'''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


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 		_lowerCamelCase		(   tf.keras.Model    ):
          def __init__(  self , lowerCAmelCase   )       ->					Tuple:
           super().__init__()
           SCREAMING_SNAKE_CASE__:   Any=      tokenizer
           SCREAMING_SNAKE_CASE__:   Tuple=      AutoConfig.from_pretrained(lowerCAmelCase   )
           SCREAMING_SNAKE_CASE__:   str=      TFAutoModel.from_config(lowerCAmelCase   )



          def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
           SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.tokenizer(lowerCAmelCase   )
           SCREAMING_SNAKE_CASE__:   Optional[int]=      self.bert(**lowerCAmelCase   )
           return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					int:
     super().setUp()

     SCREAMING_SNAKE_CASE__:   Any=      [
         BertTokenizer.from_pretrained(lowerCAmelCase   ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [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   )

     SCREAMING_SNAKE_CASE__:   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''',
     ]
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(zip(self.test_sentences , self.test_sentences[::-1]   )   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:

     for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers   ):
      for test_inputs in (self.test_sentences, self.paired_sentences):
       SCREAMING_SNAKE_CASE__:   List[Any]=      tokenizer(lowerCAmelCase , return_tensors='''tf''' , padding='''longest'''   )
       SCREAMING_SNAKE_CASE__:   Optional[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 						UpperCamelCase_				(  self   )       ->					int:

     for tf_tokenizer in self.tf_tokenizers:
      SCREAMING_SNAKE_CASE__:   Any=      tf_tokenizer(self.paired_sentences   )
      SCREAMING_SNAKE_CASE__:   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 						UpperCamelCase_				(  self   )       ->					int:

     for tf_tokenizer in self.tf_tokenizers:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      tf.function(lowerCAmelCase   )
      for test_inputs in (self.test_sentences, self.paired_sentences):
       SCREAMING_SNAKE_CASE__:   Optional[int]=      tf.constant(lowerCAmelCase   )
       SCREAMING_SNAKE_CASE__:   Optional[int]=      compiled_tokenizer(lowerCAmelCase   )
       SCREAMING_SNAKE_CASE__:   Dict=      tf_tokenizer(lowerCAmelCase   )

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



    @slow
    def 						UpperCamelCase_				(  self   )       ->					Dict:

     for tf_tokenizer in self.tf_tokenizers:
      SCREAMING_SNAKE_CASE__:   Any=      ModelToSave(tokenizer=lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   List[str]=      tf.convert_to_tensor(self.test_sentences   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      model(lowerCAmelCase   )  # Build model with some sample inputs
      with TemporaryDirectory() as tempdir:
       SCREAMING_SNAKE_CASE__:   str=      Path(lowerCAmelCase   ) / '''saved.model'''
       model.save(lowerCAmelCase   )
       SCREAMING_SNAKE_CASE__:   List[str]=      tf.keras.models.load_model(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Any=      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   )


from ..utils import DummyObject, requires_backends






class 		_lowerCamelCase		(   metaclass=UpperCamelCase_    ):
    __a  =      ["torch", "scipy"]
    def __init__(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Any:
     requires_backends(self , ['''torch''', '''scipy''']   )
    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					List[str]:
     requires_backends(cls , ['''torch''', '''scipy''']   )



    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					Tuple:
     requires_backends(cls , ['''torch''', '''scipy''']   )

