


"""simple docstring"""







import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

	if is_vision_available():
		from transformers import MaskaFormerImageProcessor

if is_vision_available():
	from PIL import Image




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       int    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Any=2    ,    UpperCAmelCase__    :       int=True    ,    UpperCAmelCase__    :       Union[str, Any]=False    ,    UpperCAmelCase__    :       Tuple=1_0    ,    UpperCAmelCase__    :       Optional[int]=3    ,    UpperCAmelCase__    :       Union[str, Any]=3_2 * 8    ,    UpperCAmelCase__    :       Dict=3_2 * 8    ,    UpperCAmelCase__    :       int=4    ,    UpperCAmelCase__    :       Optional[Any]=6_4    ,    ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       parent
						__SCREAMING_SNAKE_CASE		     =       batch_size
						__SCREAMING_SNAKE_CASE		     =       is_training
						__SCREAMING_SNAKE_CASE		     =       use_auxiliary_loss
						__SCREAMING_SNAKE_CASE		     =       num_queries
						__SCREAMING_SNAKE_CASE		     =       num_channels
						__SCREAMING_SNAKE_CASE		     =       min_size
						__SCREAMING_SNAKE_CASE		     =       max_size
						__SCREAMING_SNAKE_CASE		     =       num_labels
						__SCREAMING_SNAKE_CASE		     =       hidden_dim
						__SCREAMING_SNAKE_CASE		     =       hidden_dim




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]     ).to(
						    lowerCamelCase_     )

						__SCREAMING_SNAKE_CASE		     =       torch.ones([self.batch_size, self.min_size, self.max_size]    ,    device=lowerCamelCase_     )

						__SCREAMING_SNAKE_CASE		     =       (
						    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,    device=lowerCamelCase_     ) > 0.5
						).float()
						__SCREAMING_SNAKE_CASE		     =       (torch.rand((self.batch_size, self.num_labels)    ,    device=lowerCamelCase_     ) > 0.5).long()

						__SCREAMING_SNAKE_CASE		     =       self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       MaskaFormerConfig(
						    hidden_size=self.hidden_dim    ,    )
						__SCREAMING_SNAKE_CASE		     =       self.num_queries
						__SCREAMING_SNAKE_CASE		     =       self.num_labels

						__SCREAMING_SNAKE_CASE		     =       [1, 1, 1, 1]
						__SCREAMING_SNAKE_CASE		     =       self.num_channels

						__SCREAMING_SNAKE_CASE		     =       6_4
						__SCREAMING_SNAKE_CASE		     =       1_2_8
						__SCREAMING_SNAKE_CASE		     =       self.hidden_dim
						__SCREAMING_SNAKE_CASE		     =       self.hidden_dim
						__SCREAMING_SNAKE_CASE		     =       self.hidden_dim
						return config




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.prepare_config_and_inputs()
						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       output.encoder_hidden_states
						__SCREAMING_SNAKE_CASE		     =       output.pixel_decoder_hidden_states
						__SCREAMING_SNAKE_CASE		     =       output.transformer_decoder_hidden_states

						self.parent.assertTrue(len(lowerCamelCase_     )    ,    len(config.backbone_config.depths     )     )
						self.parent.assertTrue(len(lowerCamelCase_     )    ,    len(config.backbone_config.depths     )     )
						self.parent.assertTrue(len(lowerCamelCase_     )    ,    config.decoder_layers     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       Optional[Any]=False     ) ->			Tuple:
						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       MaskaFormerModel(config=lowerCamelCase_     )
											model.to(lowerCamelCase_     )
											model.eval()

											__SCREAMING_SNAKE_CASE		     =       model(pixel_values=lowerCamelCase_    ,    pixel_mask=lowerCamelCase_     )
											__SCREAMING_SNAKE_CASE		     =       model(lowerCamelCase_    ,    output_hidden_states=lowerCamelCase_     )

						self.parent.assertEqual(
						    output.transformer_decoder_last_hidden_state.shape    ,    (self.batch_size, self.num_queries, self.hidden_dim)    ,    )
						# let's ensure the other two hidden state exists
						self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None     )
						self.parent.assertTrue(output.encoder_last_hidden_state is not None     )

						if output_hidden_states:
											self.check_output_hidden_state(lowerCamelCase_    ,    lowerCamelCase_     )




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       Optional[Any]    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       str     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       MaskaFormerForUniversalSegmentation(config=lowerCamelCase_     )
						model.to(lowerCamelCase_     )
						model.eval()

						def comm_check_on_output(UpperCAmelCase__    :       Dict     ):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None     )
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None     )
											self.parent.assertTrue(result.encoder_last_hidden_state is not None     )
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape    ,    (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)    ,    )
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape    ,    (self.batch_size, self.num_queries, self.num_labels + 1)     )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(pixel_values=lowerCamelCase_    ,    pixel_mask=lowerCamelCase_     )
											__SCREAMING_SNAKE_CASE		     =       model(lowerCamelCase_     )

											comm_check_on_output(lowerCamelCase_     )

											__SCREAMING_SNAKE_CASE		     =       model(
											    pixel_values=lowerCamelCase_    ,    pixel_mask=lowerCamelCase_    ,    mask_labels=lowerCamelCase_    ,    class_labels=lowerCamelCase_     )

						comm_check_on_output(lowerCamelCase_     )

						self.parent.assertTrue(result.loss is not None     )
						self.parent.assertEqual(result.loss.shape    ,    torch.Size([1]     )     )




@require_torch
class 				UpperCamelCase_			(				a__    ,						a__    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Tuple					      =    (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
	snake_case__       :						Any					      =    {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

	snake_case__       :						Union[str, Any]					      =    False
	snake_case__       :						str					      =    False
	snake_case__       :						List[Any]					      =    False
	snake_case__       :						Dict					      =    False




	def 						UpperCAmelCase_   (				self    :       str     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       MaskaFormerModelTester(self     )
						__SCREAMING_SNAKE_CASE		     =       ConfigTester(self    ,    config_class=lowerCamelCase_    ,    has_text_modality=lowerCamelCase_     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			List[str]:
						self.config_tester.run_common_tests()




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_    ,    **lowerCamelCase_    ,    output_hidden_states=lowerCamelCase_     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase_     )




	@unittest.skip(reason="Mask2Former does not use inputs_embeds"     )
	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Optional[Any]:
						pass




	@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"     )
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Optional[int]:
						pass




	@unittest.skip(reason="Mask2Former is not a generative model"     )
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Any:
						pass




	@unittest.skip(reason="Mask2Former does not use token embeddings"     )
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						pass




	@require_torch_multi_gpu
	@unittest.skip(
	    reason="Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`"     )
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			List[str]:
						pass




	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Dict:
						pass




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						__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(lowerCamelCase_     )
											__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]    ,    lowerCamelCase_     )




	@slow
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Optional[Any]:
						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											__SCREAMING_SNAKE_CASE		     =       MaskaFormerModel.from_pretrained(lowerCamelCase_     )
											self.assertIsNotNone(lowerCamelCase_     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       (self.model_tester.min_size,) * 2
						__SCREAMING_SNAKE_CASE		     =       {
						    "pixel_values": torch.randn((2, 3, *size)    ,    device=lowerCamelCase_     ),
						    "mask_labels": torch.randn((2, 1_0, *size)    ,    device=lowerCamelCase_     ),
						    "class_labels": torch.zeros(2    ,    1_0    ,    device=lowerCamelCase_     ).long(),
						}
						__SCREAMING_SNAKE_CASE		     =       self.model_tester.get_config()

						__SCREAMING_SNAKE_CASE		     =       MaskaFormerForUniversalSegmentation(lowerCamelCase_     ).to(lowerCamelCase_     )
						__SCREAMING_SNAKE_CASE		     =       model(**lowerCamelCase_     )
						self.assertTrue(outputs.loss is not None     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_    ,    **lowerCamelCase_    ,    output_hidden_states=lowerCamelCase_     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Dict:
						__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(lowerCamelCase_     ).to(lowerCamelCase_     )
											__SCREAMING_SNAKE_CASE		     =       model(**lowerCamelCase_    ,    output_attentions=lowerCamelCase_     )
											self.assertTrue(outputs.attentions is not None     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Dict:
						if not self.model_tester.is_training:
											return

						__SCREAMING_SNAKE_CASE		     =       self.all_model_classes[1]
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs()

						__SCREAMING_SNAKE_CASE		     =       model_class(lowerCamelCase_     )
						model.to(lowerCamelCase_     )
						model.train()

						__SCREAMING_SNAKE_CASE		     =       model(lowerCamelCase_    ,    mask_labels=lowerCamelCase_    ,    class_labels=lowerCamelCase_     ).loss
						loss.backward()




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.all_model_classes[1]
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs()
						__SCREAMING_SNAKE_CASE		     =       True
						__SCREAMING_SNAKE_CASE		     =       True

						__SCREAMING_SNAKE_CASE		     =       model_class(lowerCamelCase_     ).to(lowerCamelCase_     )
						model.train()

						__SCREAMING_SNAKE_CASE		     =       model(lowerCamelCase_    ,    mask_labels=lowerCamelCase_    ,    class_labels=lowerCamelCase_     )

						__SCREAMING_SNAKE_CASE		     =       outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						__SCREAMING_SNAKE_CASE		     =       outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()

						__SCREAMING_SNAKE_CASE		     =       outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

						__SCREAMING_SNAKE_CASE		     =       outputs.attentions[0]
						attentions.retain_grad()

						outputs.loss.backward(retain_graph=lowerCamelCase_     )

						self.assertIsNotNone(encoder_hidden_states.grad     )
						self.assertIsNotNone(pixel_decoder_hidden_states.grad     )
						self.assertIsNotNone(transformer_decoder_hidden_states.grad     )
						self.assertIsNotNone(attentions.grad     )


a__       :							List[str]        =  1E-4




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					return image




@require_vision
@slow
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	@cached_property
	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						return "facebook/mask2former-swin-small-coco-instance"




	@cached_property
	def 						UpperCAmelCase_   (				self    :       int     ) ->			List[Any]:
						return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints     ) if is_vision_available() else None




	def 						UpperCAmelCase_   (				self    :       str     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       MaskaFormerModel.from_pretrained(self.model_checkpoints     ).to(lowerCamelCase_     )
						__SCREAMING_SNAKE_CASE		     =       self.default_image_processor
						__SCREAMING_SNAKE_CASE		     =       prepare_img()
						__SCREAMING_SNAKE_CASE		     =       image_processor(lowerCamelCase_    ,    return_tensors="pt"     ).to(lowerCamelCase_     )
						__SCREAMING_SNAKE_CASE		     =       inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0     )
						# check size
						self.assertEqual(lowerCamelCase_    ,    (1, 3, 3_8_4, 3_8_4)     )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(**lowerCamelCase_     )

						__SCREAMING_SNAKE_CASE		     =       torch.tensor(
						    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]     ).to(lowerCamelCase_     )
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,    lowerCamelCase_    ,    atol=lowerCamelCase_     )     )

						__SCREAMING_SNAKE_CASE		     =       torch.tensor(
						    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]     ).to(lowerCamelCase_     )
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,    lowerCamelCase_    ,    atol=lowerCamelCase_     )     )

						__SCREAMING_SNAKE_CASE		     =       torch.tensor(
						    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]     ).to(lowerCamelCase_     )
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,    lowerCamelCase_    ,    atol=lowerCamelCase_     )     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(lowerCamelCase_     ).eval()
						__SCREAMING_SNAKE_CASE		     =       self.default_image_processor
						__SCREAMING_SNAKE_CASE		     =       prepare_img()
						__SCREAMING_SNAKE_CASE		     =       image_processor(lowerCamelCase_    ,    return_tensors="pt"     ).to(lowerCamelCase_     )
						__SCREAMING_SNAKE_CASE		     =       inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0     )
						# check size
						self.assertEqual(lowerCamelCase_    ,    (1, 3, 3_8_4, 3_8_4)     )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(**lowerCamelCase_     )
						# masks_queries_logits
						__SCREAMING_SNAKE_CASE		     =       outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape    ,    (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     )
						__SCREAMING_SNAKE_CASE		     =       [
						    [-8.7_839, -9.0_056, -8.8_121],
						    [-7.4_104, -7.0_313, -6.5_401],
						    [-6.6_105, -6.3_427, -6.4_675],
						]
						__SCREAMING_SNAKE_CASE		     =       torch.tensor(lowerCamelCase_     ).to(lowerCamelCase_     )
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,    lowerCamelCase_    ,    atol=lowerCamelCase_     )     )
						# class_queries_logits
						__SCREAMING_SNAKE_CASE		     =       outputs.class_queries_logits
						self.assertEqual(class_queries_logits.shape    ,    (1, model.config.num_queries, model.config.num_labels + 1)     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor(
						    [
						        [1.8_324, -8.0_835, -4.1_922],
						        [0.8_450, -9.0_050, -3.6_053],
						        [0.3_045, -7.7_293, -3.0_275],
						    ]     ).to(lowerCamelCase_     )
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,    lowerCamelCase_    ,    atol=lowerCamelCase_     )     )




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(lowerCamelCase_     ).eval()
						__SCREAMING_SNAKE_CASE		     =       self.default_image_processor

						__SCREAMING_SNAKE_CASE		     =       image_processor(
						    [np.zeros((3, 8_0_0, 1_3_3_3)     ), np.zeros((3, 8_0_0, 1_3_3_3)     )]    ,    segmentation_maps=[np.zeros((3_8_4, 3_8_4)     ).astype(np.floataa     ), np.zeros((3_8_4, 3_8_4)     ).astype(np.floataa     )]    ,    return_tensors="pt"    ,    )

						__SCREAMING_SNAKE_CASE		     =       inputs["pixel_values"].to(lowerCamelCase_     )
						__SCREAMING_SNAKE_CASE		     =       [el.to(lowerCamelCase_     ) for el in inputs["mask_labels"]]
						__SCREAMING_SNAKE_CASE		     =       [el.to(lowerCamelCase_     ) for el in inputs["class_labels"]]

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(**lowerCamelCase_     )

						self.assertTrue(outputs.loss is not None     )




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


a__       :							Dict        =  logging.get_logger(__name__)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       str    ,    *UpperCAmelCase__    :       List[Any]    ,    **UpperCAmelCase__    :       Tuple     ) ->			None:
						warnings.warn(
						    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use VideoMAEImageProcessor instead."    ,    UpperCAmelCase__    ,    )
						super().__init__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )
