
'''simple docstring'''
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							None
				lowerCAmelCase_     			=							BloomTokenizerFast
				lowerCAmelCase_     			=							BloomTokenizerFast
				lowerCAmelCase_     			=							True
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							"""tokenizer_file"""
				lowerCAmelCase_     			=							{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											super().setUp()
											UpperCamelCase   			=     BloomTokenizerFast.from_pretrained('bigscience/tokenizer'     )
											tokenizer.save_pretrained(self.tmpdirname     )







				def 						UpperCAmelCase_    (       self						,				**A_     )->		Any:







											'''simple docstring'''






											kwargs.update(self.special_tokens_map     )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname						,				**A_     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_rust_tokenizer()

											UpperCamelCase   			=     ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											UpperCamelCase   			=     [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

											UpperCamelCase   			=     tokenizer.batch_encode_plus(A_     )['input_ids']
											self.assertListEqual(A_						,				A_     )

											UpperCamelCase   			=     tokenizer.batch_decode(A_     )
											self.assertListEqual(A_						,				A_     )







				def 						UpperCAmelCase_    (       self						,				A_=6     )->		Optional[int]:







											'''simple docstring'''






											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																									UpperCamelCase   			=     self.rust_tokenizer_class.from_pretrained(A_						,				**A_     )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									UpperCamelCase   			=     'This is a simple input'
																									UpperCamelCase   			=     ['This is a simple input 1', 'This is a simple input 2']
																									UpperCamelCase   			=     ('This is a simple input', 'This is a pair')
																									UpperCamelCase   			=     [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(A_						,				max_length=A_     )
																																tokenizer_r.encode_plus(A_						,				max_length=A_     )

																																tokenizer_r.batch_encode_plus(A_						,				max_length=A_     )
																																tokenizer_r.encode(A_						,				max_length=A_     )
																																tokenizer_r.batch_encode_plus(A_						,				max_length=A_     )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'     )

																									UpperCamelCase   			=     None  # Hotfixing padding = None
																									self.assertRaises(A_						,				tokenizer_r.encode						,				A_						,				max_length=A_						,				padding='max_length'     )

																									# Simple input
																									self.assertRaises(A_						,				tokenizer_r.encode_plus						,				A_						,				max_length=A_						,				padding='max_length'     )

																									# Simple input
																									self.assertRaises(
																									    A_						,				tokenizer_r.batch_encode_plus						,				A_						,				max_length=A_						,				padding='max_length'						,				)

																									# Pair input
																									self.assertRaises(A_						,				tokenizer_r.encode						,				A_						,				max_length=A_						,				padding='max_length'     )

																									# Pair input
																									self.assertRaises(A_						,				tokenizer_r.encode_plus						,				A_						,				max_length=A_						,				padding='max_length'     )

																									# Pair input
																									self.assertRaises(
																									    A_						,				tokenizer_r.batch_encode_plus						,				A_						,				max_length=A_						,				padding='max_length'						,				)







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_rust_tokenizer()
											UpperCamelCase   			=     load_dataset('xnli'						,				'all_languages'						,				split='test'						,				streaming=A_     )

											UpperCamelCase   			=     next(iter(A_     )     )['premise']  # pick up one data
											UpperCamelCase   			=     list(sample_data.values()     )

											UpperCamelCase   			=     list(map(tokenizer.encode						,				A_     )     )
											UpperCamelCase   			=     [tokenizer.decode(A_						,				clean_up_tokenization_spaces=A_     ) for x in output_tokens]
											self.assertListEqual(A_						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )						,				1     )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )						,				1     )








'''simple docstring'''







import os

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

import datasets


lowerCAmelCase     :  Optional[int]											=    datasets.logging.get_logger(__name__)


lowerCAmelCase     :  List[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     :  List[Any]											=    """\
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     :  Optional[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 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			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 a						(			self   ,						snake_case__ ):

            '''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     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

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







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

