import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Any		=CpmAntTokenizer
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False



     def 			a__  (					self       ):
            super().setUp()

            _A=	[
                '<d>',
                '</d>',
                '<s>',
                '</s>',
                '</_>',
                '<unk>',
                '<pad>',
                '</n>',
                '我',
                '是',
                'C',
                'P',
                'M',
                'A',
                'n',
                't',
            ]
            _A=	os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES['vocab_file']       )
            with open(self.vocab_file				,      'w'				,      encoding='utf-8'       ) as vocab_writer:
                   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )





     @tooslow
     def 			a__  (					self       ):
            _A=	CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'       )
            _A=	'今天天气真好！'
            _A=	['今天', '天气', '真', '好', '！']
            _A=	tokenizer.tokenize(lowerCAmelCase__       )
            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )
            _A=	'今天天气真好！'
            _A=	[tokenizer.bos_token] + tokens

            _A=	[6, 9802, 14962, 2082, 831, 244]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )				,      lowerCAmelCase__       )

            _A=	tokenizer.decode(lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )