






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Tuple						 =       self.config_class(**self.inputs_dict				)
								self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,				"hidden_sizes"				)				)
								self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,				"num_attention_heads"				)				)




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=13		,				_SCREAMING_SNAKE_CASE=64		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=[128, 256, 384]		,				_SCREAMING_SNAKE_CASE=[4, 6, 8]		,				_SCREAMING_SNAKE_CASE=[2, 3, 4]		,				_SCREAMING_SNAKE_CASE=[16, 16, 16]		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=[2, 2, 2]		,				_SCREAMING_SNAKE_CASE=[2, 2, 2]		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=2		,				) ->				Union[str, Any]:
								snake_case_				:							int						 =       parent
								snake_case_				:							Optional[int]						 =       batch_size
								snake_case_				:							List[str]						 =       image_size
								snake_case_				:							Optional[Any]						 =       num_channels
								snake_case_				:							Optional[int]						 =       kernel_size
								snake_case_				:							Dict						 =       stride
								snake_case_				:							List[str]						 =       padding
								snake_case_				:							Any						 =       hidden_sizes
								snake_case_				:							Optional[Any]						 =       num_attention_heads
								snake_case_				:							List[str]						 =       depths
								snake_case_				:							Optional[int]						 =       key_dim
								snake_case_				:							Optional[int]						 =       drop_path_rate
								snake_case_				:							int						 =       patch_size
								snake_case_				:							Union[str, Any]						 =       attention_ratio
								snake_case_				:							List[Any]						 =       mlp_ratio
								snake_case_				:							int						 =       initializer_range
								snake_case_				:							int						 =       [
								    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]
								snake_case_				:							Optional[int]						 =       is_training
								snake_case_				:							Union[str, Any]						 =       use_labels
								snake_case_				:							Optional[int]						 =       num_labels
								snake_case_				:							Union[str, Any]						 =       initializer_range






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Dict						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								snake_case_				:							Any						 =       None
								if self.use_labels:
										snake_case_				:							str						 =       ids_tensor([self.batch_size]		,				self.num_labels				)

								snake_case_				:							Dict						 =       self.get_config()
								return config, pixel_values, labels






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								return LevitConfig(
								    image_size=self.image_size		,				num_channels=self.num_channels		,				kernel_size=self.kernel_size		,				stride=self.stride		,				padding=self.padding		,				patch_size=self.patch_size		,				hidden_sizes=self.hidden_sizes		,				num_attention_heads=self.num_attention_heads		,				depths=self.depths		,				key_dim=self.key_dim		,				drop_path_rate=self.drop_path_rate		,				mlp_ratio=self.mlp_ratio		,				attention_ratio=self.attention_ratio		,				initializer_range=self.initializer_range		,				down_ops=self.down_ops		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Optional[Any]						 =       LevitModel(config=_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Dict						 =       (self.image_size, self.image_size)
								snake_case_ ,       snake_case_				:							Union[str, Any]						 =       image_size[0], image_size[1]
								for _ in range(4				):
										snake_case_				:							Dict						 =       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
										snake_case_				:							List[Any]						 =       floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,				(self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Any						 =       self.num_labels
								snake_case_				:							Optional[Any]						 =       LevitForImageClassification(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							str						 =       self.prepare_config_and_inputs()
								snake_case_ ,       snake_case_ ,       snake_case_				:							int						 =       config_and_inputs
								snake_case_				:							Dict						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




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


						'''simple docstring'''




						A     :   List[Any]						=					(
						    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
						    if is_torch_available()
						    else ()
						)
						A     :   Optional[int]						=					(
						    {
						        'feature-extraction': LevitModel,
						        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

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






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[Any]						 =       LevitModelTester(self				)
								snake_case_				:							Union[str, Any]						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE		,				hidden_size=37				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								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       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								return






						@unittest.skip(reason="Levit does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								pass






						@unittest.skip(reason="Levit does not support input and output embeddings"				)
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								pass






						@unittest.skip(reason="Levit does not output attentions"				)
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								pass






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

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

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






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								def check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Optional[Any]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										model.to(_SCREAMING_SNAKE_CASE				)
										model.eval()

										with torch.no_grad():
												snake_case_				:							Dict						 =       model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				)

										snake_case_				:							Optional[Any]						 =       outputs.hidden_states

										snake_case_				:							Optional[int]						 =       len(self.model_tester.depths				) + 1
										self.assertEqual(len(_SCREAMING_SNAKE_CASE				)		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							str						 =       (self.model_tester.image_size, self.model_tester.image_size)
										snake_case_ ,       snake_case_				:							List[Any]						 =       image_size[0], image_size[1]
										for _ in range(4				):
												snake_case_				:							Dict						 =       floor(
												    (
												        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
												        / self.model_tester.stride
												    )
												    + 1				)
												snake_case_				:							Union[str, Any]						 =       floor(
												    (
												        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
												        / self.model_tester.stride
												    )
												    + 1				)
										# verify the first hidden states (first block)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]				)		,				[
										        height * width,
										        self.model_tester.hidden_sizes[0],
										    ]		,				)

								snake_case_ ,       snake_case_				:							Optional[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							Any						 =       True
										check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										snake_case_				:							str						 =       True

										check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False				) ->				int:
								snake_case_				:							int						 =       super()._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)

								if return_labels:
										if model_class.__name__ == "LevitForImageClassificationWithTeacher":
												del inputs_dict["labels"]

								return inputs_dict






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






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Tuple						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								if not self.model_tester.is_training:
										return

								snake_case_ ,       snake_case_				:							Union[str, Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				:							Dict						 =       True

								for model_class in self.all_model_classes:
										# LevitForImageClassificationWithTeacher supports inference-only
										if (
										    model_class in get_values(_SCREAMING_SNAKE_CASE				)
										    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
										):
												continue
										snake_case_				:							Union[str, Any]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										model.to(_SCREAMING_SNAKE_CASE				)
										model.train()
										snake_case_				:							int						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Any						 =       model(**_SCREAMING_SNAKE_CASE				).loss
										loss.backward()






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_ ,       snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
										return

								snake_case_				:							int						 =       False
								snake_case_				:							Dict						 =       True

								for model_class in self.all_model_classes:
										if model_class in get_values(_SCREAMING_SNAKE_CASE				) or not model_class.supports_gradient_checkpointing:
												continue
										# LevitForImageClassificationWithTeacher supports inference-only
										if model_class.__name__ == "LevitForImageClassificationWithTeacher":
												continue
										snake_case_				:							List[Any]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										model.gradient_checkpointing_enable()
										model.to(_SCREAMING_SNAKE_CASE				)
										model.train()
										snake_case_				:							Optional[int]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Dict						 =       model(**_SCREAMING_SNAKE_CASE				).loss
										loss.backward()






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

								snake_case_				:							Optional[int]						 =       [
								    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
								    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
								    {"title": "regression", "num_labels": 1, "dtype": torch.float},
								]

								for model_class in self.all_model_classes:
										if (
										    model_class
										    not in [
										        *get_values(_SCREAMING_SNAKE_CASE				),
										    ]
										    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
										):
												continue

										for problem_type in problem_types:
												with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''				):
														snake_case_				:							str						 =       problem_type["title"]
														snake_case_				:							Any						 =       problem_type["num_labels"]

														snake_case_				:							Optional[int]						 =       model_class(_SCREAMING_SNAKE_CASE				)
														model.to(_SCREAMING_SNAKE_CASE				)
														model.train()

														snake_case_				:							Any						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)

														if problem_type["num_labels"] > 1:
																snake_case_				:							str						 =       inputs["labels"].unsqueeze(1				).repeat(1		,				problem_type["num_labels"]				)

														snake_case_				:							str						 =       inputs["labels"].to(problem_type["dtype"]				)

														# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
														# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
														# they have the same size." which is a symptom something in wrong for the regression problem.
														# See https://github.com/huggingface/transformers/issues/11780
														with warnings.catch_warnings(record=_SCREAMING_SNAKE_CASE				) as warning_list:
																snake_case_				:							Dict						 =       model(**_SCREAMING_SNAKE_CASE				).loss
														for w in warning_list:
																if "Using a target size that is different to the input size" in str(w.message				):
																		raise ValueError(
																		    f'''Something is going wrong in the regression problem: intercepted {w.message}'''				)

														loss.backward()






						@slow
						def       _lowerCAmelCase       (	self				) ->				Dict:
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_				:							Any						 =       LevitModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




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


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				Dict:
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Tuple						 =       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
								    _SCREAMING_SNAKE_CASE				)

								snake_case_				:							Union[str, Any]						 =       self.default_image_processor
								snake_case_				:							Dict						 =       prepare_img()
								snake_case_				:							Tuple						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)

								# forward pass
								with torch.no_grad():
										snake_case_				:							Optional[Any]						 =       model(**_SCREAMING_SNAKE_CASE				)

								# verify the logits
								snake_case_				:							Tuple						 =       torch.Size((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Dict						 =       torch.tensor([1.0448, -0.3745, -1.8317]				).to(_SCREAMING_SNAKE_CASE				)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=1e-4				)				)








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
