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      lowerCAmelCase						(					tf.keras.Model			):



           def __init__(					self				,      lowerCAmelCase__       ):
                  super().__init__()
                  _A=	tokenizer
                  _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                  _A=	TFAutoModel.from_config(lowerCAmelCase__       )





           def 			a__  (					self				,      lowerCAmelCase__       ):
                  _A=	self.tokenizer(lowerCAmelCase__       )
                  _A=	self.bert(**lowerCAmelCase__       )
                  return out["pooler_output"]





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



     def 			a__  (					self       ):
            super().setUp()

            _A=	[
                BertTokenizer.from_pretrained(lowerCAmelCase__       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
            ]  # repeat for when fast_bert_tokenizer=false
            _A=	[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       )

            _A=	[
                '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',
            ]
            _A=	list(zip(self.test_sentences				,      self.test_sentences[::-1]       )       )



     def 			a__  (					self       ):

            for tokenizer, tf_tokenizer in zip(self.tokenizers				,      self.tf_tokenizers       ):
                   for test_inputs in (self.test_sentences, self.paired_sentences):
                          _A=	tokenizer(lowerCAmelCase__				,      return_tensors='tf'				,      padding='longest'       )
                          _A=	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 			a__  (					self       ):

            for tf_tokenizer in self.tf_tokenizers:
                   _A=	tf_tokenizer(self.paired_sentences       )
                   _A=	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 			a__  (					self       ):

            for tf_tokenizer in self.tf_tokenizers:
                   _A=	tf.function(lowerCAmelCase__       )
                   for test_inputs in (self.test_sentences, self.paired_sentences):
                          _A=	tf.constant(lowerCAmelCase__       )
                          _A=	compiled_tokenizer(lowerCAmelCase__       )
                          _A=	tf_tokenizer(lowerCAmelCase__       )

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





     @slow
     def 			a__  (					self       ):

            for tf_tokenizer in self.tf_tokenizers:
                   _A=	ModelToSave(tokenizer=lowerCAmelCase__       )
                   _A=	tf.convert_to_tensor(self.test_sentences       )
                   _A=	model(lowerCAmelCase__       )  # Build model with some sample inputs
                   with TemporaryDirectory() as tempdir:
                          _A=	Path(lowerCAmelCase__       ) / 'saved.model'
                          model.save(lowerCAmelCase__       )
                          _A=	tf.keras.models.load_model(lowerCAmelCase__       )
                   _A=	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       )





def 							UpperCamelCase				(    )		->						list[list[int]]:




       '''simple docstring'''






       return [list(range(10_00 - i       ,					-10_00 - i       ,					-1   )   ) for i in range(10_00   )]


UpperCAmelCase_								=							generate_large_matrix()
UpperCAmelCase_								=							(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						None:




       '''simple docstring'''






       assert all(row == sorted(lowerCAmelCase_       ,					reverse=lowerCAmelCase_   ) for row in grid   )
       assert all(list(lowerCAmelCase_   ) == sorted(lowerCAmelCase_       ,					reverse=lowerCAmelCase_   ) for col in zip(*lowerCAmelCase_   )   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       _A=	len(lowerCAmelCase_   ) - 1

       # Edge cases such as no values or all numbers are negative.
       if not array or array[0] < 0:
              return 0

       while right + 1 > left:
              _A=	(left + right) // 2
              _A=	array[mid]

              # Num must be negative and the index must be greater than or equal to 0.
              if num < 0 and array[mid - 1] >= 0:
                     return mid

              if num >= 0:
                     _A=	mid + 1
              else:
                     _A=	mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
       return len(lowerCAmelCase_   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       _A=	len(grid[0]   )

       for i in range(len(lowerCAmelCase_   )   ):
              _A=	find_negative_index(grid[i][:bound]   )
              total += bound
       return (len(lowerCAmelCase_   ) * len(grid[0]   )) - total

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       return len([number for row in grid for number in row if number < 0]   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       for row in grid:
              for i, number in enumerate(lowerCAmelCase_   ):
                     if number < 0:
                            total += len(lowerCAmelCase_   ) - i
                            break
       return total

def 							UpperCamelCase				(    )		->						None:




       '''simple docstring'''






       from timeit import timeit

       print('Running benchmarks'   )
       _A=	(
           'from __main__ import count_negatives_binary_search, '
           'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
       )
       for func in (
           "count_negatives_binary_search",  # took 0.7727 seconds
           "count_negatives_brute_force_with_break",  # took 4.6505 seconds
           "count_negatives_brute_force",  # took 12.8160 seconds
       ):
              _A=	timeit(F"{func}(grid=grid)"       ,					setup=lowerCAmelCase_       ,					number=5_00   )
              print(F"{func}() took {time:0.4f} seconds"   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()