





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image


def 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Tuple=False			,							lowerCAmelCase:		int=False			,							lowerCAmelCase:		Union[str, Any]=False )->						Tuple:
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _snake_case      : List[str]														=		[]
  for i in range(image_processor_tester.batch_size ):
    if equal_resolution:
      _snake_case      : Optional[int]														=		image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      _snake_case      : int														=		image_processor_tester.min_resolution
      if getattr(lowerCAmelCase			,							'size_divisor'			,							lowerCAmelCase ):
        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
        _snake_case      : str														=		max(image_processor_tester.size_divisor			,							lowerCAmelCase )
      _snake_case							,    _snake_case      : int														=		np.random.choice(np.arange(lowerCAmelCase			,							image_processor_tester.max_resolution )			,							2 )
    image_inputs.append(
        np.random.randint(2_55			,							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
    _snake_case      : Dict														=		[Image.fromarray(np.moveaxis(lowerCAmelCase			,							0			,							-1 ) ) for image in image_inputs]

  if torchify:
    _snake_case      : Optional[int]														=		[torch.from_numpy(lowerCAmelCase ) for image in image_inputs]

  return image_inputs


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		int=10			,							lowerCAmelCase:		Union[str, Any]=10			,							lowerCAmelCase:		int=False			,							lowerCAmelCase:		Optional[int]=False )->						int:
  _snake_case      : int														=		[]
  for i in range(image_processor_tester.num_frames ):
    video.append(np.random.randint(2_55			,							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
    _snake_case      : List[str]														=		[Image.fromarray(np.moveaxis(lowerCAmelCase			,							0			,							-1 ) ) for frame in video]

  if torchify:
    _snake_case      : Any														=		[torch.from_numpy(lowerCAmelCase ) for frame in video]

  return video


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		Union[str, Any]=False			,							lowerCAmelCase:		int=False			,							lowerCAmelCase:		Union[str, Any]=False )->						Dict:
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

  return video_inputs







class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				List[Any]					    =None







  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		self.image_processing_class(**self.image_processor_dict							)
    _snake_case      : List[str]														=		json.loads(image_processor.to_json_string()							)
    for key, value in self.image_processor_dict.items():
      self.assertEqual(obj[key]					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.image_processing_class(**self.image_processor_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _snake_case      : Optional[int]														=		os.path.join(UpperCamelCase					, 'image_processor.json'							)
      image_processor_first.to_json_file(UpperCamelCase							)
      _snake_case      : List[str]														=		self.image_processing_class.from_json_file(UpperCamelCase							)

    self.assertEqual(image_processor_second.to_dict()					, image_processor_first.to_dict()							)







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Any														=		self.image_processing_class(**self.image_processor_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _snake_case      : List[Any]														=		image_processor_first.save_pretrained(UpperCamelCase							)[0]
      check_json_file_has_correct_format(UpperCamelCase							)
      _snake_case      : List[Any]														=		self.image_processing_class.from_pretrained(UpperCamelCase							)

    self.assertEqual(image_processor_second.to_dict()					, image_processor_first.to_dict()							)







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    _snake_case      : Dict														=		self.image_processing_class()
    self.assertIsNotNone(UpperCamelCase							)







  @require_torch
  @require_vision
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    if self.test_cast_dtype is not None:
      # Initialize image_processor
      _snake_case      : Dict														=		self.image_processing_class(**self.image_processor_dict							)

      # create random PyTorch tensors
      _snake_case      : Optional[int]														=		prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCamelCase					, torchify=UpperCamelCase							)

      _snake_case      : Dict														=		image_processor(UpperCamelCase					, return_tensors='pt'							)
      # for layoutLM compatiblity
      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.floataa							)

      _snake_case      : Dict														=		image_processor(UpperCamelCase					, return_tensors='pt'							).to(torch.floataa							)
      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.floataa							)

      _snake_case      : str														=		image_processor(UpperCamelCase					, return_tensors='pt'							).to('cpu'					, torch.bfloataa							)
      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.bfloataa							)

      with self.assertRaises(UpperCamelCase							):
        _snake_case      : Any														=		image_processor(UpperCamelCase					, return_tensors='pt'							).to(torch.bfloataa					, 'cpu'							)

      # Try with text + image feature
      _snake_case      : Tuple														=		image_processor(UpperCamelCase					, return_tensors='pt'							)
      encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]							)}							)
      _snake_case      : str														=		encoding.to(torch.floataa							)

      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.floataa							)
      self.assertEqual(encoding.input_ids.dtype					, torch.long							)











"""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))

