

"""simple docstring"""




import os

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

import datasets


A_	:			Union[str, Any]  	=      datasets.logging.get_logger(__name__)


A_	:			List[Any]  	=      '\\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'

A_	:			Union[str, 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'

A_	:			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'

A_	:			List[Any]  	=      {
    '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    ):


					'''simple docstring'''






					def   __A      (			self      ):
						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   ,	A__      ):
						# check that config name specifies a valid BLEURT model
						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__							:   Optional[Any]					       =		"""bleurt-base-128"""

						if self.config_name.lower() in CHECKPOINT_URLS:
							A__							:   Tuple					       =		self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
							A__							:   List[str]					       =		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__							:   int					       =		dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
						A__							:   Union[str, Any]					       =		score.BleurtScorer(os.path.join(A__   ,	A__      )      )






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   int					       =		self.scorer.score(references=A__   ,	candidates=A__      )
						return {"scores": scores}






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



