"""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__   =					"\\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"

SCREAMING_SNAKE_CASE__   =					"\\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"

SCREAMING_SNAKE_CASE__   =					"\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"

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 					lowerCAmelCase_      (     datasets.Metric   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self						,      lowerCAmelCase      ):






										"""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(lowerCAmelCase						,      lowerCAmelCase      )      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

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



"""simple docstring"""
from __future__ import annotations

from typing import Any



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     data
										snake_case        =     None

				def __iter__(			self      ):






										"""simple docstring"""

										snake_case        =     self
										snake_case        =     []
										while node:
																if node in visited:
																						raise ContainsLoopError
																visited.append(lowerCAmelCase      )
																yield node.data
																snake_case        =     node.next_node

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										try:
																list(self      )
																return False
										except ContainsLoopError:
																return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					Node(1)
					SCREAMING_SNAKE_CASE__   =					Node(2)
					SCREAMING_SNAKE_CASE__   =					Node(3)
					SCREAMING_SNAKE_CASE__   =					Node(4)
					print(root_node.has_loop)  # False
					SCREAMING_SNAKE_CASE__   =					root_node.next_node
					print(root_node.has_loop)  # True

					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					print(root_node.has_loop)  # False

					SCREAMING_SNAKE_CASE__   =					Node(1)
					print(root_node.has_loop)  # False


