





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


__lowerCamelCase	:					List[str]			  =					["""bert-base-uncased""", """bert-base-cased"""]
__lowerCamelCase	:					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     : Optional[int]	     =       tokenizer
           UpperCamelCase     : Dict	     =       AutoConfig.from_pretrained(A_       )
           UpperCamelCase     : List[str]	     =       TFAutoModel.from_config(A_       )





          def 	__UpperCamelCase(			self	,							A_       ):
           '''simple docstring'''




           UpperCamelCase     : Tuple	     =       self.tokenizer(A_       )
           UpperCamelCase     : List[Any]	     =       self.bert(**A_       )
           return out["pooler_output"]



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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

       UpperCamelCase     : List[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     : int	     =       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     : Dict	     =       tokenizer(A_	,							return_tensors="tf"	,							padding="longest"       )
         UpperCamelCase     : List[Any]	     =       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     : Optional[Any]	     =       tf_tokenizer(self.paired_sentences       )
        UpperCamelCase     : 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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
        UpperCamelCase     : Any	     =       tf.function(A_       )
        for test_inputs in (self.test_sentences, self.paired_sentences):
         UpperCamelCase     : int	     =       tf.constant(A_       )
         UpperCamelCase     : 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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
        UpperCamelCase     : Union[str, Any]	     =       ModelToSave(tokenizer=A_       )
        UpperCamelCase     : Union[str, Any]	     =       tf.convert_to_tensor(self.test_sentences       )
        UpperCamelCase     : Optional[Any]	     =       model(A_       )  # Build model with some sample inputs
        with TemporaryDirectory() as tempdir:
         UpperCamelCase     : Optional[int]	     =       Path(A_       ) / "saved.model"
         model.save(A_       )
         UpperCamelCase     : List[Any]	     =       tf.keras.models.load_model(A_       )
        UpperCamelCase     : Union[str, Any]	     =       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 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : Union[str, Any]	     =       set()

 # Replace all the whitespace in our sentence
 UpperCamelCase     : Union[str, Any]	     =       input_str.replace(" "							,       ""					)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()					)
 return len(_lowerCAmelCase					) == 26



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : List[Any]	     =       [False] * 26
 for char in input_str:
  if char.islower():
   UpperCamelCase     : Tuple	     =       True
  elif char.isupper():
   UpperCamelCase     : str	     =       True
 return all(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 return len({char for char in input_str.lower() if char.isalpha()}					) == 26



def 		A_    (					)						->			None:
 from timeit import timeit

 UpperCamelCase     : Tuple	     =       "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("is_pangram()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_faster()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_fastest()"							,       setup=_lowerCAmelCase					)					)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
