

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		)







from __future__ import annotations

import numpy as np






def 					__lowercase      (     _A			)     ->						tuple[np.ndarray, np.ndarray]:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   np.shape(_A			)
      if rows != columns:
            SCREAMING_SNAKE_CASE :      int       =   (
                """'table' has to be of square shaped array but got a """
                F"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(_A			)

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.zeros((rows, columns)			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.zeros((rows, columns)			)
      for i in range(_A			):
            for j in range(_A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
            for j in range(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      List[str]       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   table[i][j] - total
      return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()




