




'''simple docstring'''



import json
import os
import tempfile

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


if is_torch_available():
     import numpy as np
     import torch

if is_vision_available():
     from PIL import Image







def      _UpperCamelCase	(_lowerCamelCase							:       int				,		_lowerCamelCase							:       int=False				,		_lowerCamelCase							:       Tuple=False				,		_lowerCamelCase							:       List[Any]=False		)->				Dict:




     '''simple docstring'''





     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     __snake_case         =					[]
     for i in range(image_processor_tester.batch_size		):
          if equal_resolution:
               __snake_case         =					__snake_case         =					image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               __snake_case         =					image_processor_tester.min_resolution
               if getattr(_lowerCamelCase				,		'''size_divisor'''				,		_lowerCamelCase		):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    __snake_case         =					max(image_processor_tester.size_divisor				,		_lowerCamelCase		)
               __snake_case   ,      __snake_case         =					np.random.choice(np.arange(_lowerCamelCase				,		image_processor_tester.max_resolution		)				,		2		)
          image_inputs.append(
              np.random.randint(2_55				,		size=(image_processor_tester.num_channels, width, height)				,		dtype=np.uinta		)		)

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          __snake_case         =					[Image.fromarray(np.moveaxis(_lowerCamelCase				,		0				,		-1		)		) for image in image_inputs]

     if torchify:
          __snake_case         =					[torch.from_numpy(_lowerCamelCase		) for image in image_inputs]

     return image_inputs







def      _UpperCamelCase	(_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Tuple=10				,		_lowerCamelCase							:       List[str]=10				,		_lowerCamelCase							:       Optional[Any]=False				,		_lowerCamelCase							:       str=False		)->				Dict:




     '''simple docstring'''





     __snake_case         =					[]
     for i in range(image_processor_tester.num_frames		):
          video.append(np.random.randint(2_55				,		size=(image_processor_tester.num_channels, width, height)				,		dtype=np.uinta		)		)

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          __snake_case         =					[Image.fromarray(np.moveaxis(_lowerCamelCase				,		0				,		-1		)		) for frame in video]

     if torchify:
          __snake_case         =					[torch.from_numpy(_lowerCamelCase		) for frame in video]

     return video







def      _UpperCamelCase	(_lowerCamelCase							:       List[str]				,		_lowerCamelCase							:       Any=False				,		_lowerCamelCase							:       int=False				,		_lowerCamelCase							:       Optional[int]=False		)->				Union[str, Any]:




     '''simple docstring'''





     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     __snake_case         =					[]
     for i in range(image_processor_tester.batch_size		):
          if equal_resolution:
               __snake_case         =					__snake_case         =					image_processor_tester.max_resolution
          else:
               __snake_case   ,      __snake_case         =					np.random.choice(
                   np.arange(image_processor_tester.min_resolution				,		image_processor_tester.max_resolution		)				,		2		)
               __snake_case         =					prepare_video(
                   image_processor_tester=_lowerCamelCase				,		width=_lowerCamelCase				,		height=_lowerCamelCase				,		numpify=_lowerCamelCase				,		torchify=_lowerCamelCase				,		)
          video_inputs.append(_lowerCamelCase		)

     return video_inputs







class       lowerCAmelCase				:
       __lowercase					: Tuple	    =     None


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					self.image_processing_class(**self.image_processor_dict					)
            __snake_case         =					json.loads(image_processor.to_json_string()					)
            for key, value in self.image_processor_dict.items():
                 self.assertEqual(obj[key]      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					self.image_processing_class(**self.image_processor_dict					)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					'''image_processor.json'''					)
                 image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE					)

            self.assertEqual(image_processor_second.to_dict()      ,					image_processor_first.to_dict()					)


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



            '''simple docstring'''




            __snake_case         =					self.image_processing_class(**self.image_processor_dict					)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 __snake_case         =					image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE					)[0]
                 check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE					)

            self.assertEqual(image_processor_second.to_dict()      ,					image_processor_first.to_dict()					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					self.image_processing_class()
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)




       @require_torch
       @require_vision
       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            if self.test_cast_dtype is not None:
                 # Initialize image_processor
                 __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=__SCREAMING_SNAKE_CASE      ,					torchify=__SCREAMING_SNAKE_CASE					)

                 __snake_case         =					image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors='''pt'''					)
                 # for layoutLM compatiblity
                 self.assertEqual(encoding.pixel_values.device      ,					torch.device('''cpu'''					)					)
                 self.assertEqual(encoding.pixel_values.dtype      ,					torch.floataa					)

                 __snake_case         =					image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors='''pt'''					).to(torch.floataa					)
                 self.assertEqual(encoding.pixel_values.device      ,					torch.device('''cpu'''					)					)
                 self.assertEqual(encoding.pixel_values.dtype      ,					torch.floataa					)

                 __snake_case         =					image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors='''pt'''					).to('''cpu'''      ,					torch.bfloataa					)
                 self.assertEqual(encoding.pixel_values.device      ,					torch.device('''cpu'''					)					)
                 self.assertEqual(encoding.pixel_values.dtype      ,					torch.bfloataa					)

                 with self.assertRaises(__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors='''pt'''					).to(torch.bfloataa      ,					'''cpu'''					)

                 # Try with text + image feature
                 __snake_case         =					image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors='''pt'''					)
                 encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
                 __snake_case         =					encoding.to(torch.floataa					)

                 self.assertEqual(encoding.pixel_values.device      ,					torch.device('''cpu'''					)					)
                 self.assertEqual(encoding.pixel_values.dtype      ,					torch.floataa					)
                 self.assertEqual(encoding.input_ids.dtype      ,					torch.long					)












'''simple docstring'''



def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     for index, item in enumerate(_lowerCamelCase		):
          if item == target:
               return index
     return -1







def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     if not (0 <= high < len(_lowerCamelCase		) and 0 <= low < len(_lowerCamelCase		)):
          raise Exception('''Invalid upper or lower bound!'''		)
     if high < low:
          return -1
     if sequence[low] == target:
          return low
     if sequence[high] == target:
          return high
     return rec_linear_search(_lowerCamelCase				,		low + 1				,		high - 1				,		_lowerCamelCase		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[Any]					      =						input('''Enter numbers separated by comma:\n''').strip()
     UpperCAmelCase_    :					Optional[int]					      =						[int(item.strip()) for item in user_input.split(''',''')]

     UpperCAmelCase_    :					List[str]					      =						int(input('''Enter a single number to be found in the list:\n''').strip())
     UpperCAmelCase_    :					Optional[int]					      =						linear_search(sequence, target)
     if result != -1:
          print(F"""linear_search({sequence}, {target}) = {result}""")
     else:
          print(F"""{target} was not found in {sequence}""")






