





import os

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

import datasets


lowerCAmelCase_    =    datasets.logging.get_logger(__name__)


lowerCAmelCase_    =    '''\
@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_    =    '''\
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_    =    '''
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_    =    {
    '''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					):






			def       lowerCamelCase  (self   )					->       int:


							'''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       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''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_						:   List[Any]           =  '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
											snake_case_						:   str           =  self.config_name.lower()

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Any:


							'''simple docstring'''



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







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_    =    get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')



@require_sentencepiece
@require_tokenizers
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				GPTSwaTokenizer
			lowerCamelCase_					:					str	      =				False
			lowerCamelCase_					:					str	      =				True
			lowerCamelCase_					:					List[Any]	      =				False






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:   Union[str, Any]           =  GPTSwaTokenizer(__magic_name__ ,							eos_token='''<unk>''' ,							bos_token='''<unk>''' ,							pad_token='''<unk>'''   )

							tokenizer.save_pretrained(self.tmpdirname   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  '''This is a test'''
							snake_case_						:   str           =  '''This is a test'''
							return input_text, output_text






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  '''<s>'''
							snake_case_						:   Any           =  1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__   ) ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0] ,							'''<unk>'''   )
							self.assertEqual(vocab_keys[1] ,							'''<s>'''   )
							self.assertEqual(vocab_keys[-1] ,							'''j'''   )
							self.assertEqual(len(__magic_name__   ) ,							2000   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size ,							2000   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   str           =  GPTSwaTokenizer(__magic_name__   )

							snake_case_						:   List[str]           =  tokenizer.tokenize('''This is a test'''   )
							self.assertListEqual(__magic_name__ ,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   ) ,							[465, 287, 265, 631, 842]   )

							snake_case_						:   Optional[int]           =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''] ,							)
							# fmt: on

							snake_case_						:   int           =  tokenizer.convert_tokens_to_ids(__magic_name__   )
							self.assertListEqual(
							    __magic_name__ ,							[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,							)

							snake_case_						:   Dict           =  tokenizer.convert_ids_to_tokens(__magic_name__   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']   )
							# fmt: on






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


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  GPTSwaTokenizer(__magic_name__   )
							snake_case_						:   Tuple           =  ['''This is a test''', '''I was born in 92000, and this is falsé.''']
							snake_case_						:   Optional[int]           =  [
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertListEqual(tokenizer.encode_fast(__magic_name__   ) ,							__magic_name__   )

							# Test that decode_fast returns the input text
							for text, token_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertEqual(tokenizer.decode_fast(__magic_name__   ) ,							__magic_name__   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  [
							    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
							    '''Hey there, how are you doing this fine day?''',
							    '''This is a text with a trailing spaces followed by a dot     .''',
							    '''Häj sväjs lillebrör! =)''',
							    '''Det är inget fel på Mr. Cool''',
							]

							# fmt: off
							snake_case_						:   str           =  {'''input_ids''': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=__magic_name__ ,							model_name='''AI-Sweden/gpt-sw3-126m''' ,							sequences=__magic_name__ ,							)
