





'''simple docstring'''



import os

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

import datasets


_SCREAMING_SNAKE_CASE   =      datasets.logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =      '''\
@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}
}
'''

_SCREAMING_SNAKE_CASE   =      '''\
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.
'''

_SCREAMING_SNAKE_CASE   =      '''
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]
'''

_SCREAMING_SNAKE_CASE   =      {
    '''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   _UpperCAmelCase       (self	)							->							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   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[Any]:







				'''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(_lowerCamelCase       ,_lowerCamelCase	)	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[Any]:







				'''simple docstring'''





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












'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "gpt_neox_japanese"


	def __init__(self       ,_lowerCamelCase=32000       ,_lowerCamelCase=2560       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase=4       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=1.0_0       ,_lowerCamelCase=10000       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=True       ,_lowerCamelCase=31996       ,_lowerCamelCase=31999       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.0       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_multiple_size
				__lowercase			  =							hidden_act
				__lowercase			  =							rotary_pct
				__lowercase			  =							rotary_emb_base
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							attention_dropout
				__lowercase			  =							hidden_dropout





