __lowercase     (      ):

				'''simple docstring'''





				SCREAMING_SNAKE_CASE__     							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
				return image


@require_torch
@require_vision
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				@cached_property
				def   lowerCAmelCase__		(						self     ):
								return AutoImageProcessor.from_pretrained("hustvl/yolos-small"     ) if is_vision_available() else None






				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				YolosForObjectDetection.from_pretrained("hustvl/yolos-small"     ).to(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				self.default_image_processor
								SCREAMING_SNAKE_CASE__     							=				prepare_img()
								SCREAMING_SNAKE_CASE__     							=				image_processor(images=UpperCAmelCase__       ,		return_tensors="pt"     ).to(UpperCAmelCase__     )

								# forward pass
								with torch.no_grad():
												SCREAMING_SNAKE_CASE__     							=				model(inputs.pixel_values     )

								# verify outputs
								SCREAMING_SNAKE_CASE__     							=				torch.Size((1, 100, 92)     )
								self.assertEqual(outputs.logits.shape       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				torch.tensor(
								    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]       ,		device=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				torch.tensor(
								    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]       ,		device=UpperCAmelCase__     )
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )
								self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )

								# verify postprocessing
								SCREAMING_SNAKE_CASE__     							=				image_processor.post_process_object_detection(
								    UpperCAmelCase__       ,		threshold=0.3       ,		target_sizes=[image.size[::-1]]     )[0]
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]     ).to(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				[75, 75, 17, 63, 17]
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]     ).to(UpperCAmelCase__     )

								self.assertEqual(len(results["scores"]     )       ,		5     )
								self.assertTrue(torch.allclose(results["scores"]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )
								self.assertSequenceEqual(results["labels"].tolist()       ,		UpperCAmelCase__     )
								self.assertTrue(torch.allclose(results["boxes"][0, :]       ,		UpperCAmelCase__     )     )




"""simple docstring"""






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCamelCase     =  datasets.logging.get_logger(__name__)


_lowerCamelCase     =  '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_lowerCamelCase     =  '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_lowerCamelCase     =  '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_lowerCamelCase     =  {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


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





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,		citation=_CITATION       ,		homepage="https://github.com/google-research/bleurt"       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"       ,		id="sequence"     ),
								            "references": datasets.Value("string"       ,		id="sequence"     ),
								        }     )       ,		codebase_urls=["https://github.com/google-research/bleurt"]       ,		reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# check that config name specifies a valid BLEURT model
								if self.config_name == "default":
												logger.warning(
												    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
												    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."     )
												SCREAMING_SNAKE_CASE__     							=				"bleurt-base-128"

								if self.config_name.lower() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.upper()

								else:
												raise KeyError(
												    f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''     )

								# download the model checkpoint specified by self.config_name and set up the scorer
								SCREAMING_SNAKE_CASE__     							=				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
								SCREAMING_SNAKE_CASE__     							=				score.BleurtScorer(os.path.join(UpperCAmelCase__       ,		UpperCAmelCase__     )     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.scorer.score(references=UpperCAmelCase__       ,		candidates=UpperCAmelCase__     )
								return {"scores": scores}
