
import os

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

import datasets


__a         =   datasets.logging.get_logger(__name__)


__a         =   '\\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'

__a         =   '\\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'

__a         =   '\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'

__a         =   {
    '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 					lowercase__(							datasets.Metric				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[Any]			)			->	str:
					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 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple			)			->	Dict:
					# 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\').'''			)
									lowercase_	    =   '''bleurt-base-128'''

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

					elif self.config_name.upper() in CHECKPOINT_URLS:
									lowercase_	    =   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
					lowercase_	    =   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]			)
					lowercase_	    =   score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			)




	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Dict:
					lowercase_	    =   self.scorer.score(references=SCREAMING_SNAKE_CASE_			,				candidates=SCREAMING_SNAKE_CASE_			)
					return {"scores": scores}








'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Union[str, Any]				   =					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Any				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Any				   =					torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :List[Any]				   =					torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)