





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

if is_tf_available():

       class  __A(						tf.keras.Model ):

            def __init__(      self,       A      ):





                   """simple docstring"""

                   super().__init__()
                   _UpperCamelCase              =						tokenizer
                   _UpperCamelCase              =						AutoConfig.from_pretrained(A      )
                   _UpperCamelCase              =						TFAutoModel.from_config(A      )



            def 	_UpperCamelCase     (      self,       A      ):





                   """simple docstring"""

                   _UpperCamelCase              =						self.tokenizer(A      )
                   _UpperCamelCase              =						self.bert(**A      )
                   return out["pooler_output"]

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            super().setUp()

            _UpperCamelCase              =						[
                BertTokenizer.from_pretrained(A      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
            ]  # repeat for when fast_bert_tokenizer=false
            _UpperCamelCase              =						[TFBertTokenizer.from_pretrained(A      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
                TFBertTokenizer.from_pretrained(A,       use_fast_bert_tokenizer=A      )
                for checkpoint in TOKENIZER_CHECKPOINTS
            ]
            assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

            _UpperCamelCase              =						[
                '''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''',
            ]
            _UpperCamelCase              =						list(zip(self.test_sentences,       self.test_sentences[::-1]      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tokenizer, tf_tokenizer in zip(self.tokenizers,       self.tf_tokenizers      ):
                   for test_inputs in (self.test_sentences, self.paired_sentences):
                          _UpperCamelCase              =						tokenizer(A,       return_tensors='''tf''',       padding='''longest'''      )
                          _UpperCamelCase              =						tf_tokenizer(A      )

                          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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tf_tokenizer in self.tf_tokenizers:
                   _UpperCamelCase              =						tf_tokenizer(self.paired_sentences      )
                   _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tf_tokenizer in self.tf_tokenizers:
                   _UpperCamelCase              =						tf.function(A      )
                   for test_inputs in (self.test_sentences, self.paired_sentences):
                          _UpperCamelCase              =						tf.constant(A      )
                          _UpperCamelCase              =						compiled_tokenizer(A      )
                          _UpperCamelCase              =						tf_tokenizer(A      )

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



     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tf_tokenizer in self.tf_tokenizers:
                   _UpperCamelCase              =						ModelToSave(tokenizer=A      )
                   _UpperCamelCase              =						tf.convert_to_tensor(self.test_sentences      )
                   _UpperCamelCase              =						model(A      )  # Build model with some sample inputs
                   with TemporaryDirectory() as tempdir:
                          _UpperCamelCase              =						Path(A      ) / '''saved.model'''
                          model.save(A      )
                          _UpperCamelCase              =						tf.keras.models.load_model(A      )
                   _UpperCamelCase              =						loaded_model(A      )
                   # 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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 0    ,       lowerCAmelCase = 0					):
       _UpperCamelCase              =						right or len(lowerCAmelCase					) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(lowerCAmelCase    ,       lowerCAmelCase    ,       left + 1    ,       right - 1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






