


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


a : List[str]		=				'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   _ask_options(
			    '''In which compute environment are you running?'''       ,			['''This machine''', '''AWS (Amazon SageMaker)''']       ,			_convert_compute_environment       ,			)
			if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
						__lowercase 					=   get_sagemaker_input()
			else:
						__lowercase 					=   get_cluster_input()
			return config


def 		lowercase_  (     _UpperCamelCase=None  ):


			'''simple docstring'''







			if subparsers is not None:
						__lowercase 					=   subparsers.add_parser('''config'''       ,			description=_A  )
			else:
						__lowercase 					=   argparse.ArgumentParser('''Accelerate config command'''       ,			description=_A  )

			parser.add_argument(
			    '''--config_file'''       ,			default=_A       ,			help=(
			        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
			        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
			        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
			        '''with \'huggingface\'.'''
			    )       ,			)

			if subparsers is not None:
						parser.set_defaults(func=_A  )
			return parser


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   get_user_input()
			if args.config_file is not None:
						__lowercase 					=   args.config_file
			else:
						if not os.path.isdir(_A  ):
									os.makedirs(_A  )
						__lowercase 					=   default_yaml_config_file

			if config_file.endswith('''.json'''  ):
						config.to_json_file(_A  )
			else:
						config.to_yaml_file(_A  )
			print(F'accelerate configuration saved at {config_file}'  )


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   config_command_parser()
			__lowercase 					=   parser.parse_args()
			config_command(_A  )


if __name__ == "__main__":
					main()






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}


