
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 GLPNImageProcessor




class 		a_						( unittest.TestCase	):







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Optional[int]=7     ,					__UpperCamelCase			:       List[str]=3     ,					__UpperCamelCase			:       Dict=18     ,					__UpperCamelCase			:       List[str]=30     ,					__UpperCamelCase			:       List[Any]=4_00     ,					__UpperCamelCase			:       Optional[int]=True     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Dict=True     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   num_channels
        _UpperCAmelCase          =   image_size
        _UpperCAmelCase          =   min_resolution
        _UpperCAmelCase          =   max_resolution
        _UpperCAmelCase          =   do_resize
        _UpperCAmelCase          =   size_divisor
        _UpperCAmelCase          =   do_rescale







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






        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }







@require_torch
@require_vision
class 		a_						( snake_case__     ,						unittest.TestCase	):
    a      :				List[Any]     =				GLPNImageProcessor if is_vision_available() else None







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






        '''simple docstring'''






        _UpperCAmelCase          =   GLPNImageProcessingTester(self     )







    @property
    def      _snake_case					(			self			:       Dict     )		->Dict:






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()







    def      _snake_case					(			self			:       Dict     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,					"""do_resize"""     )     )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,					"""size_divisor"""     )     )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,					"""resample"""     )     )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,					"""do_rescale"""     )     )







    def      _snake_case					(			self			:       Optional[int]     )		->str:






        '''simple docstring'''






        pass







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _UpperCAmelCase          =   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 (GLPNImageProcessor doesn't support batching)
        _UpperCAmelCase          =   image_processing(image_inputs[0]     ,					return_tensors="""pt"""     ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )







    def      _snake_case					(			self			:       str     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _UpperCAmelCase          =   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 (GLPNImageProcessor doesn't support batching)
        _UpperCAmelCase          =   image_processing(image_inputs[0]     ,					return_tensors="""pt"""     ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )







    def      _snake_case					(			self			:       Optional[int]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _UpperCAmelCase          =   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 (GLPNImageProcessor doesn't support batching)
        _UpperCAmelCase          =   image_processing(image_inputs[0]     ,					return_tensors="""pt"""     ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )

"""simple docstring"""



from collections.abc import Callable

import numpy as np


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  np.array:





    """simple docstring"""

    _UpperCAmelCase          =   int(np.ceil((x_end - xa) / step_size      )      )
    _UpperCAmelCase          =   np.zeros((n + 1,)      )
    _UpperCAmelCase          =   ya
    _UpperCAmelCase          =   xa

    for k in range(_A      ):
        _UpperCAmelCase          =   y[k] + step_size * ode_func(_A					,      y[k]      )
        _UpperCAmelCase          =   y[k] + (
            (step_size / 2) * (ode_func(_A					,      y[k]      ) + ode_func(x + step_size					,      _A      ))
        )
        x += step_size

    return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()