

"""simple docstring"""






def 	lowercase      (   ) ->      list[list[int]]:
 return [list(range(1000 - i      ,   -1000 - i      ,   -1						)						) for i in range(1000						)]


__lowerCamelCase										=		generate_large_matrix()
__lowerCamelCase										=		(
    [[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 	lowercase      (   __UpperCamelCase						) ->      None:
 assert all(row == sorted(__UpperCamelCase      ,   reverse=__UpperCamelCase						) for row in grid						)
 assert all(list(__UpperCamelCase						) == sorted(__UpperCamelCase      ,   reverse=__UpperCamelCase						) for col in zip(*__UpperCamelCase						)						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 __magic_name__     =      0
 __magic_name__     =      len(__UpperCamelCase						) - 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:
  __magic_name__     =      (left + right) // 2
  __magic_name__     =      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:
   __magic_name__     =      mid + 1
  else:
   __magic_name__     =      mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
 return len(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 __magic_name__     =      0
 __magic_name__     =      len(grid[0]						)

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





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return len([number for row in grid for number in row if number < 0]						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 __magic_name__     =      0
 for row in grid:
  for i, number in enumerate(__UpperCamelCase						):
   if number < 0:
    total += len(__UpperCamelCase						) - i
    break
 return total





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

 print('''Running benchmarks'''						)
 __magic_name__     =      (
     '''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
 ):
  __magic_name__     =      timeit(f'''{func}(grid=grid)'''      ,   setup=__UpperCamelCase      ,   number=500						)
  print(f'''{func}() took {time:0.4f} seconds'''						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tempfile.mkdtemp()

      # fmt: off
      __magic_name__     =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
      # fmt: on
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

      __magic_name__     =      {
          '''do_resize''': True,
          '''size''': {'''height''': 18, '''width''': 18},
          '''do_normalize''': True,
          '''image_mean''': [0.5, 0.5, 0.5],
          '''image_std''': [0.5, 0.5, 0.5],
      }
      __magic_name__     =      os.path.join(self.tmpdirname ,						UpperCamelCase_       )
      with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''       ) as fp:
       json.dump(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return BertTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      shutil.rmtree(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [np.random.randint(255 ,						size=(3, 30, 400) ,						dtype=np.uinta       )]

      __magic_name__     =      [Image.fromarray(np.moveaxis(UpperCamelCase_ ,						0 ,						-1       )       ) for x in image_inputs]

      return image_inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_image_processor()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      processor.save_pretrained(self.tmpdirname       )
      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()       )
      processor.save_pretrained(self.tmpdirname       )

      __magic_name__     =      self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''       )
      __magic_name__     =      self.get_image_processor(do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(
          self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor_add_kwargs.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      image_processor(UpperCamelCase_ ,						return_tensors='''np'''       )
      __magic_name__     =      processor(images=UpperCamelCase_ ,						return_tensors='''np'''       )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum() ,						input_processor[key].sum() ,						delta=1E-2       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''

      __magic_name__     =      processor(text=UpperCamelCase_       )

      __magic_name__     =      tokenizer(UpperCamelCase_       )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

      # test if it raises when no input is passed
      with self.assertRaises(UpperCamelCase_       ):
       processor()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __magic_name__     =      processor.batch_decode(UpperCamelCase_       )
      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_       )

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						processor.model_input_names       )

