


import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf
	from tensorflow.python.eager import context
	from tensorflow.python.framework import ops

	from transformers import GradientAccumulator, create_optimizer
@require_tf
class 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								self.assertEqual(len(snake_case_						) ,       len(snake_case_						)						)
								for a, b in zip(snake_case_ ,       snake_case_						):
											self.assertAlmostEqual(snake_case_ ,       snake_case_ ,       delta=snake_case_						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   GradientAccumulator()
								accumulator([tf.constant([1.0, 2.0]						)]						)
								accumulator([tf.constant([-2.0, 1.0]						)]						)
								accumulator([tf.constant([-1.0, 2.0]						)]						)
								with self.assertRaises(snake_case_						):
											accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
								self.assertEqual(accumulator.step ,       3						)
								self.assertEqual(len(accumulator.gradients						) ,       1						)
								self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,       [-2.0, 5.0] ,       tol=1e-2						)
								accumulator.reset()
								self.assertEqual(accumulator.step ,       0						)
								self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,       [0.0, 0.0] ,       tol=1e-2						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   None
								ops.enable_eager_execution_internal()
								__lowercase 					=   tf.config.list_physical_devices('''CPU'''						)
								if len(snake_case_						) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0] ,       [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
								__lowercase 					=   tf.config.list_logical_devices(device_type='''CPU'''						)
								__lowercase 					=   tf.distribute.MirroredStrategy(devices=devices[:2]						)

								with strategy.scope():
											__lowercase 					=   GradientAccumulator()
											__lowercase 					=   tf.Variable([4.0, 3.0]						)
											__lowercase					, __lowercase 					=   create_optimizer(5e-5 ,       1_0 ,       5						)
											__lowercase 					=   tf.Variable([0.0, 0.0] ,       trainable=snake_case_						)

								def accumulate_on_replica(snake_case_						):
											accumulator([gradient]						)

								def apply_on_replica():
											optimizer.apply_gradients(list(zip(accumulator.gradients ,       [variable]						)						)						)

								@tf.function
								def accumulate(snake_case_ ,       snake_case_						):
											with strategy.scope():
														__lowercase 					=   strategy.experimental_local_results(snake_case_						)
														local_variables[0].assign(snake_case_						)
														local_variables[1].assign(snake_case_						)
														strategy.run(snake_case_ ,       args=(gradient_placeholder,)						)

								@tf.function
								def apply_grad():
											with strategy.scope():
														strategy.run(snake_case_						)

								def _check_local_values(snake_case_ ,       snake_case_						):
											__lowercase 					=   strategy.experimental_local_results(accumulator._gradients[0]						)
											self.assertListAlmostEqual(values[0].value() ,       snake_case_ ,       tol=1e-2						)
											self.assertListAlmostEqual(values[1].value() ,       snake_case_ ,       tol=1e-2						)

								accumulate([1.0, 2.0] ,       [-1.0, 1.0]						)
								accumulate([3.0, -1.0] ,       [-1.0, -1.0]						)
								accumulate([-2.0, 2.0] ,       [3.0, -2.0]						)
								self.assertEqual(accumulator.step ,       3						)
								_check_local_values([2.0, 3.0] ,       [1.0, -2.0]						)
								apply_grad()
								self.assertListAlmostEqual(variable.value() ,       [4.0, 3.0] ,       tol=1e-2						)
								accumulator.reset()
								self.assertEqual(accumulator.step ,       0						)
								_check_local_values([0.0, 0.0] ,       [0.0, 0.0]						)






import os

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

import datasets


a : Optional[int]		=				datasets.logging.get_logger(__name__)


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

a : Union[str, Any]		=				'''\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

a : Union[str, Any]		=				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

a : Tuple		=				{
    '''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 A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								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 A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								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(snake_case_ ,       snake_case_						)						)






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   self.scorer.score(references=snake_case_ ,       candidates=snake_case_						)
								return {"scores": scores}


