






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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import TvltImageProcessor

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict      ,     __magic_name__   :    Optional[Any]=10      ,     __magic_name__   :    int=10      ,     __magic_name__   :    Optional[Any]=False      ,     __magic_name__   :    int=False   )						->     Optional[int]:






	"""simple docstring"""



	UpperCamelCase	:List[Any]		=      []
	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	:Dict		=      [Image.fromarray(np.moveaxis(__magic_name__      ,     0      ,     -1   )   ) for frame in video]

	if torchify:
		UpperCamelCase	:Tuple		=      [torch.from_numpy(__magic_name__   ) for frame in video]

	return video

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]      ,     __magic_name__   :    List[str]=False      ,     __magic_name__   :    int=False      ,     __magic_name__   :    List[str]=False   )						->     List[Any]:






	"""simple docstring"""



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

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

	return video_inputs

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   int=7					,    __lowerCamelCase							:   List[str]=3					,    __lowerCamelCase							:   Union[str, Any]=4					,    __lowerCamelCase							:   Tuple=18					,    __lowerCamelCase							:   List[str]=30					,    __lowerCamelCase							:   List[Any]=400					,    __lowerCamelCase							:   Tuple=True					,    __lowerCamelCase							:   Optional[int]=None					,    __lowerCamelCase							:   Tuple=True					,    __lowerCamelCase							:   Dict=[0.5, 0.5, 0.5]					,    __lowerCamelCase							:   List[Any]=[0.5, 0.5, 0.5]					,    __lowerCamelCase							:   Union[str, Any]=True					,    __lowerCamelCase							:   Tuple=None					,    ):
		UpperCamelCase	:Dict		=      size if size is not None else {"""shortest_edge""": 18}
		UpperCamelCase	:List[Any]		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

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





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

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





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Any		=      TvltImageProcessorTester(self      )





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





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:Optional[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]      ):
		# Initialize image_processor
		UpperCamelCase	:Tuple		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PIL videos
		UpperCamelCase	:Dict		=      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	:int		=      image_processor(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[int]		=      image_processor(__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							:   List[Any]      ):
		# Initialize image_processor
		UpperCamelCase	:Any		=      self.image_processing_class(**self.image_processor_dict      )
		# create random numpy tensors
		UpperCamelCase	:List[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	:Optional[Any]		=      image_processor(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	:Any		=      image_processor(__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							:   str      ):
		# Initialize image_processor
		UpperCamelCase	:Dict		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PyTorch tensors
		UpperCamelCase	:Union[str, Any]		=      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	:List[Any]		=      image_processor(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	:Dict		=      image_processor(__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()
