




import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
     from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=[1, 2, 1]       ,_SCREAMING_SNAKE_CASE=[2, 2, 4]       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=2.0       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=0.0       ,_SCREAMING_SNAKE_CASE=0.0       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-5       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=8       ,)			->		List[str]:
         UpperCAmelCase_      :							Dict       =      parent
         UpperCAmelCase_      :							Optional[int]       =      batch_size
         UpperCAmelCase_      :							Dict       =      image_size
         UpperCAmelCase_      :							Optional[Any]       =      patch_size
         UpperCAmelCase_      :							Tuple       =      num_channels
         UpperCAmelCase_      :							Optional[int]       =      embed_dim
         UpperCAmelCase_      :							str       =      depths
         UpperCAmelCase_      :							Optional[Any]       =      num_heads
         UpperCAmelCase_      :							Any       =      window_size
         UpperCAmelCase_      :							List[str]       =      mlp_ratio
         UpperCAmelCase_      :							Union[str, Any]       =      qkv_bias
         UpperCAmelCase_      :							Union[str, Any]       =      hidden_dropout_prob
         UpperCAmelCase_      :							Optional[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      drop_path_rate
         UpperCAmelCase_      :							Union[str, Any]       =      hidden_act
         UpperCAmelCase_      :							int       =      use_absolute_embeddings
         UpperCAmelCase_      :							List[str]       =      patch_norm
         UpperCAmelCase_      :							Optional[int]       =      layer_norm_eps
         UpperCAmelCase_      :							Dict       =      initializer_range
         UpperCAmelCase_      :							Optional[int]       =      is_training
         UpperCAmelCase_      :							int       =      scope
         UpperCAmelCase_      :							List[Any]       =      use_labels
         UpperCAmelCase_      :							Any       =      type_sequence_label_size
         UpperCAmelCase_      :							Optional[int]       =      encoder_stride





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Dict       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         UpperCAmelCase_      :							Any       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )

         UpperCAmelCase_      :							str       =      self.get_config()

         return config, pixel_values, labels





    def 							a__				(				self  )			->		Any:
         return SwinvaConfig(
             image_size=self.image_size       ,patch_size=self.patch_size       ,num_channels=self.num_channels       ,embed_dim=self.embed_dim       ,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       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      SwinvaModel(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[str]       =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
         UpperCAmelCase_      :							Tuple       =      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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							Dict       =      SwinvaForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(
             result.logits.shape       ,(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

         # test greyscale images
         UpperCAmelCase_      :							str       =      1
         UpperCAmelCase_      :							Dict       =      SwinvaForMaskedImageModeling(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()

         UpperCAmelCase_      :							Tuple       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, 1, self.image_size, self.image_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Any       =      self.type_sequence_label_size
         UpperCAmelCase_      :							Dict       =      SwinvaForImageClassification(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							List[Any]       =      self.prepare_config_and_inputs()
         UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      config_and_inputs
         UpperCAmelCase_      :							Union[str, Any]       =      {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
    )
    lowerCAmelCase        =				(
        {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Any       =      SwinvaModelTester(self  )
         UpperCAmelCase_      :							Union[str, Any]       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,embed_dim=37  )





    def 							a__				(				self  )			->		str:
         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 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''  )
    def 							a__				(				self  )			->		List[str]:
         pass





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Optional[Any]       =      model_class(_SCREAMING_SNAKE_CASE  )
              self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module)  )
              UpperCAmelCase_      :							Dict       =      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,nn.Linear  )  )





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_      :							List[str]       =      ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_      :							Dict       =      True

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Optional[Any]       =      True
              UpperCAmelCase_      :							List[str]       =      False
              UpperCAmelCase_      :							Optional[Any]       =      True
              UpperCAmelCase_      :							Any       =      model_class(_SCREAMING_SNAKE_CASE  )
              model.to(_SCREAMING_SNAKE_CASE  )
              model.eval()
              with torch.no_grad():
                   UpperCAmelCase_      :							str       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )
              UpperCAmelCase_      :							Any       =      outputs.attentions
              UpperCAmelCase_      :							Any       =      len(self.model_tester.depths  )
              self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              UpperCAmelCase_      :							str       =      True
              UpperCAmelCase_      :							List[str]       =      config.window_size**2
              UpperCAmelCase_      :							List[Any]       =      model_class(_SCREAMING_SNAKE_CASE  )
              model.to(_SCREAMING_SNAKE_CASE  )
              model.eval()
              with torch.no_grad():
                   UpperCAmelCase_      :							str       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )
              UpperCAmelCase_      :							Optional[int]       =      outputs.attentions
              self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )

              self.assertListEqual(
                  list(attentions[0].shape[-3:]  )       ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,)
              UpperCAmelCase_      :							Optional[Any]       =      len(_SCREAMING_SNAKE_CASE  )

              # Check attention is always last and order is fine
              UpperCAmelCase_      :							Any       =      True
              UpperCAmelCase_      :							Any       =      True
              UpperCAmelCase_      :							List[Any]       =      model_class(_SCREAMING_SNAKE_CASE  )
              model.to(_SCREAMING_SNAKE_CASE  )
              model.eval()
              with torch.no_grad():
                   UpperCAmelCase_      :							Tuple       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )

              if hasattr(self.model_tester       ,'''num_hidden_states_types'''  ):
                   UpperCAmelCase_      :							Optional[int]       =      self.model_tester.num_hidden_states_types
              else:
                   # also another +1 for reshaped_hidden_states
                   UpperCAmelCase_      :							List[str]       =      2
              self.assertEqual(out_len + added_hidden_states       ,len(_SCREAMING_SNAKE_CASE  )  )

              UpperCAmelCase_      :							str       =      outputs.attentions

              self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]  )       ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Dict       =      model_class(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()

         with torch.no_grad():
              UpperCAmelCase_      :							Optional[int]       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )

         UpperCAmelCase_      :							Optional[Any]       =      outputs.hidden_states

         UpperCAmelCase_      :							Dict       =      getattr(
             self.model_tester       ,'''expected_num_hidden_layers'''       ,len(self.model_tester.depths  ) + 1  )
         self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )

         # Swinv2 has a different seq_length
         UpperCAmelCase_      :							List[Any]       =      (
             config.patch_size
             if isinstance(config.patch_size       ,collections.abc.Iterable  )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase_      :							str       =      (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_      :							Optional[int]       =      outputs.reshaped_hidden_states
         self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      reshaped_hidden_states[0].shape
         UpperCAmelCase_      :							Dict       =      (
             reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,height * width  ).permute(0       ,2       ,1  )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]  )       ,[num_patches, self.model_tester.embed_dim]       ,)





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase_      :							int       =      (
             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:
              UpperCAmelCase_      :							Tuple       =      True
              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_      :							Optional[Any]       =      True

              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_      :							List[Any]       =      3

         UpperCAmelCase_      :							List[str]       =      (
             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_      :							Optional[Any]       =      (
             config.patch_size
             if isinstance(config.patch_size       ,collections.abc.Iterable  )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase_      :							Any       =      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCAmelCase_      :							Union[str, Any]       =      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Dict       =      True
              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,(padded_height, padded_width)  )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_      :							Tuple       =      True
              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,(padded_height, padded_width)  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							List[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		List[str]:
         for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							List[Any]       =      SwinvaModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_,   UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase_      :							List[str]       =      _config_zero_init(_SCREAMING_SNAKE_CASE  )
         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Optional[Any]       =      model_class(config=_SCREAMING_SNAKE_CASE  )
              for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" 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       ):






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		List[Any]:
         return (
             AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''  )
             if is_vision_available()
             else None
         )





    @slow
    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[int]       =      SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''  ).to(
             _SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      self.default_image_processor

         UpperCAmelCase_      :							List[str]       =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
         UpperCAmelCase_      :							Any       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_      :							Union[str, Any]       =      model(**_SCREAMING_SNAKE_CASE  )

         # verify the logits
         UpperCAmelCase_      :							Optional[Any]       =      torch.Size((1, 1_000)  )
         self.assertEqual(outputs.logits.shape       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      torch.tensor([-0.39_47, -0.43_06, 0.00_26]  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType


__a          =			logging.get_logger(__name__)

__a          =			{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''layoutlmv3'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=50_265       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-5       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=1_024       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=64       ,_SCREAMING_SNAKE_CASE=256       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=224       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         super().__init__(
             vocab_size=_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       ,max_position_embeddings=_SCREAMING_SNAKE_CASE       ,type_vocab_size=_SCREAMING_SNAKE_CASE       ,initializer_range=_SCREAMING_SNAKE_CASE       ,layer_norm_eps=_SCREAMING_SNAKE_CASE       ,pad_token_id=_SCREAMING_SNAKE_CASE       ,bos_token_id=_SCREAMING_SNAKE_CASE       ,eos_token_id=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Dict       =      max_ad_position_embeddings
         UpperCAmelCase_      :							Any       =      coordinate_size
         UpperCAmelCase_      :							Tuple       =      shape_size
         UpperCAmelCase_      :							Optional[int]       =      has_relative_attention_bias
         UpperCAmelCase_      :							Union[str, Any]       =      rel_pos_bins
         UpperCAmelCase_      :							Dict       =      max_rel_pos
         UpperCAmelCase_      :							Union[str, Any]       =      has_spatial_attention_bias
         UpperCAmelCase_      :							Any       =      rel_ad_pos_bins
         UpperCAmelCase_      :							Tuple       =      max_rel_ad_pos
         UpperCAmelCase_      :							List[str]       =      text_embed
         UpperCAmelCase_      :							int       =      visual_embed
         UpperCAmelCase_      :							int       =      input_size
         UpperCAmelCase_      :							Dict       =      num_channels
         UpperCAmelCase_      :							int       =      patch_size
         UpperCAmelCase_      :							Dict       =      classifier_dropout


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.12'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         # The order of inputs is different for question answering and sequence classification
         if self.task in ["question-answering", "sequence-classification"]:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]  )
         else:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                  ]  )





    @property
    def 							a__				(				self  )			->		float:
         return 1e-5





    @property
    def 							a__				(				self  )			->		int:
         return 12





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 40       ,_SCREAMING_SNAKE_CASE = 40       ,)			->		Mapping[str, Any]:
         setattr(processor.image_processor       ,'''apply_ocr'''       ,_SCREAMING_SNAKE_CASE  )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCAmelCase_      :							List[str]       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_batch       ,num_token_to_add=0  )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCAmelCase_      :							Optional[Any]       =      processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=_SCREAMING_SNAKE_CASE  )
         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_      :							Optional[Any]       =      [[''' '''.join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         UpperCAmelCase_      :							Tuple       =      [[[48, 84, 73, 128]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         UpperCAmelCase_      :							Union[str, Any]       =      self._generate_dummy_images(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      dict(
             processor(
                 _SCREAMING_SNAKE_CASE       ,text=_SCREAMING_SNAKE_CASE       ,boxes=_SCREAMING_SNAKE_CASE       ,return_tensors=_SCREAMING_SNAKE_CASE       ,)  )

         return inputs