






def   UpperCamelCase  (       snake_case__     :   int = 10	,snake_case__     :   int = 1000	,snake_case__     :   bool = True  ):




     '''simple docstring'''






     assert (
         isinstance(snake_case__	,snake_case__  )
         and isinstance(snake_case__	,snake_case__  )
         and isinstance(snake_case__	,snake_case__  )
     ), "Invalid type of value(s) specified to function!"

     if min_val > max_val:
          raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)"""  )
     return min_val if option else max_val






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     return int((number_a + number_a) / 2  )






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     assert (
         isinstance(snake_case__	,snake_case__  ) and isinstance(snake_case__	,snake_case__  ) and isinstance(snake_case__	,snake_case__  )
     ), 'argument values must be type of "int"'

     if lower > higher:
          raise ValueError("""argument value for lower and higher must be(lower > higher)"""  )

     if not lower < to_guess < higher:
          raise ValueError(
              """guess value must be within the range of lower and higher value"""  )

     def answer(snake_case__     :   int  ) -> str:
          if number > to_guess:
               return "high"
          elif number < to_guess:
               return "low"
          else:
               return "same"

     print("""started..."""  )

     __snake_case			:Tuple          =						lower
     __snake_case			:Any          =						higher

     __snake_case			:Tuple          =						[]

     while True:
          __snake_case			:List[str]          =						get_avg(snake_case__	,snake_case__  )
          last_numbers.append(snake_case__  )

          if answer(snake_case__  ) == "low":
               __snake_case			:Any          =						number
          elif answer(snake_case__  ) == "high":
               __snake_case			:Tuple          =						number
          else:
               break

     print(f'''guess the number : {last_numbers[-1]}'''  )
     print(f'''details : {last_numbers!s}'''  )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						int(input("""Enter lower value : """  ).strip()  )
     __snake_case			:Optional[Any]          =						int(input("""Enter high value : """  ).strip()  )
     __snake_case			:str          =						int(input("""Enter value to guess : """  ).strip()  )
     guess_the_number(snake_case__	,snake_case__	,snake_case__  )


if __name__ == "__main__":
     main()










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=3			,						a__=18			,						a__=30			,						a__=4_00			,						a__=True			,						a__=32			,						a__=True			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Dict          =						batch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:Dict          =						image_size
            __snake_case			:Dict          =						min_resolution
            __snake_case			:Dict          =						max_resolution
            __snake_case			:List[Any]          =						do_resize
            __snake_case			:Dict          =						size_divisor
            __snake_case			:Union[str, Any]          =						do_rescale



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




@require_torch
@require_vision
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= GLPNImageProcessor if is_vision_available() else None



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						GLPNImageProcessingTester(self    )



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.image_processor_tester.prepare_image_processor_dict()



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(a__			,						"""do_resize"""    )    )
            self.assertTrue(hasattr(a__			,						"""size_divisor"""    )    )
            self.assertTrue(hasattr(a__			,						"""resample"""    )    )
            self.assertTrue(hasattr(a__			,						"""do_rescale"""    )    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case			:Optional[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:Union[str, Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case			:List[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						numpify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						np.ndarray    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:int          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case			:Optional[int]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						torchify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						torch.Tensor    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:List[Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


