'''simple docstring'''
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


a=  ['''bert-base-uncased''', '''bert-base-cased''']
a=  '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():






  class 			__lowercase		(						tf.keras.Model						):







       """simple docstring"""






       def __init__(		self     ,			_lowerCamelCase    ):
           super().__init__()
           __UpperCamelCase	:	str		    =  tokenizer
           __UpperCamelCase	:	Optional[Any]		    =  AutoConfig.from_pretrained(_lowerCamelCase    )
           __UpperCamelCase	:	Tuple		    =  TFAutoModel.from_config(_lowerCamelCase    )







       def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
           __UpperCamelCase	:	List[str]		    =  self.tokenizer(_lowerCamelCase    )
           __UpperCamelCase	:	Tuple		    =  self.bert(**_lowerCamelCase    )
           return out["pooler_output"]






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







     """simple docstring"""






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

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

         __UpperCamelCase	:	Any		    =  [
             '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[str]		    =  list(zip(self.test_sentences     ,			self.test_sentences[::-1]    )    )







     def lowerCAmelCase     (		self    ):
         for tokenizer, tf_tokenizer in zip(self.tokenizers     ,			self.tf_tokenizers    ):
             for test_inputs in (self.test_sentences, self.paired_sentences):
                 __UpperCamelCase	:	int		    =  tokenizer(_lowerCamelCase     ,			return_tensors='tf'     ,			padding='longest'    )
                 __UpperCamelCase	:	int		    =  tf_tokenizer(_lowerCamelCase    )

                 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 lowerCAmelCase     (		self    ):
         for tf_tokenizer in self.tf_tokenizers:
             __UpperCamelCase	:	Optional[int]		    =  tf_tokenizer(self.paired_sentences    )
             __UpperCamelCase	:	List[Any]		    =  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 lowerCAmelCase     (		self    ):
         for tf_tokenizer in self.tf_tokenizers:
             __UpperCamelCase	:	Tuple		    =  tf.function(_lowerCamelCase    )
             for test_inputs in (self.test_sentences, self.paired_sentences):
                 __UpperCamelCase	:	Dict		    =  tf.constant(_lowerCamelCase    )
                 __UpperCamelCase	:	List[Any]		    =  compiled_tokenizer(_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  tf_tokenizer(_lowerCamelCase    )

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







     @slow
     def lowerCAmelCase     (		self    ):
         for tf_tokenizer in self.tf_tokenizers:
             __UpperCamelCase	:	Tuple		    =  ModelToSave(tokenizer=_lowerCamelCase    )
             __UpperCamelCase	:	Union[str, Any]		    =  tf.convert_to_tensor(self.test_sentences    )
             __UpperCamelCase	:	Optional[int]		    =  model(_lowerCamelCase    )  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                 __UpperCamelCase	:	List[Any]		    =  Path(_lowerCamelCase    ) / 'saved.model'
                 model.save(_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  tf.keras.models.load_model(_lowerCamelCase    )
             __UpperCamelCase	:	str		    =  loaded_model(_lowerCamelCase    )
             # 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    )




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



