
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


A__   :					Optional[Any]							=   get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')


@require_sentencepiece
@require_tokenizers
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		GPTSwaTokenizer
       UpperCamelCase_   =		False
       UpperCamelCase_   =		True
       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase:			Optional[Any]       =     GPTSwaTokenizer(A_ ,				eos_token='''<unk>''' ,				bos_token='''<unk>''' ,				pad_token='''<unk>'''       )

         tokenizer.save_pretrained(self.tmpdirname       )
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			int       =     '''This is a test'''
         _lowercase:			Union[str, Any]       =     '''This is a test'''
         return input_text, output_text
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			List[Any]       =     '''<s>'''
         _lowercase:			str       =     1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_       ) ,				A_       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_       ) ,				A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			List[str]       =     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(A_       ) ,				2000       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         self.assertEqual(self.get_tokenizer().vocab_size ,				2000       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			List[Any]       =     GPTSwaTokenizer(A_       )

         _lowercase:			List[Any]       =     tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(A_ ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

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

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

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

         _lowercase:			Optional[int]       =     tokenizer.convert_ids_to_tokens(A_       )
         # fmt: off
         self.assertListEqual(
             A_ ,				['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']       )
         # fmt: on
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     GPTSwaTokenizer(A_       )
         _lowercase:			List[Any]       =     ['''This is a test''', '''I was born in 92000, and this is falsé.''']
         _lowercase:			Tuple       =     [
             [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(A_ ,				A_       ):
           self.assertListEqual(tokenizer.encode_fast(A_       ) ,				A_       )

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



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





         """simple docstring"""




         _lowercase:			Any       =     [
             '''<|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
         _lowercase:			Optional[int]       =     {'''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=A_ ,				model_name='''AI-Sweden/gpt-sw3-126m''' ,				sequences=A_ ,				)


"""simple docstring"""




import comet  # From: unbabel-comet
import torch

import datasets


A__   :					int							=   datasets.logging.get_logger(__name__)

A__   :					Optional[Any]							=   '\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

A__   :					Union[str, Any]							=   '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

A__   :					int							=   '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION )
class 			__magic_name__       (       datasets.Metric ):
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         return datasets.MetricInfo(
             description=_DESCRIPTION ,				citation=_CITATION ,				homepage='''https://unbabel.github.io/COMET/html/index.html''' ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
                 {
                     '''sources''': datasets.Value('''string''' ,				id='''sequence'''       ),
                     '''predictions''': datasets.Value('''string''' ,				id='''sequence'''       ),
                     '''references''': datasets.Value('''string''' ,				id='''sequence'''       ),
                 }       ) ,				codebase_urls=['''https://github.com/Unbabel/COMET'''] ,				reference_urls=[
                 '''https://github.com/Unbabel/COMET''',
                 '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
                 '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
             ] ,				)
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         if self.config_name == "default":
           _lowercase:			str       =     comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
         else:
           _lowercase:			Optional[Any]       =     comet.load_from_checkpoint(comet.download_model(self.config_name       )       )



       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_=None ,				A_=False       )     ->	List[str]:





         """simple docstring"""




         if gpus is None:
           _lowercase:			Union[str, Any]       =     1 if torch.cuda.is_available() else 0
         _lowercase:			Any       =     {'''src''': sources, '''mt''': predictions, '''ref''': references}
         _lowercase:			int       =     [dict(zip(A_ ,				A_       )       ) for t in zip(*data.values()       )]
         _lowercase ,				_lowercase:			Tuple       =     self.scorer.predict(A_ ,				gpus=A_ ,				progress_bar=A_       )
         return {"mean_score": mean_score, "scores": scores}
