



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 ViltImageProcessor




class 				lowerCAmelCase__	(						unittest.TestCase    ):







					def __init__(							self   :  Any      ,		_A   :  int      ,		_A   :  List[Any]=7      ,		_A   :  Tuple=3      ,		_A   :  Union[str, Any]=18      ,		_A   :  Optional[int]=30      ,		_A   :  Any=400      ,		_A   :  Optional[int]=True      ,		_A   :  Union[str, Any]=None      ,		_A   :  Dict=2      ,		_A   :  Optional[int]=True      ,		_A   :  List[Any]=[0.5, 0.5, 0.5]      ,		_A   :  List[Any]=[0.5, 0.5, 0.5]      ,		):
											A__						:				Tuple         =			size if size is not None else {"shortest_edge": 30}
											A__						:				Tuple         =			parent
											A__						:				Tuple         =			batch_size
											A__						:				str         =			num_channels
											A__						:				Dict         =			image_size
											A__						:				List[Any]         =			min_resolution
											A__						:				Any         =			max_resolution
											A__						:				Dict         =			do_resize
											A__						:				Optional[int]         =			size
											A__						:				int         =			size_divisor
											A__						:				Optional[Any]         =			do_normalize
											A__						:				Optional[int]         =			image_mean
											A__						:				int         =			image_std







					def 			_lowercase      (							self   :  int):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}







					def 			_lowercase      (							self   :  List[str]      ,		_A   :  str      ,		_A   :  int=False):
											if not batched:
																	A__						:				Optional[Any]         =			self.size["shortest_edge"]
																	A__						:				List[str]         =			image_inputs[0]
																	if isinstance(_A      ,		Image.Image):
																							A__     ,							A__						:				int         =			image.size
																	else:
																							A__     ,							A__						:				Optional[int]         =			image.shape[1], image.shape[2]
																	A__						:				Tuple         =			size / min(_A      ,		_A)
																	if h < w:
																							A__     ,							A__						:				Dict         =			size, scale * w
																	else:
																							A__     ,							A__						:				str         =			scale * h, size

																	A__						:				Dict         =			int((1333 / 800) * size)
																	if max(_A      ,		_A) > max_size:
																							A__						:				str         =			max_size / max(_A      ,		_A)
																							A__						:				Any         =			newh * scale
																							A__						:				List[str]         =			neww * scale

																	A__     ,							A__						:				Tuple         =			int(newh + 0.5), int(neww + 0.5)
																	A__     ,							A__						:				List[Any]         =			(
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

											else:
																	A__						:				Dict         =			[]
																	for image in image_inputs:
																							A__     ,							A__						:				int         =			self.get_expected_values([image])
																							expected_values.append((expected_height, expected_width))
																	A__						:				Optional[int]         =			max(_A      ,		key=lambda _A: item[0])[0]
																	A__						:				Optional[Any]         =			max(_A      ,		key=lambda _A: item[1])[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Dict     =    ViltImageProcessor if is_vision_available() else None







					def 			_lowercase      (							self   :  Optional[int]):
											A__						:				List[Any]         =			ViltImageProcessingTester(self)







					@property
					def 			_lowercase      (							self   :  str):
											return self.image_processor_tester.prepare_image_processor_dict()







					def 			_lowercase      (							self   :  int):
											A__						:				Dict         =			self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(_A      ,		"image_mean"))
											self.assertTrue(hasattr(_A      ,		"image_std"))
											self.assertTrue(hasattr(_A      ,		"do_normalize"))
											self.assertTrue(hasattr(_A      ,		"do_resize"))
											self.assertTrue(hasattr(_A      ,		"size"))
											self.assertTrue(hasattr(_A      ,		"size_divisor"))







					def 			_lowercase      (							self   :  List[str]):
											A__						:				Optional[Any]         =			self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size      ,		{"shortest_edge": 30})

											A__						:				Tuple         =			self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42)
											self.assertEqual(image_processor.size      ,		{"shortest_edge": 42})







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Union[str, Any]):
											# Initialize image_processing
											A__						:				Any         =			self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											A__						:				List[Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		Image.Image)

											# Test not batched input
											A__						:				Optional[int]         =			image_processing(image_inputs[0]      ,		return_tensors="pt").pixel_values

											A__     ,							A__						:				Optional[Any]         =			self.image_processor_tester.get_expected_values(_A)
											self.assertEqual(
											    encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

											# Test batched
											A__						:				List[str]         =			image_processing(_A      ,		return_tensors="pt").pixel_values

											A__     ,							A__						:				List[Any]         =			self.image_processor_tester.get_expected_values(_A      ,		batched=_A)
											self.assertEqual(
											    encoded_images.shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )      ,		)







					def 			_lowercase      (							self   :  Tuple):
											# Initialize image_processing
											A__						:				Dict         =			self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											A__						:				int         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		np.ndarray)

											# Test not batched input
											A__						:				Any         =			image_processing(image_inputs[0]      ,		return_tensors="pt").pixel_values

											A__     ,							A__						:				Tuple         =			self.image_processor_tester.get_expected_values(_A)
											self.assertEqual(
											    encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

											# Test batched
											A__						:				str         =			image_processing(_A      ,		return_tensors="pt").pixel_values

											A__     ,							A__						:				Dict         =			self.image_processor_tester.get_expected_values(_A      ,		batched=_A)
											self.assertEqual(
											    encoded_images.shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )      ,		)







					def 			_lowercase      (							self   :  List[Any]):
											# Initialize image_processing
											A__						:				Optional[Any]         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				Optional[Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)

											# Test not batched input
											A__						:				Any         =			image_processing(image_inputs[0]      ,		return_tensors="pt").pixel_values

											A__     ,							A__						:				List[str]         =			self.image_processor_tester.get_expected_values(_A)
											self.assertEqual(
											    encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

											# Test batched
											A__						:				Tuple         =			image_processing(_A      ,		return_tensors="pt").pixel_values

											A__     ,							A__						:				Any         =			self.image_processor_tester.get_expected_values(_A      ,		batched=_A)
											self.assertEqual(
											    encoded_images.shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )      ,		)




from __future__ import annotations
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  )		->		list[float | int]:







						"""simple docstring"""

						if not all((nth_term, start_term_a, common_ratio_r)							):
												return []
						A__						:				list[float | int]         =			[]
						A__						:				Optional[Any]         =			1
						A__						:				List[Any]         =			common_ratio_r
						for _ in range(int(__lowercase							)							):
												if not series:
																		series.append(__lowercase							)
												else:
																		power += 1
																		series.append(float(start_term_a * multiple							)							)
																		A__						:				Optional[Any]         =			pow(float(__lowercase							)				,  __lowercase							)
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		snake_case     :    Optional[int]						=    float(input('Enter the last number (n term) of the Geometric Series'))
		snake_case     :    str						=    float(input('Enter the starting term (a) of the Geometric Series'))
		snake_case     :    int						=    float(
		    input('Enter the common ratio between two terms (r) of the Geometric Series')
		)
		print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))