





"""simple docstring"""




import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor

class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=3_0 ,				snake_case_=4_0_0 ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=[0.5, 0.5, 0.5] ,				snake_case_=[0.5, 0.5, 0.5] ,				snake_case_=False ,				):

        """simple docstring"""

        A_  :	Dict    						=  size if size is not None else {'height': 3_0, 'width': 3_0}
        A_  :	Tuple    						=  parent
        A_  :	Any    						=  batch_size
        A_  :	Optional[int]    						=  num_channels
        A_  :	Any    						=  min_resolution
        A_  :	int    						=  max_resolution
        A_  :	List[Any]    						=  do_resize
        A_  :	List[str]    						=  size
        A_  :	List[Any]    						=  do_normalize
        A_  :	Optional[int]    						=  image_mean
        A_  :	Union[str, Any]    						=  image_std
        A_  :	int    						=  do_reduce_labels

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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





def 						UpperCAmelCase__		(					):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  load_dataset('hf-internal-testing/fixtures_ade20k'			, split='test' )

  A_  :	Tuple    						=  Image.open(dataset[0]['file'] )
  A_  :	List[str]    						=  Image.open(dataset[1]['file'] )

  return image, map





def 						UpperCAmelCase__		(					):






  """simple docstring"""





  A_  :	Optional[Any]    						=  load_dataset('hf-internal-testing/fixtures_ade20k'			, split='test' )

  A_  :	Optional[int]    						=  Image.open(dataset[0]['file'] )
  A_  :	Optional[Any]    						=  Image.open(dataset[1]['file'] )
  A_  :	List[Any]    						=  Image.open(dataset[2]['file'] )
  A_  :	List[Any]    						=  Image.open(dataset[3]['file'] )

  return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Dict        =       SegformerImageProcessor if is_vision_available() else None

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  SegformerImageProcessingTester(self  )

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case_ ,				'do_resize'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'size'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'do_normalize'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'image_mean'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'image_std'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'do_reduce_labels'  )  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size ,				{'height': 3_0, 'width': 3_0}  )
        self.assertEqual(image_processor.do_reduce_labels ,				snake_case_  )

        A_  :	Tuple    						=  self.image_processing_class.from_dict(self.image_processor_dict ,				size=4_2 ,				reduce_labels=snake_case_  )
        self.assertEqual(image_processor.size ,				{'height': 4_2, 'width': 4_2}  )
        self.assertEqual(image_processor.do_reduce_labels ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

        # Test not batched input
        A_  :	Union[str, 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
        A_  :	Any    						=  image_processing(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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        A_  :	Union[str, Any]    						=  prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				numpify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_ ,				np.ndarray  )

        # Test not batched input
        A_  :	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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)

        # Test batched
        A_  :	Any    						=  image_processing(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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        A_  :	List[str]    						=  prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				torchify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_ ,				torch.Tensor  )

        # Test not batched input
        A_  :	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
        A_  :	str    						=  image_processing(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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        A_  :	int    						=  prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				torchify=snake_case_  )
        A_  :	Tuple    						=  []
        for image in image_inputs:
          self.assertIsInstance(snake_case_ ,				torch.Tensor  )
          maps.append(torch.zeros(image.shape[-2:]  ).long()  )

        # Test not batched input
        A_  :	Optional[Any]    						=  image_processing(image_inputs[0] ,				maps[0] ,				return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(
            encoding['labels'].shape ,				(
                1,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(encoding['labels'].dtype ,				torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 2_5_5  )

        # Test batched
        A_  :	int    						=  image_processing(snake_case_ ,				snake_case_ ,				return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].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'],
            ) ,				)
        self.assertEqual(
            encoding['labels'].shape ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(encoding['labels'].dtype ,				torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 2_5_5  )

        # Test not batched input (PIL images)
        A_						,	A_  :	str    						=  prepare_semantic_single_inputs()

        A_  :	Any    						=  image_processing(snake_case_ ,				snake_case_ ,				return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(
            encoding['labels'].shape ,				(
                1,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(encoding['labels'].dtype ,				torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 2_5_5  )

        # Test batched input (PIL images)
        A_						,	A_  :	Tuple    						=  prepare_semantic_batch_inputs()

        A_  :	Union[str, Any]    						=  image_processing(snake_case_ ,				snake_case_ ,				return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,				(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(
            encoding['labels'].shape ,				(
                2,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)
        self.assertEqual(encoding['labels'].dtype ,				torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 2_5_5  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        A_						,	A_  :	Optional[Any]    						=  prepare_semantic_single_inputs()
        A_  :	Dict    						=  image_processing(snake_case_ ,				snake_case_ ,				return_tensors='pt'  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 1_5_0  )

        A_  :	Optional[Any]    						=  True
        A_  :	Optional[int]    						=  image_processing(snake_case_ ,				snake_case_ ,				return_tensors='pt'  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 2_5_5  )






"""simple docstring"""




import math as m





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=0.001 ):






  """simple docstring"""





  return (f(a + h ) - f(a - h )) / (2 * h)





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0			, _UpperCAmelCase=100			, _UpperCAmelCase=0.0_001			, _UpperCAmelCase=1E-6			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	Optional[int]    						=  xa  # set the initial guess
  A_  :	Any    						=  [a]
  A_  :	str    						=  abs(f(_UpperCAmelCase ) )
  A_  :	str    						=  lambda _UpperCAmelCase : calc_derivative(_UpperCAmelCase			, _UpperCAmelCase			, h=_UpperCAmelCase )  # noqa: E731  Derivative of f(x)
  for _ in range(_UpperCAmelCase ):
    if fa(_UpperCAmelCase ) == 0:
      raise ValueError('No converging solution found' )
    A_  :	Union[str, Any]    						=  a - f(_UpperCAmelCase ) / fa(_UpperCAmelCase )  # Calculate the next estimate
    if logsteps:
      steps.append(_UpperCAmelCase )
    if error < maxerror:
      break
  else:
    raise ValueError('Iteration limit reached, no converging solution found' )
  if logsteps:
    # If logstep is true, then log intermediate steps
    return a, error, steps
  return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   lowerCamelCase_					:						str      = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_					:						int      = newton_raphson(
       f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"solution = {{{solution:f}}}, error = {{{error:f}}}")