





'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 							__lowerCAmelCase	(       __magic_name__		,		__magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       AutoencoderKL
       _snake_case      :	str									=       'sample'
       _snake_case      :	Union[str, Any]									=       1e-2





       @property
       def snake_case__		(				self  :						Union[str, Any]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			4
            _UpperCamelCase							       =			3
            _UpperCamelCase							       =			(32, 32)

            _UpperCamelCase							       =			floats_tensor((batch_size, num_channels) + sizes	).to(lowerCAmelCase__	)

            return {"sample": image}





       @property
       def snake_case__		(				self  :						str	)       ->	Optional[Any]:

            '''simple docstring'''




            return (3, 32, 32)





       @property
       def snake_case__		(				self  :						int	)       ->	Tuple:

            '''simple docstring'''




            return (3, 32, 32)





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

            '''simple docstring'''




            _UpperCamelCase							       =			{
                '''block_out_channels''': [32, 64],
                '''in_channels''': 3,
                '''out_channels''': 3,
                '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                '''latent_channels''': 4,
            }
            _UpperCamelCase							       =			self.dummy_input
            return init_dict, inputs_dict





       def snake_case__		(				self  :						str	)       ->	Optional[Any]:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            pass





       @unittest.skipIf(torch_device == '''mps'''			,					'''Gradient checkpointing skipped on MPS'''	)
       def snake_case__		(				self  :						Any	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase							       =			self.model_class(**lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)

            assert not model.is_gradient_checkpointing and model.training

            _UpperCamelCase							       =			model(**lowerCAmelCase__	).sample
            # run the backwards pass on the model. For backwards pass, for simplicity purpose,
            # we won't calculate the loss and rather backprop on out.sum()
            model.zero_grad()

            _UpperCamelCase							       =			torch.randn_like(lowerCAmelCase__	)
            _UpperCamelCase							       =			(out - labels).mean()
            loss.backward()

            # re-instantiate the model now enabling gradient checkpointing
            _UpperCamelCase							       =			self.model_class(**lowerCAmelCase__	)
            # clone model
            model_a.load_state_dict(model.state_dict()	)
            model_a.to(lowerCAmelCase__	)
            model_a.enable_gradient_checkpointing()

            assert model_a.is_gradient_checkpointing and model_a.training

            _UpperCamelCase							       =			model_a(**lowerCAmelCase__	).sample
            # run the backwards pass on the model. For backwards pass, for simplicity purpose,
            # we won't calculate the loss and rather backprop on out.sum()
            model_a.zero_grad()
            _UpperCamelCase							       =			(out_a - labels).mean()
            loss_a.backward()

            # compare the output and parameters gradients
            self.assertTrue((loss - loss_a).abs() < 1e-5	)
            _UpperCamelCase							       =			dict(model.named_parameters()	)
            _UpperCamelCase							       =			dict(model_a.named_parameters()	)
            for name, param in named_params.items():
                 self.assertTrue(torch_all_close(param.grad.data			,					named_params_a[name].grad.data			,					atol=5e-5	)	)





       def snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''			,					output_loading_info=lowerCAmelCase__	)
            self.assertIsNotNone(lowerCAmelCase__	)
            self.assertEqual(len(loading_info['''missing_keys''']	)			,					0	)

            model.to(lowerCAmelCase__	)
            _UpperCamelCase							       =			model(**self.dummy_input	)

            assert image is not None, "Make sure output is not None"







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

            '''simple docstring'''




            _UpperCamelCase							       =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''	)
            _UpperCamelCase							       =			model.to(lowerCAmelCase__	)
            model.eval()

            if torch_device == "mps":
                 _UpperCamelCase							       =			torch.manual_seed(0	)
            else:
                 _UpperCamelCase							       =			torch.Generator(device=lowerCAmelCase__	).manual_seed(0	)

            _UpperCamelCase							       =			torch.randn(
                1			,					model.config.in_channels			,					model.config.sample_size			,					model.config.sample_size			,					generator=torch.manual_seed(0	)			,					)
            _UpperCamelCase							       =			image.to(lowerCAmelCase__	)
            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					sample_posterior=lowerCAmelCase__			,					generator=lowerCAmelCase__	).sample

            _UpperCamelCase							       =			output[0, -1, -3:, -3:].flatten().cpu()

            # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
            # the expected output slices are not the same for CPU and GPU.
            if torch_device == "mps":
                 _UpperCamelCase							       =			torch.tensor(
                     [
                         -4.0_0_7_8e-0_1,
                         -3.8_3_2_3e-0_4,
                         -1.2_6_8_1e-0_1,
                         -1.1_4_6_2e-0_1,
                         2.0_0_9_5e-0_1,
                         1.0_8_9_3e-0_1,
                         -8.8_2_4_7e-0_2,
                         -3.0_3_6_1e-0_1,
                         -9.8_6_4_4e-0_3,
                     ]	)
            elif torch_device == "cpu":
                 _UpperCamelCase							       =			torch.tensor(
                     [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]	)
            else:
                 _UpperCamelCase							       =			torch.tensor(
                     [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]	)

            self.assertTrue(torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					rtol=1e-2	)	)

@slow
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def snake_case__		(				self  :						str			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	str:

            '''simple docstring'''




            return f"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase__	) for s in shape]	)}.npy"""





       def snake_case__		(				self  :						List[str]	)       ->	Tuple:

            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Any=0			,					lowerCAmelCase__  :						int=(4, 3, 512, 512)			,					lowerCAmelCase__  :						Tuple=False	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			torch.floataa if fpaa else torch.floataa
            _UpperCamelCase							       =			torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCAmelCase__			,					lowerCAmelCase__	)	)	).to(lowerCAmelCase__	).to(lowerCAmelCase__	)
            return image





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						List[Any]="CompVis/stable-diffusion-v1-4"			,					lowerCAmelCase__  :						Dict=False	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			'''fp16''' if fpaa else None
            _UpperCamelCase							       =			torch.floataa if fpaa else torch.floataa

            _UpperCamelCase							       =			AutoencoderKL.from_pretrained(
                lowerCAmelCase__			,					subfolder='''vae'''			,					torch_dtype=lowerCAmelCase__			,					revision=lowerCAmelCase__			,					)
            model.to(lowerCAmelCase__	).eval()

            return model





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Tuple=0	)       ->	List[Any]:

            '''simple docstring'''




            if torch_device == "mps":
                 return torch.manual_seed(lowerCAmelCase__	)
            return torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
               [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
               # fmt: on
           ]	)
       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						int	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_generator(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					sample_posterior=lowerCAmelCase__	).sample

            assert sample.shape == image.shape

            _UpperCamelCase							       =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=3e-3	)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
               [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
               # fmt: on
           ]	)
       @require_torch_gpu
       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model(fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_generator(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					sample_posterior=lowerCAmelCase__	).sample

            assert sample.shape == image.shape

            _UpperCamelCase							       =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-2	)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
               [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
               # fmt: on
           ]	)
       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						str	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model(lowerCAmelCase__	).sample

            assert sample.shape == image.shape

            _UpperCamelCase							       =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=3e-3	)





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
               [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
               # fmt: on
           ]	)
       @require_torch_gpu
       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						int	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            _UpperCamelCase							       =			sample[-1, -2:, :2, -2:].flatten().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-3	)





       @parameterized.expand(
           [
               # fmt: off
               [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
               [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
               # fmt: on
           ]	)
       @require_torch_gpu
       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model(fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)			,					fpaa=lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            _UpperCamelCase							       =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=5e-3	)





       @parameterized.expand([(13,), (16,), (27,)]	)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()			,					reason='''xformers is not required when using PyTorch 2.0.'''	)
       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						Optional[int]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model(fpaa=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)			,					fpaa=lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            model.enable_xformers_memory_efficient_attention()
            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-1	)





       @parameterized.expand([(13,), (16,), (37,)]	)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()			,					reason='''xformers is not required when using PyTorch 2.0.'''	)
       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__			,					shape=(3, 4, 64, 64)	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            model.enable_xformers_memory_efficient_attention()
            with torch.no_grad():
                 _UpperCamelCase							       =			model.decode(lowerCAmelCase__	).sample

            assert list(sample.shape	) == [3, 3, 512, 512]

            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=1e-2	)







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
               [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
               # fmt: on
           ]	)
       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_sd_vae_model()
            _UpperCamelCase							       =			self.get_sd_image(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.get_generator(lowerCAmelCase__	)

            with torch.no_grad():
                 _UpperCamelCase							       =			model.encode(lowerCAmelCase__	).latent_dist
                 _UpperCamelCase							       =			dist.sample(generator=lowerCAmelCase__	)

            assert list(sample.shape	) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

            _UpperCamelCase							       =			sample[0, -1, -3:, -3:].flatten().cpu()
            _UpperCamelCase							       =			torch.tensor(lowerCAmelCase__	)

            _UpperCamelCase							       =			3e-3 if torch_device != '''mps''' else 1e-2
            assert torch_all_close(lowerCAmelCase__			,					lowerCAmelCase__			,					atol=lowerCAmelCase__	)










'''simple docstring'''

from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching







def    a__							(							)						->						Tuple:





     """simple docstring"""

     import os as original_os
     from os import path as original_path
     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join

     _UpperCamelCase							       =			'''__test_patch_submodule_mock__'''
     with patch_submodule(_test_patching,   '''os.path.join''',   lowercase ):
          # Every way to access os.path.join must be patched, and the rest must stay untouched

          # check os.path.join
          assert isinstance(_test_patching.os,   _PatchedModuleObj )
          assert isinstance(_test_patching.os.path,   _PatchedModuleObj )
          assert _test_patching.os.path.join is mock

          # check path.join
          assert isinstance(_test_patching.path,   _PatchedModuleObj )
          assert _test_patching.path.join is mock

          # check join
          assert _test_patching.join is mock

          # check that the other attributes are untouched
          assert _test_patching.os.rename is original_rename
          assert _test_patching.path.dirname is original_dirname
          assert _test_patching.os.path.dirname is original_dirname

          # Even renamed modules or objects must be patched

          # check renamed_os.path.join
          assert isinstance(_test_patching.renamed_os,   _PatchedModuleObj )
          assert isinstance(_test_patching.renamed_os.path,   _PatchedModuleObj )
          assert _test_patching.renamed_os.path.join is mock

          # check renamed_path.join
          assert isinstance(_test_patching.renamed_path,   _PatchedModuleObj )
          assert _test_patching.renamed_path.join is mock

          # check renamed_join
          assert _test_patching.renamed_join is mock

          # check that the other attributes are untouched
          assert _test_patching.renamed_os.rename is original_rename
          assert _test_patching.renamed_path.dirname is original_dirname
          assert _test_patching.renamed_os.path.dirname is original_dirname

     # check that everthing is back to normal when the patch is over

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join







def    a__							(							)						->						Tuple:





     """simple docstring"""

     assert _test_patching.open is open

     _UpperCamelCase							       =			'''__test_patch_submodule_builtin_mock__'''
     # _test_patching has "open" in its globals
     assert _test_patching.open is open
     with patch_submodule(_test_patching,   '''open''',   lowercase ):
          assert _test_patching.open is mock

     # check that everthing is back to normal when the patch is over

     assert _test_patching.open is open







def    a__							(							)						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			'''__test_patch_submodule_missing_mock__'''
     with patch_submodule(_test_patching,   '''pandas.read_csv''',   lowercase ):
          pass







def    a__							(							)						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			'''__test_patch_submodule_missing_builtin_mock__'''
     # _test_patching doesn't have "len" in its globals
     assert getattr(_test_patching,   '''len''',   lowercase ) is None
     with patch_submodule(_test_patching,   '''len''',   lowercase ):
          assert _test_patching.len is mock
     assert _test_patching.len is len







def    a__							(							)						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			'''__test_patch_submodule_start_and_stop_mock__'''
     _UpperCamelCase							       =			patch_submodule(_test_patching,   '''open''',   lowercase )
     assert _test_patching.open is open
     patch.start()
     assert _test_patching.open is mock
     patch.stop()
     assert _test_patching.open is open







def    a__							(							)						->						List[str]:





     """simple docstring"""

     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     _UpperCamelCase							       =			'''__test_patch_submodule_successive_join__'''
     _UpperCamelCase							       =			'''__test_patch_submodule_successive_dirname__'''
     _UpperCamelCase							       =			'''__test_patch_submodule_successive_rename__'''
     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename

     with patch_submodule(_test_patching,   '''os.path.join''',   lowercase ):
          with patch_submodule(_test_patching,   '''os.rename''',   lowercase ):
               with patch_submodule(_test_patching,   '''os.path.dirname''',   lowercase ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

    # try another order
     with patch_submodule(_test_patching,   '''os.rename''',   lowercase ):
          with patch_submodule(_test_patching,   '''os.path.join''',   lowercase ):
               with patch_submodule(_test_patching,   '''os.path.dirname''',   lowercase ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename







def    a__							(							)						->						str:





     """simple docstring"""

     _UpperCamelCase							       =			'''__test_patch_submodule_doesnt_exist_mock__'''
     with patch_submodule(_test_patching,   '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''',   lowercase ):
          pass
     with patch_submodule(_test_patching,   '''os.__attribute_that_doesn_exist__''',   lowercase ):
          pass



