






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		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=32 * 8		,				_SCREAMING_SNAKE_CASE=32 * 8		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=64		,				) ->				Tuple:
								snake_case_				:							List[Any]						 =       parent
								snake_case_				:							Union[str, Any]						 =       batch_size
								snake_case_				:							Union[str, Any]						 =       is_training
								snake_case_				:							Union[str, Any]						 =       use_auxiliary_loss
								snake_case_				:							str						 =       num_queries
								snake_case_				:							Any						 =       num_channels
								snake_case_				:							List[Any]						 =       min_size
								snake_case_				:							List[Any]						 =       max_size
								snake_case_				:							Optional[Any]						 =       num_labels
								snake_case_				:							Dict						 =       hidden_dim
								snake_case_				:							Tuple						 =       hidden_dim






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Union[str, Any]						 =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
								    _SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       torch.ones([self.batch_size, self.min_size, self.max_size]		,				device=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       (
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,				device=_SCREAMING_SNAKE_CASE				) > 0.5
								).float()
								snake_case_				:							Optional[Any]						 =       (torch.rand((self.batch_size, self.num_labels)		,				device=_SCREAMING_SNAKE_CASE				) > 0.5).long()

								snake_case_				:							Optional[Any]						 =       self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							List[Any]						 =       MaskaFormerConfig(
								    hidden_size=self.hidden_dim		,				)
								snake_case_				:							str						 =       self.num_queries
								snake_case_				:							List[str]						 =       self.num_labels

								snake_case_				:							Dict						 =       [1, 1, 1, 1]
								snake_case_				:							str						 =       self.num_channels

								snake_case_				:							Union[str, Any]						 =       64
								snake_case_				:							Optional[int]						 =       128
								snake_case_				:							Dict						 =       self.hidden_dim
								snake_case_				:							Dict						 =       self.hidden_dim
								snake_case_				:							Optional[int]						 =       self.hidden_dim
								return config






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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Union[str, Any]						 =       output.encoder_hidden_states
								snake_case_				:							Any						 =       output.pixel_decoder_hidden_states
								snake_case_				:							Optional[Any]						 =       output.transformer_decoder_hidden_states

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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False				) ->				List[str]:
								with torch.no_grad():
										snake_case_				:							Any						 =       MaskaFormerModel(config=_SCREAMING_SNAKE_CASE				)
										model.to(_SCREAMING_SNAKE_CASE				)
										model.eval()

										snake_case_				:							Dict						 =       model(pixel_values=_SCREAMING_SNAKE_CASE		,				pixel_mask=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[str]						 =       model(_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE				)

								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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Tuple						 =       MaskaFormerForUniversalSegmentation(config=_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()

								def comm_check_on_output(_SCREAMING_SNAKE_CASE				):
										# 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():
										snake_case_				:							Union[str, Any]						 =       model(pixel_values=_SCREAMING_SNAKE_CASE		,				pixel_mask=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE				)

										comm_check_on_output(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Any						 =       model(
										    pixel_values=_SCREAMING_SNAKE_CASE		,				pixel_mask=_SCREAMING_SNAKE_CASE		,				mask_labels=_SCREAMING_SNAKE_CASE		,				class_labels=_SCREAMING_SNAKE_CASE				)

								comm_check_on_output(_SCREAMING_SNAKE_CASE				)

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




@require_torch
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Tuple						=					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
						A     :   Dict						=					{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

						A     :   Optional[Any]						=					False
						A     :   int						=					False
						A     :   Optional[int]						=					False
						A     :   Union[str, Any]						=					False






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							List[str]						 =       MaskaFormerModelTester(self				)
								snake_case_				:							Any						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								self.config_tester.run_common_tests()






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_ ,       snake_case_				:							str						 =       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Optional[int]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_SCREAMING_SNAKE_CASE				)






						@unittest.skip(reason="Mask2Former does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				int:
								pass






						@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"				)
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								pass






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






						@unittest.skip(reason="Mask2Former does not use token embeddings"				)
						def       _lowerCAmelCase       (	self				) ->				Dict:
								pass






						@require_torch_multi_gpu
						@unittest.skip(
						    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"				)
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								pass






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






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_ ,       snake_case_				:							str						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							List[str]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case_				:							str						 =       [*signature.parameters.keys()]

										snake_case_				:							Any						 =       ["pixel_values"]
										self.assertListEqual(arg_names[:1]		,				_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							str						 =       (self.model_tester.min_size,) * 2
								snake_case_				:							Dict						 =       {
								    "pixel_values": torch.randn((2, 3, *size)		,				device=_SCREAMING_SNAKE_CASE				),
								    "mask_labels": torch.randn((2, 10, *size)		,				device=_SCREAMING_SNAKE_CASE				),
								    "class_labels": torch.zeros(2		,				10		,				device=_SCREAMING_SNAKE_CASE				).long(),
								}
								snake_case_				:							Optional[int]						 =       self.model_tester.get_config()

								snake_case_				:							List[str]						 =       MaskaFormerForUniversalSegmentation(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       model(**_SCREAMING_SNAKE_CASE				)
								self.assertTrue(outputs.loss is not None				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_ ,       snake_case_				:							str						 =       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							Dict						 =       model_class(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       model(**_SCREAMING_SNAKE_CASE		,				output_attentions=_SCREAMING_SNAKE_CASE				)
										self.assertTrue(outputs.attentions is not None				)






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

								snake_case_				:							Dict						 =       self.all_model_classes[1]
								snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs()

								snake_case_				:							List[str]						 =       model_class(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.train()

								snake_case_				:							Any						 =       model(_SCREAMING_SNAKE_CASE		,				mask_labels=_SCREAMING_SNAKE_CASE		,				class_labels=_SCREAMING_SNAKE_CASE				).loss
								loss.backward()






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							Optional[int]						 =       self.all_model_classes[1]
								snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_				:							Tuple						 =       self.model_tester.prepare_config_and_inputs()
								snake_case_				:							Dict						 =       True
								snake_case_				:							List[str]						 =       True

								snake_case_				:							Tuple						 =       model_class(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
								model.train()

								snake_case_				:							int						 =       model(_SCREAMING_SNAKE_CASE		,				mask_labels=_SCREAMING_SNAKE_CASE		,				class_labels=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Tuple						 =       outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								snake_case_				:							Tuple						 =       outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()

								snake_case_				:							Union[str, Any]						 =       outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								snake_case_				:							Any						 =       outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=_SCREAMING_SNAKE_CASE				)

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




lowercase  :							Optional[Any]			  =    1e-4

def      lowerCAmelCase__	(    ):
		snake_case_				:							Optional[int]						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




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


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								return "facebook/mask2former-swin-small-coco-instance"






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






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							Any						 =       MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       self.default_image_processor
								snake_case_				:							List[str]						 =       prepare_img()
								snake_case_				:							List[str]						 =       image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       inputs["pixel_values"].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(_SCREAMING_SNAKE_CASE		,				(1, 3, 384, 384)				)

								with torch.no_grad():
										snake_case_				:							Tuple						 =       model(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       torch.tensor(
								    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]				).to(_SCREAMING_SNAKE_CASE				)
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=_SCREAMING_SNAKE_CASE				)				)

								snake_case_				:							Any						 =       torch.tensor(
								    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]				).to(_SCREAMING_SNAKE_CASE				)
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=_SCREAMING_SNAKE_CASE				)				)

								snake_case_				:							Union[str, Any]						 =       torch.tensor(
								    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]				).to(_SCREAMING_SNAKE_CASE				)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=_SCREAMING_SNAKE_CASE				)				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							Union[str, Any]						 =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(_SCREAMING_SNAKE_CASE				).eval()
								snake_case_				:							List[str]						 =       self.default_image_processor
								snake_case_				:							Tuple						 =       prepare_img()
								snake_case_				:							Tuple						 =       image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       inputs["pixel_values"].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(_SCREAMING_SNAKE_CASE		,				(1, 3, 384, 384)				)

								with torch.no_grad():
										snake_case_				:							Optional[int]						 =       model(**_SCREAMING_SNAKE_CASE				)
								# masks_queries_logits
								snake_case_				:							Any						 =       outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape		,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
								snake_case_				:							Union[str, Any]						 =       [
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								snake_case_				:							Dict						 =       torch.tensor(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=_SCREAMING_SNAKE_CASE				)				)
								# class_queries_logits
								snake_case_				:							Tuple						 =       outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape		,				(1, model.config.num_queries, model.config.num_labels + 1)				)
								snake_case_				:							Any						 =       torch.tensor(
								    [
								        [1.8324, -8.0835, -4.1922],
								        [0.8450, -9.0050, -3.6053],
								        [0.3045, -7.7293, -3.0275],
								    ]				).to(_SCREAMING_SNAKE_CASE				)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=_SCREAMING_SNAKE_CASE				)				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Any						 =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(_SCREAMING_SNAKE_CASE				).eval()
								snake_case_				:							Dict						 =       self.default_image_processor

								snake_case_				:							int						 =       image_processor(
								    [np.zeros((3, 800, 1333)				), np.zeros((3, 800, 1333)				)]		,				segmentation_maps=[np.zeros((384, 384)				).astype(np.floataa				), np.zeros((384, 384)				).astype(np.floataa				)]		,				return_tensors="pt"		,				)

								snake_case_				:							int						 =       inputs["pixel_values"].to(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       [el.to(_SCREAMING_SNAKE_CASE				) for el in inputs["mask_labels"]]
								snake_case_				:							str						 =       [el.to(_SCREAMING_SNAKE_CASE				) for el in inputs["class_labels"]]

								with torch.no_grad():
										snake_case_				:							List[Any]						 =       model(**_SCREAMING_SNAKE_CASE				)

								self.assertTrue(outputs.loss is not None				)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   List[Any]						=					'ViltImageProcessor'
						A     :   Optional[Any]						=					('BertTokenizer', 'BertTokenizerFast')






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							str						 =       None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."		,				_SCREAMING_SNAKE_CASE		,				)
										snake_case_				:							str						 =       kwargs.pop("feature_extractor"				)

								snake_case_				:							Tuple						 =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."				)
								if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."				)

								super().__init__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       self.image_processor






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchEncoding:
								snake_case_				:							List[Any]						 =       self.tokenizer(
								    text=_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,				return_special_tokens_mask=_SCREAMING_SNAKE_CASE		,				return_offsets_mapping=_SCREAMING_SNAKE_CASE		,				return_length=_SCREAMING_SNAKE_CASE		,				verbose=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								# add pixel_values + pixel_mask
								snake_case_				:							str						 =       self.image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE				)
								encoding.update(_SCREAMING_SNAKE_CASE				)

								return encoding






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.tokenizer.model_input_names
								snake_case_				:							Tuple						 =       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor_class






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor
