




'''simple docstring'''


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_snake_case   :   str     							=	'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'

class     A					(	unittest.TestCase  ,_a					):







							def      __lowerCAmelCase    (						self :						Any      )    ->							Dict:


														"""simple docstring"""


														_a			  =       load_tool('''text-question-answering'''      )
														self.tool.setup()
														_a			  =       load_tool('''text-question-answering'''							,    remote=lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.tool(lowerCAmelCase_							,    '''What did Hugging Face do in April 2021?'''      )
														self.assertEqual(lowerCAmelCase_							,    '''launched the BigScience Research Workshop'''      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self.remote_tool(lowerCAmelCase_							,    '''What did Hugging Face do in April 2021?'''      )
														self.assertEqual(lowerCAmelCase_							,    '''launched the BigScience Research Workshop'''      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.tool(text=lowerCAmelCase_							,    question='''What did Hugging Face do in April 2021?'''      )
														self.assertEqual(lowerCAmelCase_							,    '''launched the BigScience Research Workshop'''      )







							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       self.remote_tool(text=lowerCAmelCase_							,    question='''What did Hugging Face do in April 2021?'''      )
														self.assertEqual(lowerCAmelCase_							,    '''launched the BigScience Research Workshop'''      )







'''simple docstring'''


import os

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

import datasets


_snake_case   :   Any     							=	datasets.logging.get_logger(__name__)


_snake_case   :   Tuple     							=	'\\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'

_snake_case   :   Optional[Any]     							=	'\\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'

_snake_case   :   Optional[int]     							=	'\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'

_snake_case   :   Dict     							=	{
    '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     A					(	datasets.Metric					):







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""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      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str      )    ->							Tuple:


														"""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\').'''      )
																					_a			  =       '''bleurt-base-128'''

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

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







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[int]:


														"""simple docstring"""


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

