





'''simple docstring'''
import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


a__							:							List[str]			=namedtuple('''ExampleNamedTuple''', '''a b c''')

class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 def 	_lowerCamelCase (	self	:							Dict     ):
        __UpperCamelCase	=  torch.randn(5							,     2     )
        __UpperCamelCase	=  torch.device('cuda'     ) if torch.cuda.is_available() else torch.device('cpu'     )

        __UpperCamelCase	=  send_to_device(__A							,     __A     )
        self.assertTrue(torch.equal(resulta.cpu()							,     __A     )     )

        __UpperCamelCase	=  send_to_device((tensor, [tensor, tensor], 1)							,     __A     )
        self.assertIsInstance(__A							,     __A     )
        self.assertTrue(torch.equal(resulta[0].cpu()							,     __A     )     )
        self.assertIsInstance(resulta[1]							,     __A     )
        self.assertTrue(torch.equal(resulta[1][0].cpu()							,     __A     )     )
        self.assertTrue(torch.equal(resulta[1][1].cpu()							,     __A     )     )
        self.assertEqual(resulta[2]							,     1     )

        __UpperCamelCase	=  send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}							,     __A     )
        self.assertIsInstance(__A							,     __A     )
        self.assertTrue(torch.equal(resulta['a'].cpu()							,     __A     )     )
        self.assertIsInstance(resulta['b']							,     __A     )
        self.assertTrue(torch.equal(resulta['b'][0].cpu()							,     __A     )     )
        self.assertTrue(torch.equal(resulta['b'][1].cpu()							,     __A     )     )
        self.assertEqual(resulta['c']							,     1     )

        __UpperCamelCase	=  send_to_device(ExampleNamedTuple(a=__A							,     b=[tensor, tensor]							,     c=1     )							,     __A     )
        self.assertIsInstance(__A							,     __A     )
        self.assertTrue(torch.equal(resulta.a.cpu()							,     __A     )     )
        self.assertIsInstance(resulta.b							,     __A     )
        self.assertTrue(torch.equal(resulta.b[0].cpu()							,     __A     )     )
        self.assertTrue(torch.equal(resulta.b[1].cpu()							,     __A     )     )
        self.assertEqual(resulta.c							,     1     )

        __UpperCamelCase	=  send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}     )							,     __A     )
        self.assertIsInstance(__A							,     __A     )
        self.assertTrue(torch.equal(resulta['a'].cpu()							,     __A     )     )
        self.assertIsInstance(resulta['b']							,     __A     )
        self.assertTrue(torch.equal(resulta['b'][0].cpu()							,     __A     )     )
        self.assertTrue(torch.equal(resulta['b'][1].cpu()							,     __A     )     )
        self.assertEqual(resulta['c']							,     1     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        with self.assertRaises(__A     ) as cm:
               __UpperCamelCase	=  recursively_apply(torch.tensor							,     (torch.tensor(1     ), 1)							,     error_on_other_type=__A     )
        self.assertEqual(
            str(cm.exception     )							,     'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'							,     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  torch.tensor([1, 2, 3, 4, 5]     )
        self.assertEqual(listify(__A     )							,     [1, 2, 3, 4, 5]     )

        __UpperCamelCase	=  torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]     )
        self.assertEqual(listify(__A     )							,     [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]     )

        __UpperCamelCase	=  torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]     )
        self.assertEqual(
            listify(__A     )							,     [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        with patch_environment(aa=1							,     BB=2     ):
               self.assertEqual(os.environ.get('AA'     )							,     '1'     )
               self.assertEqual(os.environ.get('BB'     )							,     '2'     )

        self.assertNotIn('AA'							,     os.environ     )
        self.assertNotIn('BB'							,     os.environ     )






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  RegressionModel()
        __UpperCamelCase	=  model.forward
        __UpperCamelCase	=  convert_outputs_to_fpaa(model.forward     )
        __UpperCamelCase	=  extract_model_from_parallel(__A							,     keep_fpaa_wrapper=__A     )
        __UpperCamelCase	=  pickle.dumps(__A     )






 @require_cuda
 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  RegressionModel()
        __UpperCamelCase	=  model.forward
        __UpperCamelCase	=  torch.cuda.amp.autocast(dtype=torch.floataa     )(model.forward     )
        __UpperCamelCase	=  convert_outputs_to_fpaa(model.forward     )
        __UpperCamelCase	=  extract_model_from_parallel(__A							,     keep_fpaa_wrapper=__A     )
        __UpperCamelCase	=  pickle.dumps(__A     )






 @require_cuda
 @require_torch_min_version(version='2.0'     )
 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  RegressionModel()
        __UpperCamelCase	=  model.forward
        __UpperCamelCase	=  torch.cuda.amp.autocast(dtype=torch.floataa     )(model.forward     )
        __UpperCamelCase	=  convert_outputs_to_fpaa(model.forward     )
        __UpperCamelCase	=  torch.compile(model.forward							,     backend='inductor'     )
        __UpperCamelCase	=  torch.randn(4							,     1_0     ).cuda()
        __UpperCamelCase	=  model(__A     )






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  RegressionModel()
        # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
        __UpperCamelCase	=  torch.nn.parallel.DataParallel(__A     )
        __UpperCamelCase	=  extract_model_from_parallel(__A     )

        self.assertEqual(__A							,     __A     )






 @require_torch_min_version(version='2.0'     )
 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  RegressionModel()
        __UpperCamelCase	=  torch.compile(__A     )

        # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
        __UpperCamelCase	=  torch.nn.parallel.DataParallel(__A     )
        __UpperCamelCase	=  torch.compile(__A     )
        __UpperCamelCase	=  extract_model_from_parallel(__A     )

        self.assertEqual(compiled_model._orig_mod							,     compiled_model_unwrapped._orig_mod     )






 def 	_lowerCamelCase (	self	:							Dict     ):
        self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]     )]     )							,     torch.device('cpu'     )     )
        self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]     )}     )							,     torch.device('cpu'     )     )
        self.assertIsNone(find_device([1, 'a']     )     )







'''simple docstring'''
import numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __UpperCamelCase	=  np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       __UpperCamelCase	=  image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]					):
              for y in range(image.shape[0]					):
                     __UpperCamelCase	=  (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     __UpperCamelCase	=  int(summation == 5					)
       return output


# kernel to be applied
a__							:							Dict			=np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
    # read original image
    a__							:							Any			=np.array(Image.open(r'''..\image_data\lena.jpg'''))
    # Apply erosion operation to a binary image
    a__							:							Union[str, Any]			=erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    a__							:							Tuple			=Image.fromarray(output).convert('''RGB''')
    pil_img.save('''result_erosion.png''')
