





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
    import tensorflow as tf

if is_tensorflow_text_available():
    from transformers.models.bert import TFBertTokenizer


lowercase      :       Dict									=  ["""bert-base-uncased""", """bert-base-cased"""]
lowercase      :       Optional[Any]									=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():





    class  A__    (							tf.keras.Model	):




       """simple docstring"""



       def __init__(					self					,  lowercase)			->						Dict:







              '''simple docstring'''

              super().__init__()
              a__						:	Tuple =				tokenizer
              a__						:	int =				AutoConfig.from_pretrained(lowercase)
              a__						:	Dict =				TFAutoModel.from_config(lowercase)

       def 							__lowercase		(					self					,  lowercase)			->						Tuple:







              '''simple docstring'''

              a__						:	List[Any] =				self.tokenizer(lowercase)
              a__						:	Dict =				self.bert(**lowercase)
              return out["pooler_output"]





@require_tf
@require_tensorflow_text
class  A__    (							unittest.TestCase	):




   """simple docstring"""



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







          '''simple docstring'''

          super().setUp()

          a__						:	int =				[
              BertTokenizer.from_pretrained(lowercase) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
          ]  # repeat for when fast_bert_tokenizer=false
          a__						:	Union[str, Any] =				[TFBertTokenizer.from_pretrained(lowercase) for checkpoint in TOKENIZER_CHECKPOINTS] + [
              TFBertTokenizer.from_pretrained(lowercase					,  use_fast_bert_tokenizer=lowercase)
              for checkpoint in TOKENIZER_CHECKPOINTS
          ]
          assert len(self.tokenizers) == len(self.tf_tokenizers)

          a__						:	Tuple =				[
              'This is a straightforward English test sentence.',
              'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
              'Now we\'re going to add some Chinese: 一 二 三 一二三',
              'And some much more rare Chinese: 齉 堃 齉堃',
              'Je vais aussi écrire en français pour tester les accents',
              'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
          ]
          a__						:	Optional[Any] =				list(zip(self.test_sentences					,  self.test_sentences[::-1]))

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          for tokenizer, tf_tokenizer in zip(self.tokenizers					,  self.tf_tokenizers):
                 for test_inputs in (self.test_sentences, self.paired_sentences):
                        a__						:	Tuple =				tokenizer(lowercase					,  return_tensors='tf'					,  padding='longest')
                        a__						:	Any =				tf_tokenizer(lowercase)

                        for key in python_outputs.keys():
                               self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                               self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,  tf.intaa) == tf_outputs[key]))

   @slow
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          for tf_tokenizer in self.tf_tokenizers:
                 a__						:	Dict =				tf_tokenizer(self.paired_sentences)
                 a__						:	Tuple =				tf_tokenizer(
                     text=[sentence[0] for sentence in self.paired_sentences]					,  text_pair=[sentence[1] for sentence in self.paired_sentences]					,  )
                 for key in merged_outputs.keys():
                        self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,  tf.intaa) == separated_outputs[key]))

   @slow
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          for tf_tokenizer in self.tf_tokenizers:
                 a__						:	Tuple =				tf.function(lowercase)
                 for test_inputs in (self.test_sentences, self.paired_sentences):
                        a__						:	Optional[int] =				tf.constant(lowercase)
                        a__						:	Optional[Any] =				compiled_tokenizer(lowercase)
                        a__						:	Union[str, Any] =				tf_tokenizer(lowercase)

                        for key in eager_outputs.keys():
                               self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))

   @slow
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          for tf_tokenizer in self.tf_tokenizers:
                 a__						:	str =				ModelToSave(tokenizer=lowercase)
                 a__						:	str =				tf.convert_to_tensor(self.test_sentences)
                 a__						:	List[Any] =				model(lowercase)  # Build model with some sample inputs
                 with TemporaryDirectory() as tempdir:
                        a__						:	Optional[int] =				Path(lowercase) / 'saved.model'
                        model.save(lowercase)
                        a__						:	Any =				tf.keras.models.load_model(lowercase)
                 a__						:	List[Any] =				loaded_model(lowercase)
                 # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
                 self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output))					,  1e-5)









def A_	(		A__		,      A__				)  ->		str:
       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'				)

       a__						:	List[str] =				str(bin(A__				)				)[2:]  # remove the leading "0b"
       a__						:	Optional[int] =				str(bin(A__				)				)[2:]  # remove the leading "0b"

       a__						:	List[str] =				max(len(A__				)		,      len(A__				)				)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'				)				)
           for char_a, char_b in zip(a_binary.zfill(A__				)		,      b_binary.zfill(A__				)				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


