

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

if is_tf_available():







		class        a__       (   tf.keras.Model       ):







				"""simple docstring"""




				def __init__(			self    : List[str]				,				UpperCAmelCase__    : Tuple		)					->Union[str, Any]:
										"""simple docstring"""







										super().__init__()
										SCREAMING_SNAKE_CASE :      int       =   tokenizer
										SCREAMING_SNAKE_CASE :      Optional[int]       =   AutoConfig.from_pretrained(UpperCAmelCase__		)
										SCREAMING_SNAKE_CASE :      List[str]       =   TFAutoModel.from_config(UpperCAmelCase__		)




				def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : int		)					->List[str]:
										"""simple docstring"""







										SCREAMING_SNAKE_CASE :      Tuple       =   self.tokenizer(UpperCAmelCase__		)
										SCREAMING_SNAKE_CASE :      Dict       =   self.bert(**UpperCAmelCase__		)
										return out["pooler_output"]







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







		"""simple docstring"""




		def  _lowercase      (			self    : Union[str, Any]		)					->Dict:
								"""simple docstring"""







								super().setUp()

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

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


		def  _lowercase      (			self    : List[str]		)					->str:
								"""simple docstring"""







								for tokenizer, tf_tokenizer in zip(self.tokenizers				,				self.tf_tokenizers		):
														for test_inputs in (self.test_sentences, self.paired_sentences):
																				SCREAMING_SNAKE_CASE :      Optional[Any]       =   tokenizer(UpperCAmelCase__				,				return_tensors="""tf"""				,				padding="""longest"""		)
																				SCREAMING_SNAKE_CASE :      Any       =   tf_tokenizer(UpperCAmelCase__		)

																				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  _lowercase      (			self    : List[str]		)					->int:
								"""simple docstring"""







								for tf_tokenizer in self.tf_tokenizers:
														SCREAMING_SNAKE_CASE :      int       =   tf_tokenizer(self.paired_sentences		)
														SCREAMING_SNAKE_CASE :      str       =   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  _lowercase      (			self    : Optional[Any]		)					->int:
								"""simple docstring"""







								for tf_tokenizer in self.tf_tokenizers:
														SCREAMING_SNAKE_CASE :      Optional[int]       =   tf.function(UpperCAmelCase__		)
														for test_inputs in (self.test_sentences, self.paired_sentences):
																				SCREAMING_SNAKE_CASE :      Optional[int]       =   tf.constant(UpperCAmelCase__		)
																				SCREAMING_SNAKE_CASE :      Union[str, Any]       =   compiled_tokenizer(UpperCAmelCase__		)
																				SCREAMING_SNAKE_CASE :      str       =   tf_tokenizer(UpperCAmelCase__		)

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




		@slow
		def  _lowercase      (			self    : Optional[Any]		)					->Union[str, Any]:
								"""simple docstring"""







								for tf_tokenizer in self.tf_tokenizers:
														SCREAMING_SNAKE_CASE :      List[str]       =   ModelToSave(tokenizer=UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE :      Optional[int]       =   tf.convert_to_tensor(self.test_sentences		)
														SCREAMING_SNAKE_CASE :      int       =   model(UpperCAmelCase__		)  # Build model with some sample inputs
														with TemporaryDirectory() as tempdir:
																				SCREAMING_SNAKE_CASE :      Any       =   Path(UpperCAmelCase__		) / """saved.model"""
																				model.save(UpperCAmelCase__		)
																				SCREAMING_SNAKE_CASE :      Optional[Any]       =   tf.keras.models.load_model(UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE :      List[str]       =   loaded_model(UpperCAmelCase__		)
														# 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 					__lowercase      (     _A						,    _A			)     ->						int:
      assert x is not None
      assert y is not None

      SCREAMING_SNAKE_CASE :      int       =   len(_A			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   len(_A			)

      # declaring the array for storing the dp values
      SCREAMING_SNAKE_CASE :      Tuple       =   [[0] * (n + 1) for _ in range(m + 1			)]  # noqa: E741

      for i in range(1						,    m + 1			):
            for j in range(1						,    n + 1			):
                  SCREAMING_SNAKE_CASE :      List[Any]       =   1 if x[i - 1] == y[j - 1] else 0

                  SCREAMING_SNAKE_CASE :      int       =   max(l[i - 1][j]						,    l[i][j - 1]						,    l[i - 1][j - 1] + match			)

      SCREAMING_SNAKE_CASE :      int       =   """"""
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      int       =   m, n
      while i > 0 and j > 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   1 if x[i - 1] == y[j - 1] else 0

            if l[i][j] == l[i - 1][j - 1] + match:
                  if match == 1:
                        SCREAMING_SNAKE_CASE :      List[Any]       =   x[i - 1] + seq
                  i -= 1
                  j -= 1
            elif l[i][j] == l[i - 1][j]:
                  i -= 1
            else:
                  j -= 1

      return l[m][n], seq


if __name__ == "__main__":
  UpperCAmelCase__ :     str				    =    """AGGTAB"""
  UpperCAmelCase__ :     Optional[int]				    =    """GXTXAYB"""
  UpperCAmelCase__ :     Tuple				    =    4
  UpperCAmelCase__ :     Dict				    =    """GTAB"""

  UpperCAmelCase__    ,   UpperCAmelCase__ :     Optional[int]				    =    longest_common_subsequence(a, b)
  print("""len =""", ln, """, sub-sequence =""", subseq)
  import doctest

  doctest.testmod()




