





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


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

if is_tf_available():



   class SCREAMING_SNAKE_CASE     (  tf.keras.Model			):
     """simple docstring"""






     def __init__(      self			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Union[str, Any]:






        """simple docstring"""







        super().__init__()
        __lowerCAmelCase  :      Tuple    =						tokenizer
        __lowerCAmelCase  :      str    =						AutoConfig.from_pretrained(_a   )
        __lowerCAmelCase  :      Dict    =						TFAutoModel.from_config(_a   )







     def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Dict:






        """simple docstring"""







        __lowerCAmelCase  :      Any    =						self.tokenizer(_a   )
        __lowerCAmelCase  :      List[Any]    =						self.bert(**_a   )
        return out["pooler_output"]



@require_tf
@require_tensorflow_text
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     super().setUp()

     __lowerCAmelCase  :      Union[str, Any]    =						[
         BertTokenizer.from_pretrained(_a   ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     __lowerCAmelCase  :      Optional[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   )

     __lowerCAmelCase  :      str    =						[
         "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",
     ]
     __lowerCAmelCase  :      List[str]    =						list(zip(self.test_sentences	,							self.test_sentences[::-1]   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     for tokenizer, tf_tokenizer in zip(self.tokenizers	,							self.tf_tokenizers   ):
        for test_inputs in (self.test_sentences, self.paired_sentences):
           __lowerCAmelCase  :      int    =						tokenizer(_a	,							return_tensors="""tf"""	,							padding="""longest"""   )
           __lowerCAmelCase  :      int    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     for tf_tokenizer in self.tf_tokenizers:
        __lowerCAmelCase  :      Any    =						tf_tokenizer(self.paired_sentences   )
        __lowerCAmelCase  :      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 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     for tf_tokenizer in self.tf_tokenizers:
        __lowerCAmelCase  :      Tuple    =						tf.function(_a   )
        for test_inputs in (self.test_sentences, self.paired_sentences):
           __lowerCAmelCase  :      Optional[Any]    =						tf.constant(_a   )
           __lowerCAmelCase  :      Optional[int]    =						compiled_tokenizer(_a   )
           __lowerCAmelCase  :      Any    =						tf_tokenizer(_a   )

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







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[Any]:






     """simple docstring"""







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





from pathlib import Path

import fire





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   dest_dir.mkdir(exist_ok=__A					)
   for path in src_dir.iterdir():
      __lowerCAmelCase  :      str    =						[x.rstrip() for x in list(path.open().readlines()					)][:n]
      __lowerCAmelCase  :      Dict    =						dest_dir.joinpath(path.name					)
      print(__A					)
      dest_path.open("""w"""					).write("""\n""".join(__A					)					)


if __name__ == "__main__":
   fire.Fire(minify)


