
"""simple docstring"""






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 MobileNetVaImageProcessor




class      _SCREAMING_SNAKE_CASE   (     unittest.TestCase				):






			def __init__(   self			,  __A			,  __A=7			,  __A=3			,  __A=18			,  __A=30			,  __A=400			,  __A=True			,  __A=None			,  __A=True			,  __A=None			,  )     ->	Any:
						lowerCAmelCase_       :Tuple     	=    size if size is not None else {"""shortest_edge""": 20}
						lowerCAmelCase_       :List[str]     	=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
						lowerCAmelCase_       :List[Any]     	=    parent
						lowerCAmelCase_       :Optional[int]     	=    batch_size
						lowerCAmelCase_       :str     	=    num_channels
						lowerCAmelCase_       :str     	=    image_size
						lowerCAmelCase_       :Union[str, Any]     	=    min_resolution
						lowerCAmelCase_       :str     	=    max_resolution
						lowerCAmelCase_       :Optional[int]     	=    do_resize
						lowerCAmelCase_       :Tuple     	=    size
						lowerCAmelCase_       :List[str]     	=    do_center_crop
						lowerCAmelCase_       :List[Any]     	=    crop_size






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						return {
						    "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   (     UpperCAmelCase__      ,					unittest.TestCase				):
			UpperCAmelCase_    :Union[str, Any]												=		MobileNetVaImageProcessor if is_vision_available() else None






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Dict     	=    MobileNetVaImageProcessingTester(self			)






			@property
			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						return self.image_processor_tester.prepare_image_processor_dict()






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_       :str     	=    self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCAmelCase			,  """do_resize"""			)			)
						self.assertTrue(hasattr(__lowerCAmelCase			,  """size"""			)			)
						self.assertTrue(hasattr(__lowerCAmelCase			,  """do_center_crop"""			)			)
						self.assertTrue(hasattr(__lowerCAmelCase			,  """center_crop"""			)			)






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_       :Optional[Any]     	=    self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size			,  {"""shortest_edge""": 20}			)
						self.assertEqual(image_processor.crop_size			,  {"""height""": 18, """width""": 18}			)

						lowerCAmelCase_       :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 					__lowerCAmelCase					(   self			)     ->	Tuple:
						pass






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Any     	=    self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						lowerCAmelCase_       :List[Any]     	=    prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__lowerCAmelCase			)
						for image in image_inputs:
									self.assertIsInstance(__lowerCAmelCase			,  Image.Image			)

						# Test not batched input
						lowerCAmelCase_       :Optional[int]     	=    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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )			,  )

						# Test batched
						lowerCAmelCase_       :int     	=    image_processing(__lowerCAmelCase			,  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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )			,  )






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_       :Optional[int]     	=    self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase_       :str     	=    prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__lowerCAmelCase			,  numpify=__lowerCAmelCase			)
						for image in image_inputs:
									self.assertIsInstance(__lowerCAmelCase			,  np.ndarray			)

						# Test not batched input
						lowerCAmelCase_       :List[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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )			,  )

						# Test batched
						lowerCAmelCase_       :str     	=    image_processing(__lowerCAmelCase			,  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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )			,  )






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						lowerCAmelCase_       :List[Any]     	=    self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase_       :Tuple     	=    prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__lowerCAmelCase			,  torchify=__lowerCAmelCase			)
						for image in image_inputs:
									self.assertIsInstance(__lowerCAmelCase			,  torch.Tensor			)

						# Test not batched input
						lowerCAmelCase_       :int     	=    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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )			,  )

						# Test batched
						lowerCAmelCase_       :Any     	=    image_processing(__lowerCAmelCase			,  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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )			,  )











from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       ):






 '''simple docstring'''

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  Optional[Any]										=  float(input('''Enter the last number (n term) of the Geometric Series'''))
     A_		:  Optional[Any]										=  float(input('''Enter the starting term (a) of the Geometric Series'''))
     A_		:  Optional[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))

