'''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    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				List[str]=False      ,	lowerCamelCase_   :				Union[str, Any]=False      ,	lowerCamelCase_   :				Any=False  ):



     """simple docstring"""





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

     UpperCAmelCase_				:  int  = []
     for i in range(image_processor_tester.batch_size  ):
          if equal_resolution:
               UpperCAmelCase_				:  Optional[Any]  = image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               UpperCAmelCase_				:  List[Any]  = 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`
                    UpperCAmelCase_				:  int  = max(image_processor_tester.size_divisor      ,	lowerCamelCase_  )
               UpperCAmelCase_				:  List[str]  = np.random.choice(np.arange(lowerCamelCase_      ,	image_processor_tester.max_resolution  )      ,	2  )
          image_inputs.append(
              np.random.randint(255      ,	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
          UpperCAmelCase_				:  Any  = [Image.fromarray(np.moveaxis(lowerCamelCase_      ,	0      ,	-1  )  ) for image in image_inputs]

     if torchify:
          UpperCAmelCase_				:  Dict  = [torch.from_numpy(lowerCamelCase_  ) for image in image_inputs]

     return image_inputs


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				int=10      ,	lowerCamelCase_   :				Dict=10      ,	lowerCamelCase_   :				List[Any]=False      ,	lowerCamelCase_   :				Optional[int]=False  ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = []
     for i in range(image_processor_tester.num_frames  ):
          video.append(np.random.randint(255      ,	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
          UpperCAmelCase_				:  Union[str, Any]  = [Image.fromarray(np.moveaxis(lowerCamelCase_      ,	0      ,	-1  )  ) for frame in video]

     if torchify:
          UpperCAmelCase_				:  List[Any]  = [torch.from_numpy(lowerCamelCase_  ) for frame in video]

     return video


def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Union[str, Any]=False      ,	lowerCamelCase_   :				Union[str, Any]=False      ,	lowerCamelCase_   :				Dict=False  ):



     """simple docstring"""





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

     UpperCAmelCase_				:  int  = []
     for i in range(image_processor_tester.batch_size  ):
          if equal_resolution:
               UpperCAmelCase_				:  str  = image_processor_tester.max_resolution
          else:
               UpperCAmelCase_				:  List[Any]  = np.random.choice(
                   np.arange(image_processor_tester.min_resolution      ,	image_processor_tester.max_resolution  )      ,	2  )
               UpperCAmelCase_				:  int  = prepare_video(
                   image_processor_tester=lowerCamelCase_      ,	width=lowerCamelCase_      ,	height=lowerCamelCase_      ,	numpify=lowerCamelCase_      ,	torchify=lowerCamelCase_      ,	)
          video_inputs.append(lowerCamelCase_  )

     return video_inputs






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  lowerCamelCase_						:Union[str, Any]		       =						None


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = self.image_processing_class(**self.image_processor_dict		)
       UpperCAmelCase_				:  List[Any]  = json.loads(image_processor.to_json_string()		)
       for key, value in self.image_processor_dict.items():
            self.assertEqual(obj[key] ,						__lowerCamelCase		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.image_processing_class(**self.image_processor_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
            UpperCAmelCase_				:  str  = os.path.join(__lowerCamelCase ,						'image_processor.json'		)
            image_processor_first.to_json_file(__lowerCamelCase		)
            UpperCAmelCase_				:  Optional[Any]  = self.image_processing_class.from_json_file(__lowerCamelCase		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.image_processing_class(**self.image_processor_dict		)

       with tempfile.TemporaryDirectory() as tmpdirname:
            UpperCAmelCase_				:  str  = image_processor_first.save_pretrained(__lowerCamelCase		)[0]
            check_json_file_has_correct_format(__lowerCamelCase		)
            UpperCAmelCase_				:  Union[str, Any]  = self.image_processing_class.from_pretrained(__lowerCamelCase		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = self.image_processing_class()
       self.assertIsNotNone(__lowerCamelCase		)






  @require_torch
  @require_vision
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if self.test_cast_dtype is not None:
            # Initialize image_processor
            UpperCAmelCase_				:  List[Any]  = self.image_processing_class(**self.image_processor_dict		)

            # create random PyTorch tensors
            UpperCAmelCase_				:  Tuple  = prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__lowerCamelCase ,						torchify=__lowerCamelCase		)

            UpperCAmelCase_				:  Tuple  = image_processor(__lowerCamelCase ,						return_tensors='pt'		)
            # for layoutLM compatiblity
            self.assertEqual(encoding.pixel_values.device ,						torch.device('cpu'		)		)
            self.assertEqual(encoding.pixel_values.dtype ,						torch.floataa		)

            UpperCAmelCase_				:  int  = image_processor(__lowerCamelCase ,						return_tensors='pt'		).to(torch.floataa		)
            self.assertEqual(encoding.pixel_values.device ,						torch.device('cpu'		)		)
            self.assertEqual(encoding.pixel_values.dtype ,						torch.floataa		)

            UpperCAmelCase_				:  List[str]  = image_processor(__lowerCamelCase ,						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(__lowerCamelCase		):
                 UpperCAmelCase_				:  Dict  = image_processor(__lowerCamelCase ,						return_tensors='pt'		).to(torch.bfloataa ,						'cpu'		)

            # Try with text + image feature
            UpperCAmelCase_				:  List[str]  = image_processor(__lowerCamelCase ,						return_tensors='pt'		)
            encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]		)}		)
            UpperCAmelCase_				:  Tuple  = 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		)














class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   int      ):
		if (difference := index - (len(self.sequence      ) - 2)) >= 1:
			for _ in range(__lowerCamelCase      ):
				self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
		return self.sequence[:index]

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""simple docstring"""



	print(
	    """Fibonacci Series Using Dynamic Programming\n"""      ,     """Enter the index of the Fibonacci number you want to calculate """      ,     """in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,     sep=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

	while True:
		UpperCamelCase	:str		=      input(""">> """   )
		if prompt in {"exit", "quit"}:
			break

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
