
"""simple docstring"""



import collections
import inspect
import unittest

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

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


if is_torch_available():
 import torch
 from torch import nn

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

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class 		a_						:







    def __init__(			self			:       int     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Any=13     ,					__UpperCamelCase			:       str=32     ,					__UpperCamelCase			:       Tuple=2     ,					__UpperCamelCase			:       Optional[Any]=3     ,					__UpperCamelCase			:       Optional[int]=16     ,					__UpperCamelCase			:       Union[str, Any]=[32, 64, 1_28]     ,					__UpperCamelCase			:       str=[1, 2, 1]     ,					__UpperCamelCase			:       Union[str, Any]=[2, 2, 4]     ,					__UpperCamelCase			:       List[Any]=2     ,					__UpperCamelCase			:       Dict=2.0     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Optional[int]=0.0     ,					__UpperCamelCase			:       Tuple=0.0     ,					__UpperCamelCase			:       Tuple=0.1     ,					__UpperCamelCase			:       Any="gelu"     ,					__UpperCamelCase			:       str=False     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       Dict=0.0_2     ,					__UpperCamelCase			:       Optional[int]=1e-5     ,					__UpperCamelCase			:       int=True     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Any=True     ,					__UpperCamelCase			:       Optional[Any]=10     ,					__UpperCamelCase			:       List[str]=8     ,					__UpperCamelCase			:       Tuple=["stage1", "stage2"]     ,					__UpperCamelCase			:       List[str]=[1, 2]     ,					)		->str:






        '''simple docstring'''






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







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






        '''simple docstring'''






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

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

        _UpperCAmelCase          =   self.get_config()

        return config, pixel_values, labels







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






        '''simple docstring'''






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







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )

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

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







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetBackbone(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )

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

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

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

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

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







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetForMaskedImageModeling(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        self.parent.assertEqual(
            result.reconstruction.shape     ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

        # test greyscale images
        _UpperCAmelCase          =   1
        _UpperCAmelCase          =   FocalNetForMaskedImageModeling(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()

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







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.type_sequence_label_size
        _UpperCAmelCase          =   FocalNetForImageClassification(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					labels=__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)     )

        # test greyscale images
        _UpperCAmelCase          =   1
        _UpperCAmelCase          =   FocalNetForImageClassification(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()

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







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.prepare_config_and_inputs()

        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   config_and_inputs
        _UpperCAmelCase          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				str     =				(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    a      :				int     =				(
        {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    a      :				Optional[Any]     =				False

    a      :				Dict     =				False
    a      :				List[Any]     =				False
    a      :				int     =				False
    a      :				Optional[Any]     =				False







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					embed_dim=37     ,					has_text_modality=__UpperCamelCase     )







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






        '''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     )		->List[Any]:






        '''simple docstring'''






        return







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase     )







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






        '''simple docstring'''






        pass







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






        '''simple docstring'''






        pass







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

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







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            _UpperCAmelCase          =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     ,					__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   model_class(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()

        with torch.no_grad():
            _UpperCAmelCase          =   model(**self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     )     )

        _UpperCAmelCase          =   outputs.hidden_states

        _UpperCAmelCase          =   getattr(
            self.model_tester     ,					"""expected_num_hidden_layers"""     ,					len(self.model_tester.depths     ) + 1     )
        self.assertEqual(len(__UpperCamelCase     )     ,					__UpperCamelCase     )

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

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

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

        _UpperCAmelCase          =   outputs.reshaped_hidden_states
        self.assertEqual(len(__UpperCamelCase     )     ,					__UpperCamelCase     )

        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   reshaped_hidden_states[0].shape
        _UpperCAmelCase          =   (
            reshaped_hidden_states[0].view(__UpperCamelCase     ,					__UpperCamelCase     ,					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			:       Tuple     )		->str:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

            self.check_hidden_states_output(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->str:






        '''simple docstring'''






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

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

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

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

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







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






        '''simple docstring'''






        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _UpperCAmelCase          =   FocalNetModel.from_pretrained(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase          =   _config_zero_init(__UpperCamelCase     )
        for model_class in self.all_model_classes:
            _UpperCAmelCase          =   model_class(config=__UpperCamelCase     )
            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 		a_						( unittest.TestCase	):







    @cached_property
    def      _snake_case					(			self			:       Dict     )		->Tuple:






        '''simple docstring'''






        return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""     ) if is_vision_available() else None







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   self.default_image_processor

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

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase          =   model(**__UpperCamelCase     )

        # verify the logits
        _UpperCAmelCase          =   torch.Size((1, 10_00)     )
        self.assertEqual(outputs.logits.shape     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]     ).to(__UpperCamelCase     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					__UpperCamelCase     ,					atol=1e-4     )     )
        self.assertTrue(outputs.logits.argmax(dim=-1     ).item()     ,					2_81     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(FocalNetBackbone,) if is_torch_available() else ()
    a      :				Tuple     =				FocalNetConfig

    a      :				Optional[Any]     =				False







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetModelTester(self     )

"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['image_processor', 'tokenizer']
    a      :				Optional[int]     =				'AutoImageProcessor'
    a      :				Any     =				'AutoTokenizer'







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       List[str]=None     ,					**__UpperCamelCase			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   None
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""     ,					__UpperCamelCase     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   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__(__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def __call__(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






        if self._in_target_context_manager:
            return self.current_processor(*__UpperCamelCase     ,					**__UpperCamelCase     )

        _UpperCAmelCase          =   kwargs.pop("""images"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""text"""     ,					__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            _UpperCAmelCase          =   args[0]
            _UpperCAmelCase          =   args[1:]

        if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""     )

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					*__UpperCamelCase     ,					**__UpperCamelCase     )
        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					**__UpperCamelCase     )

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            _UpperCAmelCase          =   encodings["""input_ids"""]
            return inputs







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Tuple     )		->Tuple:






        '''simple docstring'''






        return self.tokenizer.batch_decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        return self.tokenizer.decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    @contextmanager
    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your images inputs, or in a separate call."""     )
        _UpperCAmelCase          =   True
        _UpperCAmelCase          =   self.tokenizer
        yield
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]=False     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->List[str]:






        '''simple docstring'''






        if added_vocab is None:
            _UpperCAmelCase          =   self.tokenizer.get_added_vocab()

        _UpperCAmelCase          =   {}

        while tokens:
            _UpperCAmelCase          =   re.search(r"""<s_(.*?)>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            if start_token is None:
                break
            _UpperCAmelCase          =   start_token.group(1     )
            _UpperCAmelCase          =   re.search(rf"""</s_{key}>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            _UpperCAmelCase          =   start_token.group()
            if end_token is None:
                _UpperCAmelCase          =   tokens.replace(__UpperCamelCase     ,					""""""     )
            else:
                _UpperCAmelCase          =   end_token.group()
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
                if content is not None:
                    _UpperCAmelCase          =   content.group(1     ).strip()
                    if r"<s_" in content and r"</s_" in content:  # non-leaf node
                        _UpperCAmelCase          =   self.tokenajson(__UpperCamelCase     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )
                        if value:
                            if len(__UpperCamelCase     ) == 1:
                                _UpperCAmelCase          =   value[0]
                            _UpperCAmelCase          =   value
                    else:  # leaf nodes
                        _UpperCAmelCase          =   []
                        for leaf in content.split(r"""<sep/>"""     ):
                            _UpperCAmelCase          =   leaf.strip()
                            if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                _UpperCAmelCase          =   leaf[1:-2]  # for categorical special tokens
                            output[key].append(__UpperCamelCase     )
                        if len(output[key]     ) == 1:
                            _UpperCAmelCase          =   output[key][0]

                _UpperCAmelCase          =   tokens[tokens.find(__UpperCamelCase     ) + len(__UpperCamelCase     ) :].strip()
                if tokens[:6] == r"<sep/>":  # non-leaf nodes
                    return [output] + self.tokenajson(tokens[6:]     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )

        if len(__UpperCamelCase     ):
            return [output] if is_inner_value else output
        else:
            return [] if is_inner_value else {"text_sequence": tokens}







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






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor_class







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






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor