





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


a__: Any							   =						get_tests_dir('fixtures/test_sentencepiece.model')

a__: Any							   =						{'target_lang': 'fi', 'source_lang': 'en'}
a__: Any							   =						'>>zh<<'
a__: int							   =						'Helsinki-NLP/'

if is_torch_available():
       a__: Tuple							   =						'pt'
elif is_tf_available():
       a__: str							   =						'tf'
else:
       a__: Tuple							   =						'jax'




@require_sentencepiece
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__							,   unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MarianTokenizer
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      True



 def 							UpperCamelCase     (				self					):
        super().setUp()
        A__		   =		['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
        A__		   =		dict(zip(__lowerCamelCase,range(len(__lowerCamelCase					)					)					)					)
        A__		   =		Path(self.tmpdirname					)
        save_json(__lowerCamelCase,save_dir / VOCAB_FILES_NAMES['''vocab''']					)
        save_json(__lowerCamelCase,save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']					)
        if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
               copyfile(__lowerCamelCase,save_dir / VOCAB_FILES_NAMES['''source_spm''']					)
               copyfile(__lowerCamelCase,save_dir / VOCAB_FILES_NAMES['''target_spm''']					)

        A__		   =		MarianTokenizer.from_pretrained(self.tmpdirname					)
        tokenizer.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return MarianTokenizer.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        return (
            "This is a test",
            "This is a test",
        )



 def 							UpperCamelCase     (				self					):
        A__		   =		'''</s>'''
        A__		   =		0

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



 def 							UpperCamelCase     (				self					):
        A__		   =		list(self.get_tokenizer().get_vocab().keys()					)

        self.assertEqual(vocab_keys[0],'''</s>'''					)
        self.assertEqual(vocab_keys[1],'''<unk>'''					)
        self.assertEqual(vocab_keys[-1],'''<pad>'''					)
        self.assertEqual(len(__lowerCamelCase					),9					)



 def 							UpperCamelCase     (				self					):
        self.assertEqual(self.get_tokenizer().vocab_size,9					)



 def 							UpperCamelCase     (				self					):
        A__		   =		MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de"					)
        A__		   =		en_de_tokenizer(['''I am a small frog'''],return_tensors=__lowerCamelCase					)
        self.assertIsInstance(__lowerCamelCase,__lowerCamelCase					)
        A__		   =		[38, 121, 14, 697, 3_8848, 0]
        self.assertListEqual(__lowerCamelCase,batch.input_ids[0]					)

        A__		   =		tempfile.mkdtemp()
        en_de_tokenizer.save_pretrained(__lowerCamelCase					)
        A__		   =		[x.name for x in Path(__lowerCamelCase					).glob('''*'''					)]
        self.assertIn('''source.spm''',__lowerCamelCase					)
        MarianTokenizer.from_pretrained(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_tokenizer()

        A__		   =		tok(
            ['''I am a small frog''' * 1000, '''I am a small frog'''],padding=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors=__lowerCamelCase					)
        self.assertIsInstance(__lowerCamelCase,__lowerCamelCase					)
        self.assertEqual(batch.input_ids.shape,(2, 512)					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_tokenizer()
        A__		   =		tok(['''I am a tiny frog''', '''I am a small frog'''],padding=__lowerCamelCase,return_tensors=__lowerCamelCase					)
        self.assertIsInstance(__lowerCamelCase,__lowerCamelCase					)
        self.assertEqual(batch_smaller.input_ids.shape,(2, 10)					)



 @slow
 def 							UpperCamelCase     (				self					):
        # fmt: off
        A__		   =		{'''input_ids''': [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], '''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, 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, 1, 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], [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, 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]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__lowerCamelCase,model_name='''Helsinki-NLP/opus-mt-en-de''',revision='''1a8c2263da11e68e50938f97e10cd57820bd504c''',decode_kwargs={'''use_source_tokenizer''': True},)




 def 							UpperCamelCase     (				self					):
        A__		   =		MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''					)

        A__		   =		'''Tämä on testi'''
        A__		   =		'''This is a test'''

        A__		   =		[76, 7, 2047, 2]
        A__		   =		[69, 12, 11, 940, 2]

        A__		   =		tokenizer(__lowerCamelCase					).input_ids
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase					)

        A__		   =		tokenizer(text_target=__lowerCamelCase					).input_ids
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase					)

        A__		   =		tokenizer.decode(__lowerCamelCase,skip_special_tokens=__lowerCamelCase					)
        self.assertEqual(__lowerCamelCase,__lowerCamelCase					)












import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[Any]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





