





import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor




class 					UpperCamelCase_    :


							def __init__(							self      :int							,			__A      :Union[str, Any]							,			__A      :int=13							,			__A      :Tuple=32							,			__A      :Any=2							,			__A      :Tuple=3							,			__A      :int=16							,			__A      :int=[1, 2, 1]							,			__A      :str=[2, 2, 4]							,			__A      :Dict=2							,			__A      :List[Any]=2.0							,			__A      :Tuple=True							,			__A      :Optional[Any]=0.0							,			__A      :int=0.0							,			__A      :Dict=0.1							,			__A      :Tuple="gelu"							,			__A      :str=False							,			__A      :List[Any]=True							,			__A      :Optional[int]=0.0_2							,			__A      :Optional[int]=1E-5							,			__A      :Dict=True							,			__A      :Tuple=None							,			__A      :List[str]=True							,			__A      :Union[str, Any]=10							,			__A      :Union[str, Any]=8							,			__A      :Dict=["stage1", "stage2"]							,			__A      :List[str]=[1, 2]							,			)				->				List[Any]:



									"""simple docstring"""



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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

									SCREAMING_SNAKE_CASE__								= self.get_config()

									return config, pixel_values, labels


							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									return SwinConfig(
									    image_size=self.image_size							,			patch_size=self.patch_size							,			num_channels=self.num_channels							,			embed_dim=self.embed_dim							,			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        _snake_case					(							self      :int							,			__A      :Optional[int]							,			__A      :str							,			__A      :Optional[Any]						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinModel(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)

									SCREAMING_SNAKE_CASE__								= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
									SCREAMING_SNAKE_CASE__								= 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        _snake_case					(							self      :Union[str, Any]							,			__A      :Union[str, Any]							,			__A      :int							,			__A      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinBackbone(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps						)							,			len(config.out_features						)						)
									self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,			[self.batch_size, model.channels[0], 16, 16]						)

									# verify channels
									self.parent.assertEqual(len(model.channels						)							,			len(config.out_features						)						)

									# verify backbone works with out_features=None
									SCREAMING_SNAKE_CASE__								= None
									SCREAMING_SNAKE_CASE__								= SwinBackbone(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= 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, model.channels[-1], 4, 4]						)

									# verify channels
									self.parent.assertEqual(len(model.channels						)							,			1						)


							def        _snake_case					(							self      :int							,			__A      :Any							,			__A      :Any							,			__A      :int						)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinForMaskedImageModeling(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(
									    result.logits.shape							,			(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

									# test greyscale images
									SCREAMING_SNAKE_CASE__								= 1
									SCREAMING_SNAKE_CASE__								= SwinForMaskedImageModeling(__A						)
									model.to(__A						)
									model.eval()

									SCREAMING_SNAKE_CASE__								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, 1, self.image_size, self.image_size)						)


							def        _snake_case					(							self      :str							,			__A      :Optional[int]							,			__A      :Tuple							,			__A      :Any						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.type_sequence_label_size
									SCREAMING_SNAKE_CASE__								= SwinForImageClassification(__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A							,			labels=__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									SCREAMING_SNAKE_CASE__								= 1
									SCREAMING_SNAKE_CASE__								= SwinForImageClassification(__A						)
									model.to(__A						)
									model.eval()

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


							def        _snake_case					(							self      :Tuple						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)		,  (
									    SCREAMING_SNAKE_CASE__
									)		,  (
									    SCREAMING_SNAKE_CASE__
									)		,  
									)								= config_and_inputs
									SCREAMING_SNAKE_CASE__								= {"""pixel_values""": pixel_values}
									return config, inputs_dict




@require_torch
class 					UpperCamelCase_    (       UpperCamelCase__			,					UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   (
							    (
							        SwinModel,
							        SwinBackbone,
							        SwinForImageClassification,
							        SwinForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase_          =   (
							    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase_          =   True

							lowerCamelCase_          =   False
							lowerCamelCase_          =   False
							lowerCamelCase_          =   False


							def        _snake_case					(							self      :str						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinModelTester(self						)
									SCREAMING_SNAKE_CASE__								= ConfigTester(self							,			config_class=__A							,			embed_dim=37						)


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



									"""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        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									return


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A						)


							@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""						)
							def        _snake_case					(							self      :List[Any]						)				->				str:



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :str						)				->				Tuple:



									"""simple docstring"""



									super().test_training_gradient_checkpointing()


							def        _snake_case					(							self      :int						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__A						)


							@unittest.skip(reason="""Swin does not use inputs_embeds"""						)
							def        _snake_case					(							self      :Optional[int]						)				->				Optional[Any]:



									"""simple docstring"""



									pass


							@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""						)
							def        _snake_case					(							self      :List[str]						)				->				List[str]:



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :Any						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								= model_class(__A						)
											self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)						)
											SCREAMING_SNAKE_CASE__								= model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__A							,			nn.Linear						)						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE__								= ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,			__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__								= True

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								= True
											SCREAMING_SNAKE_CASE__								= False
											SCREAMING_SNAKE_CASE__								= True
											SCREAMING_SNAKE_CASE__								= model_class(__A						)
											model.to(__A						)
											model.eval()
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__								= model(**self._prepare_for_class(__A							,			__A						)						)
											SCREAMING_SNAKE_CASE__								= outputs.attentions
											SCREAMING_SNAKE_CASE__								= len(self.model_tester.depths						)
											self.assertEqual(len(__A						)							,			__A						)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											SCREAMING_SNAKE_CASE__								= True
											SCREAMING_SNAKE_CASE__								= config.window_size**2
											SCREAMING_SNAKE_CASE__								= model_class(__A						)
											model.to(__A						)
											model.eval()
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__								= model(**self._prepare_for_class(__A							,			__A						)						)
											SCREAMING_SNAKE_CASE__								= outputs.attentions
											self.assertEqual(len(__A						)							,			__A						)

											self.assertListEqual(
											    list(attentions[0].shape[-3:]						)							,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,			)
											SCREAMING_SNAKE_CASE__								= len(__A						)

											# Check attention is always last and order is fine
											SCREAMING_SNAKE_CASE__								= True
											SCREAMING_SNAKE_CASE__								= True
											SCREAMING_SNAKE_CASE__								= model_class(__A						)
											model.to(__A						)
											model.eval()
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__								= model(**self._prepare_for_class(__A							,			__A						)						)

											# also another +1 for reshaped_hidden_states
											SCREAMING_SNAKE_CASE__								= 1 if model_class.__name__ == """SwinBackbone""" else 2
											self.assertEqual(out_len + added_hidden_states							,			len(__A						)						)

											SCREAMING_SNAKE_CASE__								= outputs.attentions

											self.assertEqual(len(__A						)							,			__A						)

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]						)							,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,			)


							def        _snake_case					(							self      :Tuple							,			__A      :Any							,			__A      :Union[str, Any]							,			__A      :str							,			__A      :Optional[int]						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= model_class(__A						)
									model.to(__A						)
									model.eval()

									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								= model(**self._prepare_for_class(__A							,			__A						)						)

									SCREAMING_SNAKE_CASE__								= outputs.hidden_states

									SCREAMING_SNAKE_CASE__								= getattr(
									    self.model_tester							,			"""expected_num_hidden_layers"""							,			len(self.model_tester.depths						) + 1						)
									self.assertEqual(len(__A						)							,			__A						)

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

									SCREAMING_SNAKE_CASE__								= (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]							,			)

									if not model_class.__name__ == "SwinBackbone":
											SCREAMING_SNAKE_CASE__								= outputs.reshaped_hidden_states
											self.assertEqual(len(__A						)							,			__A						)

											SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= reshaped_hidden_states[0].shape
											SCREAMING_SNAKE_CASE__								= (
											    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        _snake_case					(							self      :List[str]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE__								= (
									    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:
											SCREAMING_SNAKE_CASE__								= 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"]
											SCREAMING_SNAKE_CASE__								= True

											self.check_hidden_states_output(__A							,			__A							,			__A							,			__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__								= 3

									SCREAMING_SNAKE_CASE__								= (
									    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)
									)
									SCREAMING_SNAKE_CASE__								= (
									    config.patch_size
									    if isinstance(config.patch_size							,			collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

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

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								= 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"]
											SCREAMING_SNAKE_CASE__								= True
											self.check_hidden_states_output(__A							,			__A							,			__A							,			(padded_height, padded_width)						)


							@slow
							def        _snake_case					(							self      :Any						)				->				List[Any]:



									"""simple docstring"""



									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__								= SwinModel.from_pretrained(__A						)
											self.assertIsNotNone(__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE__								= _config_zero_init(__A						)
									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								= 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        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									return (
									    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						)
									    if is_vision_available()
									    else None
									)


							@slow
							def        _snake_case					(							self      :Dict						)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						).to(__A						)
									SCREAMING_SNAKE_CASE__								= self.default_image_processor

									SCREAMING_SNAKE_CASE__								= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
									SCREAMING_SNAKE_CASE__								= image_processor(images=__A							,			return_tensors="""pt"""						).to(__A						)

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								= model(**__A						)

									# verify the logits
									SCREAMING_SNAKE_CASE__								= torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape							,			__A						)
									SCREAMING_SNAKE_CASE__								= torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]						).to(__A						)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]							,			__A							,			atol=1E-4						)						)




@require_torch
class 					UpperCamelCase_    (       unittest.TestCase			,					UpperCamelCase__      ):
							lowerCamelCase_          =   (SwinBackbone,) if is_torch_available() else ()
							lowerCamelCase_          =   SwinConfig


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinModelTester(self						)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase							=     logging.get_logger(__name__)

_lowerCamelCase							=     {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   "videomae"


							def __init__(							self      :Dict							,			__A      :List[str]=224							,			__A      :List[Any]=16							,			__A      :int=3							,			__A      :Optional[Any]=16							,			__A      :Optional[int]=2							,			__A      :Optional[Any]=768							,			__A      :List[str]=12							,			__A      :Optional[Any]=12							,			__A      :List[str]=3072							,			__A      :Tuple="gelu"							,			__A      :Optional[int]=0.0							,			__A      :Union[str, Any]=0.0							,			__A      :int=0.0_2							,			__A      :Union[str, Any]=1E-12							,			__A      :Optional[Any]=True							,			__A      :Union[str, Any]=True							,			__A      :Optional[int]=6							,			__A      :Tuple=384							,			__A      :str=4							,			__A      :List[str]=1536							,			__A      :Dict=True							,			**__A      :int							,			)				->				Optional[int]:



									"""simple docstring"""



									super().__init__(**__A						)

									SCREAMING_SNAKE_CASE__								= image_size
									SCREAMING_SNAKE_CASE__								= patch_size
									SCREAMING_SNAKE_CASE__								= num_channels
									SCREAMING_SNAKE_CASE__								= num_frames
									SCREAMING_SNAKE_CASE__								= tubelet_size

									SCREAMING_SNAKE_CASE__								= hidden_size
									SCREAMING_SNAKE_CASE__								= num_hidden_layers
									SCREAMING_SNAKE_CASE__								= num_attention_heads
									SCREAMING_SNAKE_CASE__								= intermediate_size
									SCREAMING_SNAKE_CASE__								= hidden_act
									SCREAMING_SNAKE_CASE__								= hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								= initializer_range
									SCREAMING_SNAKE_CASE__								= layer_norm_eps
									SCREAMING_SNAKE_CASE__								= qkv_bias
									SCREAMING_SNAKE_CASE__								= use_mean_pooling

									SCREAMING_SNAKE_CASE__								= decoder_num_attention_heads
									SCREAMING_SNAKE_CASE__								= decoder_hidden_size
									SCREAMING_SNAKE_CASE__								= decoder_num_hidden_layers
									SCREAMING_SNAKE_CASE__								= decoder_intermediate_size
									SCREAMING_SNAKE_CASE__								= norm_pix_loss