


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 PoolFormerImageProcessor


class 			__lowerCAmelCase   (       unittest.TestCase    ):






  """simple docstring"""





  def __init__(   self				:				List[Any]			, _lowerCAmelCase				:				Any			, _lowerCAmelCase				:				Dict=7			, _lowerCAmelCase				:				List[str]=3			, _lowerCAmelCase				:				str=3_0			, _lowerCAmelCase				:				List[str]=4_0_0			, _lowerCAmelCase				:				Optional[int]=True			, _lowerCAmelCase				:				Optional[int]=None			, _lowerCAmelCase				:				Tuple=0.9			, _lowerCAmelCase				:				str=None			, _lowerCAmelCase				:				int=True			, _lowerCAmelCase				:				Union[str, Any]=[0.5, 0.5, 0.5]			, _lowerCAmelCase				:				Optional[Any]=[0.5, 0.5, 0.5]			, )      ->       str:




     """simple docstring"""
     snake_case_    			=    size if size is not None else {"shortest_edge": 3_0}
     snake_case_    			=    crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
     snake_case_    			=    parent
     snake_case_    			=    batch_size
     snake_case_    			=    num_channels
     snake_case_    			=    min_resolution
     snake_case_    			=    max_resolution
     snake_case_    			=    do_resize_and_center_crop
     snake_case_    			=    size
     snake_case_    			=    crop_pct
     snake_case_    			=    crop_size
     snake_case_    			=    do_normalize
     snake_case_    			=    image_mean
     snake_case_    			=    image_std



  def     lowerCAmelCase__     (   self				:				List[Any]     )      ->       Optional[int]:




     """simple docstring"""
     return {
         "size": self.size,
         "do_resize_and_center_crop": self.do_resize_and_center_crop,
         "crop_pct": self.crop_pct,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






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






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				PoolFormerImageProcessor if is_vision_available() else None



  def     lowerCAmelCase__     (   self				:				Optional[int]     )      ->       Optional[Any]:




     """simple docstring"""
     snake_case_    			=    PoolFormerImageProcessingTester(self     )



  @property
  def     lowerCAmelCase__     (   self				:				str     )      ->       int:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



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




     """simple docstring"""
     snake_case_    			=    self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(UpperCAmelCase_			, "do_resize_and_center_crop"     )     )
     self.assertTrue(hasattr(UpperCAmelCase_			, "size"     )     )
     self.assertTrue(hasattr(UpperCAmelCase_			, "crop_pct"     )     )
     self.assertTrue(hasattr(UpperCAmelCase_			, "do_normalize"     )     )
     self.assertTrue(hasattr(UpperCAmelCase_			, "image_mean"     )     )
     self.assertTrue(hasattr(UpperCAmelCase_			, "image_std"     )     )



  def     lowerCAmelCase__     (   self				:				Any     )      ->       List[str]:




     """simple docstring"""
     snake_case_    			=    self.image_processing_class.from_dict(self.image_processor_dict     )
     self.assertEqual(image_processor.size			, {"shortest_edge": 3_0}     )
     self.assertEqual(image_processor.crop_size			, {"height": 3_0, "width": 3_0}     )

     snake_case_    			=    self.image_processing_class.from_dict(self.image_processor_dict			, size=4_2			, crop_size=8_4     )
     self.assertEqual(image_processor.size			, {"shortest_edge": 4_2}     )
     self.assertEqual(image_processor.crop_size			, {"height": 8_4, "width": 8_4}     )



  def     lowerCAmelCase__     (   self				:				int     )      ->       Optional[Any]:




     """simple docstring"""
     pass



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




     """simple docstring"""
     snake_case_    			=    self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     snake_case_    			=    prepare_image_inputs(self.image_processor_tester			, equal_resolution=UpperCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase_			, Image.Image     )

     # Test not batched input
     snake_case_    			=    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
     snake_case_    			=    image_processing(UpperCAmelCase_			, 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				:				Optional[Any]     )      ->       Dict:




     """simple docstring"""
     snake_case_    			=    self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     snake_case_    			=    prepare_image_inputs(self.image_processor_tester			, equal_resolution=UpperCAmelCase_			, numpify=UpperCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase_			, np.ndarray     )

     # Test not batched input
     snake_case_    			=    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
     snake_case_    			=    image_processing(UpperCAmelCase_			, 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				:				Union[str, Any]     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     snake_case_    			=    prepare_image_inputs(self.image_processor_tester			, equal_resolution=UpperCAmelCase_			, torchify=UpperCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase_			, torch.Tensor     )

     # Test not batched input
     snake_case_    			=    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
     snake_case_    			=    image_processing(UpperCAmelCase_			, 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

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def      lowerCAmelCase_ (       __a	,    __a	,    __a = 10**-10		)     ->		float:







  """simple docstring"""





  lowerCamelCase__:      str			  =a
  while True:
    lowerCamelCase__:      Optional[Any]			  =Decimal(__a		) - (
        Decimal(eval(__a		)		) / Decimal(eval(str(diff(__a		)		)		)		)  # noqa: S307
    )
    # This number dictates the accuracy of the answer
    if abs(eval(__a		)		) < precision:  # noqa: S307
      return float(__a		)


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
     # Find root of polynomial
     print(f'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
     # Find Square Root of 5
     print(f'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
     # Exponential Roots
     print(f'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')

