import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase        =       get_tests_dir('fixtures/spiece.model')






@require_sentencepiece
@require_tokenizers
class     __UpperCAmelCase(    A__      ,	unittest.TestCase				):






 """simple docstring"""




 __magic_name__          =   ErnieMTokenizer
 __magic_name__          =   False
 __magic_name__          =   True
 __magic_name__          =   False
 __magic_name__          =   False
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        super().setUp()

        # We have a SentencePiece fixture for testing
        A_							:       Optional[int]	      =				ErnieMTokenizer(__magic_name__ ,       unk_token='''<unk>''' ,       pad_token='''<pad>'''      )
        tokenizer.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       List[str]	      =				'''this is a test'''
        A_							:       int	      =				'''this is a test'''
        return input_text, output_text
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				'''<pad>'''
        A_							:       Union[str, Any]	      =				0

        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  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				list(self.get_tokenizer().get_vocab().keys()      )

        self.assertEqual(vocab_keys[0] ,       '''<pad>'''      )
        self.assertEqual(vocab_keys[1] ,       '''<unk>'''      )
        self.assertEqual(vocab_keys[-1] ,       '''▁eloquent'''      )
        self.assertEqual(len(__magic_name__      ) ,       3_0000      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        self.assertEqual(self.get_tokenizer().vocab_size ,       3_0000      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        if not self.test_rust_tokenizer:
               return

        A_							:       Optional[Any]	      =				self.get_tokenizer()
        A_							:       List[Any]	      =				self.get_rust_tokenizer()

        A_							:       Optional[Any]	      =				'''I was born in 92000, and this is falsé.'''

        A_							:       Optional[Any]	      =				tokenizer.tokenize(__magic_name__      )
        A_							:       Union[str, Any]	      =				rust_tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__ ,       __magic_name__      )

        A_							:       List[Any]	      =				tokenizer.encode(__magic_name__ ,       add_special_tokens=__magic_name__      )
        A_							:       Union[str, Any]	      =				rust_tokenizer.encode(__magic_name__ ,       add_special_tokens=__magic_name__      )
        self.assertListEqual(__magic_name__ ,       __magic_name__      )

        A_							:       str	      =				self.get_rust_tokenizer()
        A_							:       Any	      =				tokenizer.encode(__magic_name__      )
        A_							:       List[Any]	      =				rust_tokenizer.encode(__magic_name__      )
        self.assertListEqual(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				ErnieMTokenizer(__magic_name__ ,       do_lower_case=__magic_name__ ,       unk_token='''<unk>''' ,       pad_token='''<pad>'''      )

        A_							:       str	      =				tokenizer.tokenize('''This is a test'''      )
        self.assertListEqual(__magic_name__ ,       ['''▁this''', '''▁is''', '''▁a''', '''▁test''']      )

        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      ) ,       [48, 25, 21, 1289]      )

        A_							:       Any	      =				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
        # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
        self.assertListEqual(
            __magic_name__ ,       ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']      )
        A_							:       Optional[Any]	      =				tokenizer.convert_tokens_to_ids(__magic_name__      )
        self.assertListEqual(__magic_name__ ,       [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]      )

        A_							:       Union[str, Any]	      =				tokenizer.convert_ids_to_tokens(__magic_name__      )
        self.assertListEqual(
            __magic_name__ ,       ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.'''] ,       )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				ErnieMTokenizer(__magic_name__ ,       unk_token='''<unk>''' ,       pad_token='''<pad>'''      )

        A_							:       Optional[Any]	      =				tokenizer.encode('''sequence builders'''      )
        A_							:       List[Any]	      =				tokenizer.encode('''multi-sequence build'''      )

        A_							:       Tuple	      =				tokenizer.build_inputs_with_special_tokens(__magic_name__      )
        A_							:       Optional[Any]	      =				tokenizer.build_inputs_with_special_tokens(__magic_name__ ,       __magic_name__      )

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
            tokenizer.sep_token_id
        ] + text_a + [tokenizer.sep_token_id]
 @slow
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				{'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__magic_name__ ,       model_name='''susnato/ernie-m-base_pytorch''' ,       sequences=[
                '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
                '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
                '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
                '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
                '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
                '''conditioning on both left and right context in all layers.''',
                '''The quick brown fox jumps over the lazy dog.''',
            ] ,       )



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				{
            '''model_details''': {
                '''Organization''': '''testing''',
                '''Model date''': '''today''',
                '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
                '''Architecture''': '''Convolutional Neural Network.''',
            },
            '''metrics''': '''BLEU and ROUGE-1''',
            '''evaluation_data''': {
                '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
                '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
            },
            '''training_data''': {
                '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
                '''Preprocessing''': (
                    '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
                    ''' https://arxiv.org/pdf/1810.03993.pdf'''
                ),
            },
            '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
        }
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Tuple	      =				ModelCard.from_dict(self.inputs_dict      )
        self.assertTrue(hasattr(__magic_name__ ,       '''model_details'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''intended_use'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''factors'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''metrics'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''evaluation_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''training_data'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''quantitative_analyses'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''ethical_considerations'''      )      )
        self.assertTrue(hasattr(__magic_name__ ,       '''caveats_and_recommendations'''      )      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				ModelCard.from_dict(self.inputs_dict      )
        A_							:       List[Any]	      =				json.loads(modelcard.to_json_string()      )
        for key, value in self.inputs_dict.items():
               self.assertEqual(obj[key] ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               A_							:       str	      =				os.path.join(__magic_name__ ,       '''modelcard.json'''      )
               model_card_first.to_json_file(__magic_name__      )
               A_							:       str	      =				ModelCard.from_json_file(__magic_name__      )

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				ModelCard.from_dict(self.inputs_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
               model_card_first.save_pretrained(__magic_name__      )
               A_							:       Optional[int]	      =				ModelCard.from_pretrained(__magic_name__      )

        self.assertEqual(model_card_second.to_dict() ,       model_card_first.to_dict()      )


