

"""simple docstring"""


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class    snake_case     (    unittest.TestCase,							snake_case_  ):

				def 							lowercase_					(     self							:    Tuple)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      load_tool("text-classification")
						self.tool.setup()
						__lowerCAmelCase:	Dict   				=      load_tool("text-classification"					,  remote=_A)

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.tool("That\'s quite cool"					,  ["positive", "negative"])
						self.assertEqual(_A					,  "positive")

				def 							lowercase_					(     self							:    Tuple)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.remote_tool("That\'s quite cool"					,  ["positive", "negative"])
						self.assertEqual(_A					,  "positive")

				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self.tool(text="That\'s quite cool"					,  labels=["positive", "negative"])
						self.assertEqual(_A					,  "positive")




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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.remote_tool(text="That\'s quite cool"					,  labels=["positive", "negative"])
						self.assertEqual(_A					,  "positive")





"""simple docstring"""


import os

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

import datasets


__A						       =      datasets.logging.get_logger(__name__)


__A						       =      "\\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						       =      "\\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						       =      "\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						       =      {
    "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    snake_case     (    datasets.Metric  ):

				def 							lowercase_					(     self							:    Optional[int])->       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 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict)->       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').")
								__lowerCAmelCase:	List[str]   				=      "bleurt-base-128"

						if self.config_name.lower() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Optional[int]   				=      self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Tuple   				=      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
						__lowerCAmelCase:	Union[str, Any]   				=      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
						__lowerCAmelCase:	Dict   				=      score.BleurtScorer(os.path.join(UpperCamelCase__					,  UpperCamelCase__))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.scorer.score(references=UpperCamelCase__					,  candidates=UpperCamelCase__)
						return {"scores": scores}


