
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 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=7		,	lowerCamelCase=3		,	lowerCamelCase=18		,	lowerCamelCase=30		,	lowerCamelCase=4_00		,	lowerCamelCase=True		,	lowerCamelCase=32		,	lowerCamelCase=True		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				num_channels
          snake_case__						=				image_size
          snake_case__						=				min_resolution
          snake_case__						=				max_resolution
          snake_case__						=				do_resize
          snake_case__						=				size_divisor
          snake_case__						=				do_rescale



      def A_			(			self ):
          return {
              "do_resize": self.do_resize,
              "size_divisor": self.size_divisor,
              "do_rescale": self.do_rescale,
          }





@require_torch
@require_vision
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Optional[Any]    	=     GLPNImageProcessor if is_vision_available() else None
      def A_			(			self ):
          snake_case__						=				GLPNImageProcessingTester(self )
      @property
      def A_			(			self ):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_			(			self ):
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_resize" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"size_divisor" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"resample" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_rescale" ) )
      def A_			(			self ):
          pass
      def A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          snake_case__						=				prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	Image.Image )

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          snake_case__						=				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 A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          snake_case__						=				prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase		,	numpify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	np.ndarray )

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          snake_case__						=				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 A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          snake_case__						=				prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase		,	torchify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	torch.Tensor )

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          snake_case__						=				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 )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if len(__lowerCAmelCase							) == 0:
        return False
    snake_case__						=				len(__lowerCAmelCase							) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]    ,		__lowerCAmelCase							)
    else:
        return binary_search(a_list[midpoint + 1 :]    ,		__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  input('''Enter numbers separated by comma:\n''').strip()
     __magic_name__            =  [int(item.strip()) for item in user_input.split(''',''')]
     __magic_name__            =  int(input('''Enter the number to be found in the list:\n''').strip())
     __magic_name__            =  '''''' if binary_search(sequence, target) else '''not '''
     print(F'''{target} was {not_str}found in {sequence}''')



