
"""simple docstring"""





import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    FocalNetBackbone,
				    FocalNetForImageClassification,
				    FocalNetForMaskedImageModeling,
				    FocalNetModel,
				)
				from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=32							,						__a=2							,						__a=3							,						__a=16							,						__a=[32, 64, 1_28]							,						__a=[1, 2, 1]							,						__a=[2, 2, 4]							,						__a=2							,						__a=2.0							,						__a=True							,						__a=0.0							,						__a=0.0							,						__a=0.1							,						__a="gelu"							,						__a=False							,						__a=True							,						__a=0.02							,						__a=1e-5							,						__a=True							,						__a=None							,						__a=True							,						__a=10							,						__a=8							,						__a=["stage1", "stage2"]							,						__a=[1, 2]							,						)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    embed_dim
							_UpperCamelCase									=    hidden_sizes
							_UpperCamelCase									=    depths
							_UpperCamelCase									=    num_heads
							_UpperCamelCase									=    window_size
							_UpperCamelCase									=    mlp_ratio
							_UpperCamelCase									=    qkv_bias
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    drop_path_rate
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    use_absolute_embeddings
							_UpperCamelCase									=    patch_norm
							_UpperCamelCase									=    layer_norm_eps
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    encoder_stride
							_UpperCamelCase									=    out_features
							_UpperCamelCase									=    out_indices



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							return FocalNetConfig(
							    image_size=self.image_size							,						patch_size=self.patch_size							,						num_channels=self.num_channels							,						embed_dim=self.embed_dim							,						hidden_sizes=self.hidden_sizes							,						depths=self.depths							,						num_heads=self.num_heads							,						window_size=self.window_size							,						mlp_ratio=self.mlp_ratio							,						qkv_bias=self.qkv_bias							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						drop_path_rate=self.drop_path_rate							,						hidden_act=self.hidden_act							,						use_absolute_embeddings=self.use_absolute_embeddings							,						path_norm=self.patch_norm							,						layer_norm_eps=self.layer_norm_eps							,						initializer_range=self.initializer_range							,						encoder_stride=self.encoder_stride							,						out_features=self.out_features							,						out_indices=self.out_indices							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    FocalNetModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)

							_UpperCamelCase									=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
							_UpperCamelCase									=    int(config.embed_dim * 2 ** (len(config.depths) - 1))

							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, expected_seq_len, expected_dim))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    FocalNetBackbone(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps)							,						len(config.out_features))
							self.parent.assertListEqual(list(result.feature_maps[0].shape)							,						[self.batch_size, self.image_size, 8, 8])

							# verify channels
							self.parent.assertEqual(len(model.channels)							,						len(config.out_features))
							self.parent.assertListEqual(model.channels							,						config.hidden_sizes[:-1])

							# verify backbone works with out_features=None
							_UpperCamelCase									=    None
							_UpperCamelCase									=    FocalNetBackbone(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps)							,						1)
							self.parent.assertListEqual(list(result.feature_maps[0].shape)							,						[self.batch_size, self.image_size * 2, 4, 4])

							# verify channels
							self.parent.assertEqual(len(model.channels)							,						1)
							self.parent.assertListEqual(model.channels							,						[config.hidden_sizes[-1]])



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    FocalNetForMaskedImageModeling(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(
							    result.reconstruction.shape							,						(self.batch_size, self.num_channels, self.image_size, self.image_size))

							# test greyscale images
							_UpperCamelCase									=    1
							_UpperCamelCase									=    FocalNetForMaskedImageModeling(__a)
							model.to(__a)
							model.eval()

							_UpperCamelCase									=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.reconstruction.shape							,						(self.batch_size, 1, self.image_size, self.image_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.type_sequence_label_size
							_UpperCamelCase									=    FocalNetForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))

							# test greyscale images
							_UpperCamelCase									=    1
							_UpperCamelCase									=    FocalNetForImageClassification(__a)
							model.to(__a)
							model.eval()

							_UpperCamelCase									=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))




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






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()

							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        FocalNetModel,
		        FocalNetForImageClassification,
		        FocalNetForMaskedImageModeling,
		        FocalNetBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__         = (
		    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__         = False

		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    FocalNetModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						embed_dim=37							,						has_text_modality=__a)



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






							'''simple docstring'''
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							return



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



		@unittest.skip(reason='''FocalNet does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							pass



		@unittest.skip(reason='''FocalNet does not use feedforward chunking''')
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												_UpperCamelCase									=    model_class(__a)
												self.assertIsInstance(model.get_input_embeddings()							,						(nn.Module))
												_UpperCamelCase									=    model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a							,						nn.Linear))



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												_UpperCamelCase									=    model_class(__a)
												_UpperCamelCase									=    inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_UpperCamelCase									=    [*signature.parameters.keys()]

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    model_class(__a)
							model.to(__a)
							model.eval()

							with torch.no_grad():
												_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))

							_UpperCamelCase									=    outputs.hidden_states

							_UpperCamelCase									=    getattr(
							    self.model_tester							,						'''expected_num_hidden_layers'''							,						len(self.model_tester.depths) + 1)
							self.assertEqual(len(__a)							,						__a)

							# FocalNet has a different seq_length
							_UpperCamelCase									=    (
							    config.patch_size
							    if isinstance(config.patch_size							,						collections.abc.Iterable)
							    else (config.patch_size, config.patch_size)
							)

							_UpperCamelCase									=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:])							,						[num_patches, self.model_tester.embed_dim]							,						)

							_UpperCamelCase									=    outputs.reshaped_hidden_states
							self.assertEqual(len(__a)							,						__a)

							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    reshaped_hidden_states[0].shape
							_UpperCamelCase									=    (
							    reshaped_hidden_states[0].view(__a							,						__a							,						height * width).permute(0							,						2							,						1)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:])							,						[num_patches, self.model_tester.embed_dim]							,						)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size							,						collections.abc.Iterable)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes[:-1]:
												_UpperCamelCase									=    True
												self.check_hidden_states_output(__a							,						__a							,						__a							,						__a)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_UpperCamelCase									=    True

												self.check_hidden_states_output(__a							,						__a							,						__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase									=    3

							_UpperCamelCase									=    (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size							,						collections.abc.Iterable)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							_UpperCamelCase									=    (
							    config.patch_size
							    if isinstance(config.patch_size							,						collections.abc.Iterable)
							    else (config.patch_size, config.patch_size)
							)

							_UpperCamelCase									=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							_UpperCamelCase									=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes[:-1]:
												_UpperCamelCase									=    True
												self.check_hidden_states_output(__a							,						__a							,						__a							,						(padded_height, padded_width))

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_UpperCamelCase									=    True
												self.check_hidden_states_output(__a							,						__a							,						__a							,						(padded_height, padded_width))



		@slow
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    FocalNetModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




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






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												for name, param in model.named_parameters():
																	if "embeddings" not in name and param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,						[0.0, 1.0]							,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,						)






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



		@cached_property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							# TODO update organization
							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''') if is_vision_available() else None




		@slow
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''').to(__a)
							_UpperCamelCase									=    self.default_image_processor

							_UpperCamelCase									=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

							# verify the logits
							_UpperCamelCase									=    torch.Size((1, 10_00))
							self.assertEqual(outputs.logits.shape							,						__a)
							_UpperCamelCase									=    torch.tensor([0.2166, -0.4368, 0.2191]).to(__a)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]							,						__a							,						atol=1e-4))
							self.assertTrue(outputs.logits.argmax(dim=-1).item()							,						2_81)






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (FocalNetBackbone,) if is_torch_available() else ()
		lowercase__         = FocalNetConfig

		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    FocalNetModelTester(self)








"""simple docstring"""





from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def  lowerCamelCase__						(     )				->						List[str]:


					"""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''',	__snake_case      ):
										# 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  lowerCamelCase__						(     )				->						List[str]:


					"""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''',	__snake_case      ):
										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  lowerCamelCase__						(     )				->						Union[str, Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Dict:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Optional[int]:


					"""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''',	__snake_case      ):
										with patch_submodule(_test_patching,	'''os.rename''',	__snake_case      ):
															with patch_submodule(_test_patching,	'''os.path.dirname''',	__snake_case      ):
																				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''',	__snake_case      ):
										with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
															with patch_submodule(_test_patching,	'''os.path.dirname''',	__snake_case      ):
																				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  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





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






