






"""simple docstring"""



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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast



@require_vision
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  tempfile.mkdtemp()

         A__										=  BlipImageProcessor()
         A__										=  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""       )

         A__										=  BlipProcessor(UpperCamelCase    ,						UpperCamelCase       )

         processor.save_pretrained(self.tmpdirname       )

    def 				UpperCamelCase	(     self: Optional[Any]    ,						**UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Tuple    ,						**UpperCamelCase: Optional[int]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  [np.random.randint(2_55    ,						size=(3, 30, 4_00)    ,						dtype=np.uinta       )]

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

         return image_inputs

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  BlipProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

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

         A__										=  BlipProcessor.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    ,						UpperCamelCase       )

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

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.get_image_processor()
         A__										=  self.get_tokenizer()

         A__										=  BlipProcessor(tokenizer=UpperCamelCase    ,						image_processor=UpperCamelCase       )

         A__										=  self.prepare_image_inputs()

         A__										=  image_processor(UpperCamelCase    ,						return_tensors="""np"""       )
         A__										=  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 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__										=  self.get_image_processor()
         A__										=  self.get_tokenizer()

         A__										=  BlipProcessor(tokenizer=UpperCamelCase    ,						image_processor=UpperCamelCase       )

         A__										=  """lower newer"""

         A__										=  processor(text=UpperCamelCase       )

         A__										=  tokenizer(UpperCamelCase    ,						return_token_type_ids=UpperCamelCase       )

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

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  self.get_image_processor()
         A__										=  self.get_tokenizer()

         A__										=  BlipProcessor(tokenizer=UpperCamelCase    ,						image_processor=UpperCamelCase       )

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

         A__										=  processor(text=UpperCamelCase    ,						images=UpperCamelCase       )

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

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

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  self.get_image_processor()
         A__										=  self.get_tokenizer()

         A__										=  BlipProcessor(tokenizer=UpperCamelCase    ,						image_processor=UpperCamelCase       )

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

         A__										=  processor.batch_decode(UpperCamelCase       )
         A__										=  tokenizer.batch_decode(UpperCamelCase       )

         self.assertListEqual(UpperCamelCase    ,						UpperCamelCase       )





    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  self.get_image_processor()
         A__										=  self.get_tokenizer()

         A__										=  BlipProcessor(tokenizer=UpperCamelCase    ,						image_processor=UpperCamelCase       )

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

         A__										=  processor(text=UpperCamelCase    ,						images=UpperCamelCase       )

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









"""simple docstring"""



from collections.abc import Callable





def  _snake_case     (    UpperCAmelCase_  :					Callable[[float], float]					,   UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):
     A__										=  a
     A__										=  b
     if function(UpperCAmelCase_			) == 0:  # one of the a or b is a root for the function
          return a
     elif function(UpperCAmelCase_			) == 0:
          return b
     elif (
         function(UpperCAmelCase_			) * function(UpperCAmelCase_			) > 0
     ):  # if none of these are root and they are both positive or negative,
          # then this algorithm can't find the root
          raise ValueError("""could not find root in given interval."""			)
     else:
          A__										=  start + (end - start) / 2.0
          while abs(start - mid			) > 10**-7:  # until precisely equals to 10^-7
               if function(UpperCAmelCase_			) == 0:
                    return mid
               elif function(UpperCAmelCase_			) * function(UpperCAmelCase_			) < 0:
                    A__										=  mid
               else:
                    A__										=  mid
               A__										=  start + (end - start) / 2.0
          return mid





def  _snake_case     (    UpperCAmelCase_  :					float			):
     return x**3 - 2 * x - 5


if __name__ == "__main__":
 print(bisection(f, 1, 1_0_0_0))

 import doctest

 doctest.testmod()






