'''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


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

if is_tf_available():

  class 	lowerCamelCase_	(tf.keras.Model    ):
        '''simple docstring'''

        def __init__(       self						:				Union[str, Any]						,					A						:				Tuple       ):
               super().__init__()
               _UpperCAmelCase     :  Any							      =    tokenizer
               _UpperCAmelCase     :  int							      =    AutoConfig.from_pretrained(A       )
               _UpperCAmelCase     :  str							      =    TFAutoModel.from_config(A       )





        def      _A	(       self						:				Union[str, Any]						,					A						:				Dict       ):
               _UpperCAmelCase     :  Any							      =    self.tokenizer(A       )
               _UpperCAmelCase     :  Union[str, Any]							      =    self.bert(**A       )
               return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				List[str]       ):
             super().setUp()

             _UpperCAmelCase     :  Union[str, Any]							      =    [
                 BertTokenizer.from_pretrained(A       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
             ]  # repeat for when fast_bert_tokenizer=false
             _UpperCAmelCase     :  List[Any]							      =    [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     :  Dict							      =    [
                 "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     :  str							      =    list(zip(self.test_sentences						,					self.test_sentences[::-1]       )       )




      def      _A	(       self						:				Optional[Any]       ):
             for tokenizer, tf_tokenizer in zip(self.tokenizers						,					self.tf_tokenizers       ):
                    for test_inputs in (self.test_sentences, self.paired_sentences):
                           _UpperCAmelCase     :  Any							      =    tokenizer(A						,					return_tensors="tf"						,					padding="longest"       )
                           _UpperCAmelCase     :  str							      =    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      _A	(       self						:				int       ):
             for tf_tokenizer in self.tf_tokenizers:
                    _UpperCAmelCase     :  str							      =    tf_tokenizer(self.paired_sentences       )
                    _UpperCAmelCase     :  int							      =    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      _A	(       self						:				Tuple       ):
             for tf_tokenizer in self.tf_tokenizers:
                    _UpperCAmelCase     :  List[Any]							      =    tf.function(A       )
                    for test_inputs in (self.test_sentences, self.paired_sentences):
                           _UpperCAmelCase     :  int							      =    tf.constant(A       )
                           _UpperCAmelCase     :  Optional[Any]							      =    compiled_tokenizer(A       )
                           _UpperCAmelCase     :  int							      =    tf_tokenizer(A       )

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





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




'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






