




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint



@require_torch
class    lowercase_		(unittest.TestCase ):



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[[1, 2, 4], [1, 2, 3, 4]]
								SCREAMING_SNAKE_CASE_					      =							DisjunctiveConstraint(snake_case__  )
								self.assertTrue(isinstance(dc.token_ids					,							snake_case__  )  )

								with self.assertRaises(snake_case__  ):
										DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

								with self.assertRaises(snake_case__  ):
										DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(snake_case__  ):
										DisjunctiveConstraint(snake_case__  )  # fails here



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[[1, 2, 3], [1, 2, 4]]

								SCREAMING_SNAKE_CASE_					      =							DisjunctiveConstraint(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(1  )
								SCREAMING_SNAKE_CASE_					      =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.current_seq == [1]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(2  )
								SCREAMING_SNAKE_CASE_					      =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.current_seq == [1, 2]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(3  )
								SCREAMING_SNAKE_CASE_					      =							stepped is True and completed is True and reset is False
								self.assertTrue(snake_case__  )
								self.assertTrue(dc.completed  )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3]  )






						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								SCREAMING_SNAKE_CASE_					      =							DisjunctiveConstraint(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(1  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.current_seq == [1]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(2  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.current_seq == [1, 2]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(4  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.current_seq == [1, 2, 4]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(5  )
								self.assertTrue(dc.completed  )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

								dc.reset()

								SCREAMING_SNAKE_CASE_					      =							dc.update(1  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.remaining() == 3  )
								self.assertTrue(dc.current_seq == [1]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(2  )
								self.assertTrue(not dc.completed  )
								self.assertTrue(dc.remaining() == 2  )
								self.assertTrue(dc.current_seq == [1, 2]  )

								SCREAMING_SNAKE_CASE_					      =							dc.update(5  )
								self.assertTrue(dc.completed  )  # Completed!
								self.assertTrue(dc.remaining() == 0  )
								self.assertTrue(dc.current_seq == [1, 2, 5]  )



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 						_UpperCAmelCase      (     datasets.Metric							):






		def _snake_case		(						self				:     List[str]):
				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				:     Any   ,  UpperCAmelCase				:     Any):
				# 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').")
						SCREAMING_SNAKE_CASE_				:str				     =						"bleurt-base-128"

				if self.config_name.lower() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						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
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
				SCREAMING_SNAKE_CASE_				:Dict				     =						score.BleurtScorer(os.path.join(UpperCAmelCase   ,  UpperCAmelCase))







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.scorer.score(references=UpperCAmelCase   ,  candidates=UpperCAmelCase)
				return {"scores": scores}






