
import os

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

import datasets


_lowerCamelCase	:    List[Any]          =      datasets.logging.get_logger(__name__)


_lowerCamelCase	:    str          =      '''\
@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}
}
'''

_lowerCamelCase	:    Dict          =      '''\
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.
'''

_lowerCamelCase	:    str          =      '''
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]
'''

_lowerCamelCase	:    str          =      {
    '''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        SCREAMING_SNAKE_CASE__					(     datasets.Metric ):





	'''simple docstring'''



	def 			A							(  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 			A							(  self     :   Optional[int]      ,  lowercase     :   Dict     ):


				'''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\').'     )
							_snake_case          =      'bleurt-base-128'

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

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





	def 			A							(  self     :   int      ,  lowercase     :   Tuple      ,  lowercase     :   int     ):


				'''simple docstring'''


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

from __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)