


import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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_MAPPING,
     SegformerForImageClassification,
     SegformerForSemanticSegmentation,
     SegformerModel,
 )
 from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import SegformerImageProcessor






class 	__lowercase					(	__snake_case	):





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






         """simple docstring"""







         UpperCAmelCase      					=  self.config_class(**self.inputs_dict							)
         self.parent.assertTrue(hasattr(__lowerCamelCase       ,							"""hidden_sizes"""							)							)
         self.parent.assertTrue(hasattr(__lowerCamelCase       ,							"""num_attention_heads"""							)							)
         self.parent.assertTrue(hasattr(__lowerCamelCase       ,							"""num_encoder_blocks"""							)							)






class 	__lowercase					:





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Union[str, Any]=1_3       ,							__lowerCamelCase    :       Optional[int]=6_4       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       Optional[Any]=4       ,							__lowerCamelCase    :       Any=[2, 2, 2, 2]       ,							__lowerCamelCase    :       str=[8, 4, 2, 1]       ,							__lowerCamelCase    :       List[Any]=[1_6, 3_2, 6_4, 1_2_8]       ,							__lowerCamelCase    :       int=[1, 4, 8, 1_6]       ,							__lowerCamelCase    :       List[str]=[1, 2, 4, 8]       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       Any=True       ,							__lowerCamelCase    :       Dict="gelu"       ,							__lowerCamelCase    :       str=0.1       ,							__lowerCamelCase    :       int=0.1       ,							__lowerCamelCase    :       List[Any]=0.02       ,							__lowerCamelCase    :       Any=3       ,							__lowerCamelCase    :       int=None       ,							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  num_encoder_blocks
         UpperCAmelCase      					=  sr_ratios
         UpperCAmelCase      					=  depths
         UpperCAmelCase      					=  hidden_sizes
         UpperCAmelCase      					=  downsampling_rates
         UpperCAmelCase      					=  num_attention_heads
         UpperCAmelCase      					=  is_training
         UpperCAmelCase      					=  use_labels
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  hidden_dropout_prob
         UpperCAmelCase      					=  attention_probs_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  num_labels
         UpperCAmelCase      					=  scope





      def 							_lowercase					(		self    :       Tuple							)     -> 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.image_size, self.image_size]       ,							self.num_labels							)

         UpperCAmelCase      					=  self.get_config()
         return config, pixel_values, labels





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






         """simple docstring"""







         return SegformerConfig(
             image_size=self.image_size       ,							num_channels=self.num_channels       ,							num_encoder_blocks=self.num_encoder_blocks       ,							depths=self.depths       ,							hidden_sizes=self.hidden_sizes       ,							num_attention_heads=self.num_attention_heads       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							initializer_range=self.initializer_range       ,							)





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






         """simple docstring"""







         UpperCAmelCase      					=  SegformerModel(config=__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         UpperCAmelCase      					=  UpperCAmelCase      					=  self.image_size // (self.downsampling_rates[-1] * 2)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)





      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Any							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  SegformerForSemanticSegmentation(__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         self.parent.assertEqual(
             result.logits.shape       ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)							)
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(
             result.logits.shape       ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)							)
         self.parent.assertGreater(result.loss       ,							0.0							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  1
         UpperCAmelCase      					=  SegformerForSemanticSegmentation(config=__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  torch.randint(0       ,							1       ,							(self.batch_size, self.image_size, self.image_size)							).to(__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertGreater(result.loss       ,							0.0							)





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






         """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 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(
          (
              SegformerModel,
              SegformerForSemanticSegmentation,
              SegformerForImageClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase										=							(
          {
              '''feature-extraction''': SegformerModel,
              '''image-classification''': SegformerForImageClassification,
              '''image-segmentation''': SegformerForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      UpperCamelCase										=							True
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  SegformerModelTester(self							)
         UpperCAmelCase      					=  SegformerConfigTester(self       ,							config_class=__lowerCamelCase							)





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






         """simple docstring"""







         self.config_tester.run_common_tests()





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






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase							)





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






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_binary_image_segmentation(*__lowerCamelCase							)





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






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*__lowerCamelCase							)





      @unittest.skip("""SegFormer does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Tuple							)     -> Dict:






         """simple docstring"""







         pass





      @unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> Union[str, Any]:






         """simple docstring"""







         pass





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






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            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]       ,							__lowerCamelCase							)





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






         """simple docstring"""







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

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  True
            UpperCAmelCase      					=  False
            UpperCAmelCase      					=  True
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            with torch.no_grad():
               UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)
            UpperCAmelCase      					=  outputs.attentions

            UpperCAmelCase      					=  sum(self.model_tester.depths							)
            self.assertEqual(len(__lowerCamelCase							)       ,							__lowerCamelCase							)

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

            self.assertEqual(len(__lowerCamelCase							)       ,							__lowerCamelCase							)

            # verify the first attentions (first block, first layer)
            UpperCAmelCase      					=  (self.model_tester.image_size // 4) ** 2
            UpperCAmelCase      					=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(attentions[0].shape[-3:]							)       ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,							)

            # verify the last attentions (last block, last layer)
            UpperCAmelCase      					=  (self.model_tester.image_size // 3_2) ** 2
            UpperCAmelCase      					=  (self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
            self.assertListEqual(
                list(attentions[-1].shape[-3:]							)       ,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]       ,							)
            UpperCAmelCase      					=  len(__lowerCamelCase							)

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

            self.assertEqual(out_len + 1       ,							len(__lowerCamelCase							)							)

            UpperCAmelCase      					=  outputs.attentions

            self.assertEqual(len(__lowerCamelCase							)       ,							__lowerCamelCase							)
            # verify the first attentions (first block, first layer)
            UpperCAmelCase      					=  (self.model_tester.image_size // 4) ** 2
            UpperCAmelCase      					=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]							)       ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,							)





      def 							_lowercase					(		self    :       Tuple							)     -> List[str]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Tuple							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()

            with torch.no_grad():
               UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

            UpperCAmelCase      					=  outputs.hidden_states

            UpperCAmelCase      					=  self.model_tester.num_encoder_blocks
            self.assertEqual(len(__lowerCamelCase							)       ,							__lowerCamelCase							)

            # verify the first hidden states (first block)
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]							)       ,							[
                    self.model_tester.hidden_sizes[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ]       ,							)

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  True
            check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

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

            check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





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






         """simple docstring"""







         if not self.model_tester.is_training:
            return

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

         for model_class in self.all_model_classes:
            if model_class in get_values(__lowerCamelCase							):
               continue

            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.train()
            UpperCAmelCase      					=  self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase       ,							return_labels=__lowerCamelCase							)
            UpperCAmelCase      					=  model(**__lowerCamelCase							).loss
            loss.backward()





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






         """simple docstring"""







         pass





      @slow
      def 							_lowercase					(		self    :       Any							)     -> str:






         """simple docstring"""







         for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  SegformerModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)

def 	_UpperCamelCase					(    )    ->Any:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_torch
class 	__lowercase					(	unittest.TestCase	):





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






         """simple docstring"""







         UpperCAmelCase      					=  SegformerImageProcessor(
             image_scale=(5_1_2, 5_1_2)       ,							keep_ratio=__lowerCamelCase       ,							align=__lowerCamelCase       ,							do_random_crop=__lowerCamelCase							)
         UpperCAmelCase      					=  SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""							).to(
             __lowerCamelCase							)

         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""pt"""							)
         UpperCAmelCase      					=  encoded_inputs.pixel_values.to(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase							)

         UpperCAmelCase      					=  torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  torch.tensor(
             [
                 [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                 [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                 [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
             ]							).to(__lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,							__lowerCamelCase       ,							atol=1e-4							)							)





      @slow
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  SegformerImageProcessor(
             image_scale=(5_1_2, 5_1_2)       ,							keep_ratio=__lowerCamelCase       ,							align=__lowerCamelCase       ,							do_random_crop=__lowerCamelCase							)
         UpperCAmelCase      					=  SegformerForSemanticSegmentation.from_pretrained(
             """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""							).to(__lowerCamelCase							)

         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""pt"""							)
         UpperCAmelCase      					=  encoded_inputs.pixel_values.to(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase							)

         UpperCAmelCase      					=  torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  torch.tensor(
             [
                 [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                 [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                 [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
             ]							).to(__lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,							__lowerCamelCase       ,							atol=1e-1							)							)





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






         """simple docstring"""







         UpperCAmelCase      					=  SegformerImageProcessor(
             image_scale=(5_1_2, 5_1_2)       ,							keep_ratio=__lowerCamelCase       ,							align=__lowerCamelCase       ,							do_random_crop=__lowerCamelCase							)
         UpperCAmelCase      					=  SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""							).to(
             __lowerCamelCase							)

         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""pt"""							)
         UpperCAmelCase      					=  encoded_inputs.pixel_values.to(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase							)

         UpperCAmelCase      					=  outputs.logits.detach().cpu()

         UpperCAmelCase      					=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase       ,							target_sizes=[(5_0_0, 3_0_0)]							)
         UpperCAmelCase      					=  torch.Size((5_0_0, 3_0_0)							)
         self.assertEqual(segmentation[0].shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase							)
         UpperCAmelCase      					=  torch.Size((1_2_8, 1_2_8)							)
         self.assertEqual(segmentation[0].shape       ,							__lowerCamelCase							)







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 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''image_processor''', '''tokenizer''']
      UpperCamelCase										=							'''BridgeTowerImageProcessor'''
      UpperCamelCase										=							('''RobertaTokenizer''', '''RobertaTokenizerFast''')





      def __init__(		self    :       int       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       List[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         super().__init__(__lowerCamelCase       ,							__lowerCamelCase							)





      def __call__(		self    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[bool, str, PaddingStrategy] = False       ,							__lowerCamelCase    :       Union[bool, str, TruncationStrategy] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       int = 0       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> BatchEncoding:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(
             text=__lowerCamelCase       ,							add_special_tokens=__lowerCamelCase       ,							padding=__lowerCamelCase       ,							truncation=__lowerCamelCase       ,							max_length=__lowerCamelCase       ,							stride=__lowerCamelCase       ,							pad_to_multiple_of=__lowerCamelCase       ,							return_token_type_ids=__lowerCamelCase       ,							return_attention_mask=__lowerCamelCase       ,							return_overflowing_tokens=__lowerCamelCase       ,							return_special_tokens_mask=__lowerCamelCase       ,							return_offsets_mapping=__lowerCamelCase       ,							return_length=__lowerCamelCase       ,							verbose=__lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							**__lowerCamelCase       ,							)
         # add pixel_values + pixel_mask
         UpperCAmelCase      					=  self.image_processor(
             __lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_center_crop=__lowerCamelCase       ,							**__lowerCamelCase							)
         encoding.update(__lowerCamelCase							)

         return encoding





      def 							_lowercase					(		self    :       Optional[Any]       ,							*__lowerCamelCase    :       Optional[int]       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Dict       ,							*__lowerCamelCase    :       Any       ,							**__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







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





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer.model_input_names
         UpperCAmelCase      					=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



