






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_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VideoMAEImageProcessor

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def __init__(						self							:   Tuple					,    __lowerCamelCase							:   List[str]					,    __lowerCamelCase							:   List[Any]=7					,    __lowerCamelCase							:   List[str]=3					,    __lowerCamelCase							:   str=10					,    __lowerCamelCase							:   Optional[int]=18					,    __lowerCamelCase							:   str=30					,    __lowerCamelCase							:   List[Any]=400					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Any=True					,    __lowerCamelCase							:   Tuple=[0.5, 0.5, 0.5]					,    __lowerCamelCase							:   Union[str, Any]=[0.5, 0.5, 0.5]					,    __lowerCamelCase							:   Optional[Any]=None					,    ):
		UpperCamelCase	:str		=      size if size is not None else {"""shortest_edge""": 18}
		UpperCamelCase	:List[str]		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

		UpperCamelCase	:int		=      parent
		UpperCamelCase	:Tuple		=      batch_size
		UpperCamelCase	:Union[str, Any]		=      num_channels
		UpperCamelCase	:Optional[int]		=      num_frames
		UpperCamelCase	:Dict		=      image_size
		UpperCamelCase	:Dict		=      min_resolution
		UpperCamelCase	:str		=      max_resolution
		UpperCamelCase	:Dict		=      do_resize
		UpperCamelCase	:Optional[Any]		=      size
		UpperCamelCase	:Tuple		=      do_normalize
		UpperCamelCase	:int		=      image_mean
		UpperCamelCase	:List[Any]		=      image_std
		UpperCamelCase	:Dict		=      crop_size





	def 		_A					(						self							:   Optional[Any]      ):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "crop_size": self.crop_size,
		}

@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE			(     _a						,  unittest.TestCase ):
	snake_case__				:	Dict	      =    VideoMAEImageProcessor if is_vision_available() else None





	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:Optional[Any]		=      VideoMAEImageProcessingTester(self      )





	@property
	def 		_A					(						self							:   int      ):
		return self.image_processor_tester.prepare_image_processor_dict()





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict      )
		self.assertTrue(hasattr(__lowerCamelCase					,    """image_mean"""      )      )
		self.assertTrue(hasattr(__lowerCamelCase					,    """image_std"""      )      )
		self.assertTrue(hasattr(__lowerCamelCase					,    """do_normalize"""      )      )
		self.assertTrue(hasattr(__lowerCamelCase					,    """do_resize"""      )      )
		self.assertTrue(hasattr(__lowerCamelCase					,    """do_center_crop"""      )      )
		self.assertTrue(hasattr(__lowerCamelCase					,    """size"""      )      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict      )
		self.assertEqual(image_processor.size					,    {"""shortest_edge""": 18}      )
		self.assertEqual(image_processor.crop_size					,    {"""height""": 18, """width""": 18}      )

		UpperCamelCase	:List[str]		=      self.image_processing_class.from_dict(self.image_processor_dict					,    size=42					,    crop_size=84      )
		self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}      )
		self.assertEqual(image_processor.crop_size					,    {"""height""": 84, """width""": 84}      )





	def 		_A					(						self							:   Optional[Any]      ):
		pass





	def 		_A					(						self							:   int      ):
		# Initialize image_processing
		UpperCamelCase	:Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PIL videos
		UpperCamelCase	:int		=      prepare_video_inputs(self.image_processor_tester					,    equal_resolution=__lowerCamelCase      )
		for video in video_inputs:
			self.assertIsInstance(__lowerCamelCase					,    __lowerCamelCase      )
			self.assertIsInstance(video[0]					,    Image.Image      )

		# Test not batched input
		UpperCamelCase	:List[Any]		=      image_processing(video_inputs[0]					,    return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_videos.shape					,    (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )					,    )

		# Test batched
		UpperCamelCase	:Optional[Any]		=      image_processing(__lowerCamelCase					,    return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_videos.shape					,    (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )					,    )





	def 		_A					(						self							:   Optional[Any]      ):
		# Initialize image_processing
		UpperCamelCase	:int		=      self.image_processing_class(**self.image_processor_dict      )
		# create random numpy tensors
		UpperCamelCase	:str		=      prepare_video_inputs(self.image_processor_tester					,    equal_resolution=__lowerCamelCase					,    numpify=__lowerCamelCase      )
		for video in video_inputs:
			self.assertIsInstance(__lowerCamelCase					,    __lowerCamelCase      )
			self.assertIsInstance(video[0]					,    np.ndarray      )

		# Test not batched input
		UpperCamelCase	:str		=      image_processing(video_inputs[0]					,    return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_videos.shape					,    (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )					,    )

		# Test batched
		UpperCamelCase	:List[Any]		=      image_processing(__lowerCamelCase					,    return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_videos.shape					,    (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )					,    )





	def 		_A					(						self							:   Any      ):
		# Initialize image_processing
		UpperCamelCase	:Dict		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PyTorch tensors
		UpperCamelCase	:str		=      prepare_video_inputs(self.image_processor_tester					,    equal_resolution=__lowerCamelCase					,    torchify=__lowerCamelCase      )
		for video in video_inputs:
			self.assertIsInstance(__lowerCamelCase					,    __lowerCamelCase      )
			self.assertIsInstance(video[0]					,    torch.Tensor      )

		# Test not batched input
		UpperCamelCase	:Any		=      image_processing(video_inputs[0]					,    return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_videos.shape					,    (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )					,    )

		# Test batched
		UpperCamelCase	:Optional[Any]		=      image_processing(__lowerCamelCase					,    return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_videos.shape					,    (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )					,    )








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()
