




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 ViTImageProcessor







class       UpperCAmelCase(     unittest.TestCase     ):



	"""simple docstring"""






	def __init__(     self       ,      lowerCamelCase       ,      lowerCamelCase=7       ,      lowerCamelCase=3       ,      lowerCamelCase=18       ,      lowerCamelCase=30       ,      lowerCamelCase=400       ,      lowerCamelCase=True       ,      lowerCamelCase=None       ,      lowerCamelCase=True       ,      lowerCamelCase=[0.5, 0.5, 0.5]       ,      lowerCamelCase=[0.5, 0.5, 0.5]       ,      )					->			Optional[int]:





								"""simple docstring"""


								lowercase__  :       List[str]   					=							size if size is not None else {"height": 18, "width": 18}
								lowercase__  :       Optional[Any]   					=							parent
								lowercase__  :       Optional[int]   					=							batch_size
								lowercase__  :       str   					=							num_channels
								lowercase__  :       List[str]   					=							image_size
								lowercase__  :       List[Any]   					=							min_resolution
								lowercase__  :       Optional[int]   					=							max_resolution
								lowercase__  :       List[str]   					=							do_resize
								lowercase__  :       Tuple   					=							size
								lowercase__  :       Tuple   					=							do_normalize
								lowercase__  :       Any   					=							image_mean
								lowercase__  :       str   					=							image_std







	def     __a	(     self				)					->			int:





								"""simple docstring"""


								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}







@require_torch
@require_vision
class       UpperCAmelCase(     __lowerCAmelCase    ,	unittest.TestCase     ):



	"""simple docstring"""






	a	:					Tuple         =     ViTImageProcessor if is_vision_available() else None

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





								"""simple docstring"""


								lowercase__  :       List[Any]   					=							ViTImageProcessingTester(self				)

	@property
	def     __a	(     self				)					->			Union[str, Any]:





								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()

	def     __a	(     self				)					->			int:





								"""simple docstring"""


								lowercase__  :       Union[str, Any]   					=							self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "image_mean"				)				)
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "image_std"				)				)
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "do_normalize"				)				)
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "do_resize"				)				)
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "size"				)				)

	def     __a	(     self				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       List[str]   					=							self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size       ,      {"height": 18, "width": 18}				)

								lowercase__  :       Dict   					=							self.image_processing_class.from_dict(self.image_processor_dict       ,      size=42				)
								self.assertEqual(image_processor.size       ,      {"height": 42, "width": 42}				)

	def     __a	(     self				)					->			Dict:





								"""simple docstring"""


								pass

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





								"""simple docstring"""


								lowercase__  :       Union[str, Any]   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								lowercase__  :       List[str]   					=							prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__SCREAMING_SNAKE_CASE				)
								for image in image_inputs:
															self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,      Image.Image				)

								# Test not batched input
								lowercase__  :       Any   					=							image_processing(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       Tuple   					=							image_processing(__SCREAMING_SNAKE_CASE       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

	def     __a	(     self				)					->			str:





								"""simple docstring"""


								lowercase__  :       str   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								lowercase__  :       List[Any]   					=							prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__SCREAMING_SNAKE_CASE       ,      numpify=__SCREAMING_SNAKE_CASE				)
								for image in image_inputs:
															self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,      np.ndarray				)

								# Test not batched input
								lowercase__  :       List[str]   					=							image_processing(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       int   					=							image_processing(__SCREAMING_SNAKE_CASE       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )







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





								"""simple docstring"""


								lowercase__  :       int   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								lowercase__  :       int   					=							prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__SCREAMING_SNAKE_CASE       ,      torchify=__SCREAMING_SNAKE_CASE				)
								for image in image_inputs:
															self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,      torch.Tensor				)

								# Test not batched input
								lowercase__  :       Tuple   					=							image_processing(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       Optional[int]   					=							image_processing(__SCREAMING_SNAKE_CASE       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )






"""simple docstring"""


from __future__ import annotations





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   )			->     list[float | int]:

 '''simple docstring'''






 if not all((nth_term, start_term_a, common_ratio_r)		):
  return []
 snake_case__										= []
 snake_case__										= 1
 snake_case__										= common_ratio_r
 for _ in range(int(_UpperCAmelCase		)		):
  if not series:
   series.append(_UpperCAmelCase		)
  else:
   power += 1
   series.append(float(start_term_a * multiple		)		)
   snake_case__										= pow(float(_UpperCAmelCase		)		,   _UpperCAmelCase		)
 return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   a__    :   int    						= float(input("""Enter the last number (n term) of the Geometric Series"""))
   a__    :   str    						= float(input("""Enter the starting term (a) of the Geometric Series"""))
   a__    :   str    						= 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))

