
"""simple docstring"""




import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
     MODEL_MAPPING,
     DataaVecVisionForImageClassification,
     DataaVecVisionForSemanticSegmentation,
     DataaVecVisionModel,
 )
 from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor




class 					UpperCamelCase     :

    """simple docstring"""



    def __init__(    self		,     lowercase__		,     lowercase__=100		,     lowercase__=13		,     lowercase__=30		,     lowercase__=2		,     lowercase__=3		,     lowercase__=True		,     lowercase__=True		,     lowercase__=32		,     lowercase__=4		,     lowercase__=4		,     lowercase__=37		,     lowercase__="gelu"		,     lowercase__=0.1		,     lowercase__=0.1		,     lowercase__=10		,     lowercase__=0.02		,     lowercase__=3		,     lowercase__=None		,     lowercase__=[0, 1, 2, 3]		,     )   ->   Dict:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     parent
         SCREAMING_SNAKE_CASE           =     100
         SCREAMING_SNAKE_CASE           =     batch_size
         SCREAMING_SNAKE_CASE           =     image_size
         SCREAMING_SNAKE_CASE           =     patch_size
         SCREAMING_SNAKE_CASE           =     num_channels
         SCREAMING_SNAKE_CASE           =     is_training
         SCREAMING_SNAKE_CASE           =     use_labels
         SCREAMING_SNAKE_CASE           =     hidden_size
         SCREAMING_SNAKE_CASE           =     num_hidden_layers
         SCREAMING_SNAKE_CASE           =     num_attention_heads
         SCREAMING_SNAKE_CASE           =     intermediate_size
         SCREAMING_SNAKE_CASE           =     hidden_act
         SCREAMING_SNAKE_CASE           =     hidden_dropout_prob
         SCREAMING_SNAKE_CASE           =     attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE           =     type_sequence_label_size
         SCREAMING_SNAKE_CASE           =     initializer_range
         SCREAMING_SNAKE_CASE           =     scope
         SCREAMING_SNAKE_CASE           =     out_indices
         SCREAMING_SNAKE_CASE           =     num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         SCREAMING_SNAKE_CASE           =     (image_size // patch_size) ** 2
         SCREAMING_SNAKE_CASE           =     num_patches + 1


    def 							A (    self      )   ->   str:
         """simple docstring"""







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

         SCREAMING_SNAKE_CASE           =     None
         SCREAMING_SNAKE_CASE           =     None
         if self.use_labels:
              SCREAMING_SNAKE_CASE           =     ids_tensor([self.batch_size]		,     self.type_sequence_label_size      )
              SCREAMING_SNAKE_CASE           =     ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels      )

         SCREAMING_SNAKE_CASE           =     self.get_config()

         return config, pixel_values, labels, pixel_labels


    def 							A (    self      )   ->   Dict:
         """simple docstring"""







         return DataaVecVisionConfig(
             vocab_size=self.vocab_size		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=lowercase__		,     initializer_range=self.initializer_range		,     out_indices=self.out_indices		,     )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     DataaVecVisionModel(config=lowercase__      )
         model.to(lowercase__      )
         model.eval()
         SCREAMING_SNAKE_CASE           =     model(lowercase__      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         SCREAMING_SNAKE_CASE           =     (self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, num_patches + 1, self.hidden_size)      )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   int:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.type_sequence_label_size
         SCREAMING_SNAKE_CASE           =     DataaVecVisionForImageClassification(lowercase__      )
         model.to(lowercase__      )
         model.eval()
         SCREAMING_SNAKE_CASE           =     model(lowercase__		,     labels=lowercase__      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)      )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Union[str, Any]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.num_labels
         SCREAMING_SNAKE_CASE           =     DataaVecVisionForSemanticSegmentation(lowercase__      )
         model.to(lowercase__      )
         model.eval()
         SCREAMING_SNAKE_CASE           =     model(lowercase__      )
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
         SCREAMING_SNAKE_CASE           =     model(lowercase__		,     labels=lowercase__      )
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )




    def 							A (    self      )   ->   List[Any]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     config_and_inputs
         SCREAMING_SNAKE_CASE           =     {'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__  ,				unittest.TestCase				):

    """simple docstring"""



    UpperCAmelCase_    :				Any						  =    (
        (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_    :				Union[str, Any]						  =    (
        {
            "feature-extraction": DataaVecVisionModel,
            "image-classification": DataaVecVisionForImageClassification,
            "image-segmentation": DataaVecVisionForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase_    :				List[str]						  =    False
    UpperCAmelCase_    :				List[str]						  =    False
    UpperCAmelCase_    :				str						  =    False


    def 							A (    self      )   ->   Optional[int]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     DataaVecVisionModelTester(self      )
         SCREAMING_SNAKE_CASE           =     ConfigTester(
             self		,     config_class=lowercase__		,     has_text_modality=lowercase__		,     hidden_size=37      )


    def 							A (    self      )   ->   Optional[Any]:
         """simple docstring"""







         self.config_tester.run_common_tests()


    def 							A (    self      )   ->   Any:
         """simple docstring"""







         pass


    @require_torch_multi_gpu
    @unittest.skip(
        reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
    def 							A (    self      )   ->   Optional[int]:
         """simple docstring"""







         pass


    def 							A (    self      )   ->   Optional[Any]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
              self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
              SCREAMING_SNAKE_CASE           =     model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase__		,     nn.Linear      )      )


    def 							A (    self      )   ->   str:
         """simple docstring"""







         SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE           =     ['pixel_values']
              self.assertListEqual(arg_names[:1]		,     lowercase__      )


    def 							A (    self      )   ->   List[str]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__      )


    def 							A (    self      )   ->   Optional[int]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*lowercase__      )


    def 							A (    self      )   ->   Optional[int]:
         """simple docstring"""







         if not self.model_tester.is_training:
              return

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

         for model_class in self.all_model_classes:
              if model_class in [*get_values(lowercase__      )]:
                   continue

              SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
              model.to(lowercase__      )
              model.train()
              SCREAMING_SNAKE_CASE           =     self._prepare_for_class(lowercase__		,     lowercase__		,     return_labels=lowercase__      )
              SCREAMING_SNAKE_CASE           =     model(**lowercase__      ).loss
              loss.backward()


    def 							A (    self      )   ->   int:
         """simple docstring"""







         SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
              return

         SCREAMING_SNAKE_CASE           =     False
         SCREAMING_SNAKE_CASE           =     True

         for model_class in self.all_model_classes:
              if model_class in [*get_values(lowercase__      )] or not model_class.supports_gradient_checkpointing:
                   continue
              # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
              # this can then be incorporated into _prepare_for_class in test_modeling_common.py
              elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     inputs_dict['pixel_values'].shape
                   SCREAMING_SNAKE_CASE           =     torch.zeros(
                       [self.model_tester.batch_size, height, width]		,     device=lowercase__      ).long()
              SCREAMING_SNAKE_CASE           =     model_class(lowercase__      )
              model.gradient_checkpointing_enable()
              model.to(lowercase__      )
              model.train()
              SCREAMING_SNAKE_CASE           =     self._prepare_for_class(lowercase__		,     lowercase__		,     return_labels=lowercase__      )
              SCREAMING_SNAKE_CASE           =     model(**lowercase__      ).loss
              loss.backward()


    def 							A (    self      )   ->   int:
         """simple docstring"""







         SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE           =     _config_zero_init(lowercase__      )
         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE           =     model_class(config=lowercase__      )
              for name, param in model.named_parameters():
                   # we skip lambda parameters as these require special initial values
                   # determined by config.layer_scale_init_value
                   if "lambda" in name:
                        continue
                   if 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'''		,     )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__=2E-4		,     lowercase__="outputs"		,     lowercase__=None      )   ->   List[Any]:
         """simple docstring"""







         super().check_pt_tf_outputs(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      )


    def 							A (    self      )   ->   Tuple:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowercase__      )




    @slow
    def 							A (    self      )   ->   int:
         """simple docstring"""







         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE           =     DataaVecVisionModel.from_pretrained(lowercase__      )
              self.assertIsNotNone(lowercase__      )






def   UpperCamelCase_  (		):
     SCREAMING_SNAKE_CASE           =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image




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

    """simple docstring"""



    @cached_property
    def 							A (    self      )   ->   Union[str, Any]:
         """simple docstring"""







         return (
             BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'      ) if is_vision_available() else None
         )




    @slow
    def 							A (    self      )   ->   List[str]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'      ).to(
             lowercase__      )

         SCREAMING_SNAKE_CASE           =     self.default_image_processor
         SCREAMING_SNAKE_CASE           =     prepare_img()
         SCREAMING_SNAKE_CASE           =     image_processor(images=lowercase__		,     return_tensors='pt'      ).to(lowercase__      )

         # forward pass
         with torch.no_grad():
              SCREAMING_SNAKE_CASE           =     model(**lowercase__      )
         SCREAMING_SNAKE_CASE           =     outputs.logits

         # verify the logits
         SCREAMING_SNAKE_CASE           =     torch.Size((1, 1000)      )
         self.assertEqual(logits.shape		,     lowercase__      )

         SCREAMING_SNAKE_CASE           =     torch.tensor([0.3_277, -0.1_395, 0.0_911]      ).to(lowercase__      )

         self.assertTrue(torch.allclose(logits[0, :3]		,     lowercase__		,     atol=1E-4      )      )

         SCREAMING_SNAKE_CASE           =     [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
         self.assertEqual(logits[0].topk(2      ).indices.cpu().tolist()		,     lowercase__      )


"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	snake_case			       =		False

if is_vision_available():
	from PIL import Image

	from transformers import (
	    AutoProcessor,
	    PixaStructImageProcessor,
	    PixaStructProcessor,
	    PreTrainedTokenizerFast,
	    TaTokenizer,
	)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,				reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."  ,				)
@require_vision
@require_torch
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     tempfile.mkdtemp()

									SCREAMING_SNAKE_CASE           =     PixaStructImageProcessor()
									SCREAMING_SNAKE_CASE           =     TaTokenizer.from_pretrained('t5-small'      )

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(lowercase__		,     lowercase__      )

									processor.save_pretrained(self.tmpdirname      )


				def 							A (    self		,     **lowercase__      )   ->   Dict:
									"""simple docstring"""







									return AutoProcessor.from_pretrained(self.tmpdirname		,     **lowercase__      ).tokenizer


				def 							A (    self		,     **lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







									return AutoProcessor.from_pretrained(self.tmpdirname		,     **lowercase__      ).image_processor


				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									shutil.rmtree(self.tmpdirname      )


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta      )]

									SCREAMING_SNAKE_CASE           =     [Image.fromarray(np.moveaxis(lowercase__		,     0		,     -1      )      ) for x in image_inputs]

									return image_inputs


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()      )
									processor.save_pretrained(self.tmpdirname      )

									SCREAMING_SNAKE_CASE           =     self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)'      )
									SCREAMING_SNAKE_CASE           =     self.get_image_processor(do_normalize=lowercase__		,     padding_value=1.0      )

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor.from_pretrained(
									    self.tmpdirname		,     bos_token='(BOS)'		,     eos_token='(EOS)'		,     do_normalize=lowercase__		,     padding_value=1.0      )

									self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()      )
									self.assertIsInstance(processor.tokenizer		,     lowercase__      )

									self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.image_processor		,     lowercase__      )


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     image_processor(lowercase__		,     return_tensors='np'      )
									SCREAMING_SNAKE_CASE           =     processor(images=lowercase__		,     return_tensors='np'      )

									for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()		,     input_processor[key].sum()		,     delta=1E-2      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__      )

									SCREAMING_SNAKE_CASE           =     tokenizer(lowercase__		,     return_token_type_ids=lowercase__		,     add_special_tokens=lowercase__      )

									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]		,     encoded_processor[key]      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'
									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__      )

									self.assertListEqual(
									    list(inputs.keys()      )		,     ['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']      )

									# test if it raises when no input is passed
									with pytest.raises(lowercase__      ):
														processor()


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'
									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__      )

									SCREAMING_SNAKE_CASE           =     [512, 1024, 2048, 4096]
									SCREAMING_SNAKE_CASE           =     [770, 770, 770, 770]
									# with text
									for i, max_patch in enumerate(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__		,     max_patches=lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[0]		,     lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[1]		,     expected_hidden_size[i]      )

									# without text input
									for i, max_patch in enumerate(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     processor(images=lowercase__		,     max_patches=lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[0]		,     lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[1]		,     expected_hidden_size[i]      )


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE           =     processor.batch_decode(lowercase__      )
									SCREAMING_SNAKE_CASE           =     tokenizer.batch_decode(lowercase__      )

									self.assertListEqual(lowercase__		,     lowercase__      )




				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'
									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__      )

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(
									    list(inputs.keys()      )		,     ['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']      )

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__      )

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(list(inputs.keys()      )		,     ['input_ids', 'attention_mask']      )
