



'''simple docstring'''





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
  )
  from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor






class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=2					,  A=3					,  A=4					,  A=2					,  A=7					,  A=True					,  A=True					,  A=True					,  A=True					,  A=99					,  A=36					,  A=3					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=6					,  A=6					,  A=3					,  A=4					,  A=None					,  A=1_000					,  )       ->	List[str]:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    patch_size
      _a           =    text_seq_length
      _a           =    is_training
      _a           =    use_input_mask
      _a           =    use_token_type_ids
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    type_vocab_size
      _a           =    type_sequence_label_size
      _a           =    initializer_range
      _a           =    coordinate_size
      _a           =    shape_size
      _a           =    num_labels
      _a           =    num_choices
      _a           =    scope
      _a           =    range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      _a           =    text_seq_length
      _a           =    (image_size // patch_size) ** 2 + 1
      _a           =    self.text_seq_length + self.image_seq_length



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


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.text_seq_length]					,  self.vocab_size			)

      _a           =    ids_tensor([self.batch_size, self.text_seq_length, 4]					,  self.range_bbox			)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]			):
           for j in range(bbox.shape[1]			):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                     _a           =    bbox[i, j, 3]
                     _a           =    bbox[i, j, 1]
                     _a           =    t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                     _a           =    bbox[i, j, 2]
                     _a           =    bbox[i, j, 0]
                     _a           =    t

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

      _a           =    None
      if self.use_input_mask:
           _a           =    random_attention_mask([self.batch_size, self.text_seq_length]			)

      _a           =    None
      if self.use_token_type_ids:
           _a           =    ids_tensor([self.batch_size, self.text_seq_length]					,  self.type_vocab_size			)

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

      _a           =    LayoutLMvaConfig(
          vocab_size=self.vocab_size					,  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					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  initializer_range=self.initializer_range					,  coordinate_size=self.coordinate_size					,  shape_size=self.shape_size					,  input_size=self.image_size					,  patch_size=self.patch_size					,  )

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    LayoutLMvaModel(config=A			)
      model.to(A			)
      model.eval()

      # text + image
      _a           =    model(A					,  pixel_values=A			)
      _a           =    model(
          A					,  bbox=A					,  pixel_values=A					,  attention_mask=A					,  token_type_ids=A			)
      _a           =    model(A					,  bbox=A					,  pixel_values=A					,  token_type_ids=A			)
      _a           =    model(A					,  bbox=A					,  pixel_values=A			)

      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)

      # text only
      _a           =    model(A			)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,  (self.batch_size, self.text_seq_length, self.hidden_size)			)

      # image only
      _a           =    model(pixel_values=A			)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,  (self.batch_size, self.image_seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.num_labels
      _a           =    LayoutLMvaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(
          A					,  bbox=A					,  pixel_values=A					,  attention_mask=A					,  token_type_ids=A					,  labels=A					,  )
      self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.num_labels
      _a           =    LayoutLMvaForTokenClassification(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(
          A					,  bbox=A					,  pixel_values=A					,  attention_mask=A					,  token_type_ids=A					,  labels=A					,  )
      self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.text_seq_length, self.num_labels)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    LayoutLMvaForQuestionAnswering(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(
          A					,  bbox=A					,  pixel_values=A					,  attention_mask=A					,  token_type_ids=A					,  start_positions=A					,  end_positions=A					,  )
      self.parent.assertEqual(result.start_logits.shape					,  (self.batch_size, self.seq_length)			)
      self.parent.assertEqual(result.end_logits.shape					,  (self.batch_size, self.seq_length)			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''input_ids''': input_ids,
          '''bbox''': bbox,
          '''pixel_values''': pixel_values,
          '''token_type_ids''': token_type_ids,
          '''attention_mask''': input_mask,
      }
      return config, inputs_dict






@require_torch
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			False
 __lowerCamelCase       :			int		=			False
 __lowerCamelCase       :			int		=			False

 __lowerCamelCase       :			List[str]		=			(
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase       :			List[str]		=			(
     {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
     if is_torch_available()
     else {}
 )



 def    a__    (self					,  A					,  A					,  A					,  A					,  A			)       ->	Dict:


      """simple docstring"""



      return True



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    LayoutLMvaModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self					,  A					,  A					,  A=False			)       ->	List[str]:


      """simple docstring"""



      _a           =    copy.deepcopy(A			)
      if model_class in get_values(A			):
           _a           =    {
               k: v.unsqueeze(1			).expand(-1					,  self.model_tester.num_choices					,  -1			).contiguous()
               if isinstance(A					,  torch.Tensor			) and v.ndim > 1
               else v
               for k, v in inputs_dict.items()
           }
      if return_labels:
           if model_class in get_values(A			):
                _a           =    torch.ones(self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
           elif model_class in get_values(A			):
                _a           =    torch.zeros(
                    self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
                _a           =    torch.zeros(
                    self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
           elif model_class in [
               *get_values(A			),
           ]:
                _a           =    torch.zeros(
                    self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
           elif model_class in [
               *get_values(A			),
           ]:
                _a           =    torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.text_seq_length)					,  dtype=torch.long					,  device=A					,  )

      return inputs_dict



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _a           =    type
           self.model_tester.create_and_check_model(*A			)



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A			)



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A			)



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A			)



 @slow
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a           =    LayoutLMvaModel.from_pretrained(A			)
           self.assertIsNotNone(A			)





def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @cached_property
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      return LayoutLMvaImageProcessor(apply_ocr=A			) if is_vision_available() else None



 @slow
 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''			).to(A			)

      _a           =    self.default_image_processor
      _a           =    prepare_img()
      _a           =    image_processor(images=A					,  return_tensors='''pt'''			).pixel_values.to(A			)

      _a           =    torch.tensor([[1, 2]]			)
      _a           =    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]			).unsqueeze(0			)

      # forward pass
      _a           =    model(
          input_ids=input_ids.to(A			)					,  bbox=bbox.to(A			)					,  pixel_values=pixel_values.to(A			)					,  )

      # verify the logits
      _a           =    torch.Size((1, 199, 768)			)
      self.assertEqual(outputs.last_hidden_state.shape					,  A			)

      _a           =    torch.tensor(
          [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]			).to(A			)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,  A					,  atol=1E-4			)			)




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

