


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 	__lowercase					(	unittest.TestCase	):





      def 							_lowercase					(		self    :       List[Any]							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  tempfile.mkdtemp()

         UpperCAmelCase      					=  BlipImageProcessor()
         UpperCAmelCase      					=  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)

         UpperCAmelCase      					=  BlipaProcessor(__lowerCamelCase       ,							__lowerCamelCase							)

         processor.save_pretrained(self.tmpdirname							)





      def 							_lowercase					(		self    :       int       ,							**__lowerCamelCase    :       Optional[int]							)     -> str:






         """simple docstring"""







         return AutoProcessor.from_pretrained(self.tmpdirname       ,							**__lowerCamelCase							).tokenizer





      def 							_lowercase					(		self    :       int       ,							**__lowerCamelCase    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return AutoProcessor.from_pretrained(self.tmpdirname       ,							**__lowerCamelCase							).image_processor





      def 							_lowercase					(		self    :       List[str]							)     -> Tuple:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  [np.random.randint(2_5_5       ,							size=(3, 3_0, 4_0_0)       ,							dtype=np.uinta							)]

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

         return image_inputs





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  BlipaProcessor(tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         UpperCAmelCase      					=  self.get_tokenizer(bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""							)
         UpperCAmelCase      					=  self.get_image_processor(do_normalize=__lowerCamelCase       ,							padding_value=1.0							)

         UpperCAmelCase      					=  BlipaProcessor.from_pretrained(
             self.tmpdirname       ,							bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""       ,							do_normalize=__lowerCamelCase       ,							padding_value=1.0							)

         self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer_add_kwargs.get_vocab()							)
         self.assertIsInstance(processor.tokenizer       ,							__lowerCamelCase							)

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





      def 							_lowercase					(		self    :       int							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  BlipaProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  self.prepare_image_inputs()

         UpperCAmelCase      					=  image_processor(__lowerCamelCase       ,							return_tensors="""np"""							)
         UpperCAmelCase      					=  processor(images=__lowerCamelCase       ,							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 							_lowercase					(		self    :       Dict							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  BlipaProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  """lower newer"""

         UpperCAmelCase      					=  processor(text=__lowerCamelCase							)

         UpperCAmelCase      					=  tokenizer(__lowerCamelCase       ,							return_token_type_ids=__lowerCamelCase							)

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





      def 							_lowercase					(		self    :       int							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  BlipaProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  """lower newer"""
         UpperCAmelCase      					=  self.prepare_image_inputs()

         UpperCAmelCase      					=  processor(text=__lowerCamelCase       ,							images=__lowerCamelCase							)

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

         # test if it raises when no input is passed
         with pytest.raises(__lowerCamelCase							):
            processor()





      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  BlipaProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

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

         UpperCAmelCase      					=  processor.batch_decode(__lowerCamelCase							)
         UpperCAmelCase      					=  tokenizer.batch_decode(__lowerCamelCase							)

         self.assertListEqual(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_image_processor()
         UpperCAmelCase      					=  self.get_tokenizer()

         UpperCAmelCase      					=  BlipaProcessor(tokenizer=__lowerCamelCase       ,							image_processor=__lowerCamelCase							)

         UpperCAmelCase      					=  """lower newer"""
         UpperCAmelCase      					=  self.prepare_image_inputs()

         UpperCAmelCase      					=  processor(text=__lowerCamelCase       ,							images=__lowerCamelCase							)

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()							)       ,							["""pixel_values""", """input_ids""", """attention_mask"""]							)







def 	_UpperCamelCase					(    )    ->list[list[int]]:
   return [list(range(1_0_0_0 - i	,    -1_0_0_0 - i	,    -1 ) ) for i in range(1_0_0_0 )]


__a	 =      generate_large_matrix()
__a	 =      (
    [[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:
   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:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  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:
      UpperCAmelCase      					=  (left + right) // 2
      UpperCAmelCase      					=  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:
         UpperCAmelCase      					=  mid + 1
      else:
         UpperCAmelCase      					=  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:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(grid[0] )

   for i in range(len(lowerCAmelCase_ ) ):
      UpperCAmelCase      					=  find_negative_index(grid[i][:bound] )
      total += bound
   return (len(lowerCAmelCase_ ) * len(grid[0] )) - total
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   return len([number for row in grid for number in row if number < 0] )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   for row in grid:
      for i, number in enumerate(lowerCAmelCase_ ):
         if number < 0:
            total += len(lowerCAmelCase_ ) - i
            break
   return total
def 	_UpperCamelCase					(    )    ->None:
   from timeit import timeit

   print("""Running benchmarks""" )
   UpperCAmelCase      					=  (
       """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
   ):
      UpperCAmelCase      					=  timeit(F"""{func}(grid=grid)"""	,    setup=lowerCAmelCase_	,    number=5_0_0 )
      print(F"""{func}() took {time:0.4f} seconds""" )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()



