
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        SCREAMING_SNAKE_CASE__    :

       """simple docstring"""






       def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=99     ,	SCREAMING_SNAKE_CASE_=36     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=512     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1000     ,	)->   Optional[Any]:





             '''simple docstring'''






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

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



       def     A__		(  self			)->   Dict:





             '''simple docstring'''






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

             __UpperCamelCase       =			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]:
                               __UpperCamelCase       =			bbox[i, j, 3]
                               __UpperCamelCase       =			bbox[i, j, 1]
                               __UpperCamelCase       =			t
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                               __UpperCamelCase       =			bbox[i, j, 2]
                               __UpperCamelCase       =			bbox[i, j, 0]
                               __UpperCamelCase       =			t

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

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

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

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

             __UpperCamelCase       =			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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





             '''simple docstring'''






             __UpperCamelCase       =			LayoutLMvaModel(config=_A			)
             model.to(_A			)
             model.eval()

             # text + image
             __UpperCamelCase       =			model(_A     ,	pixel_values=_A			)
             __UpperCamelCase       =			model(
                 _A     ,	bbox=_A     ,	pixel_values=_A     ,	attention_mask=_A     ,	token_type_ids=_A			)
             __UpperCamelCase       =			model(_A     ,	bbox=_A     ,	pixel_values=_A     ,	token_type_ids=_A			)
             __UpperCamelCase       =			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
             __UpperCamelCase       =			model(_A			)
             self.parent.assertEqual(
                 result.last_hidden_state.shape     ,	(self.batch_size, self.text_seq_length, self.hidden_size)			)

             # image only
             __UpperCamelCase       =			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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





             '''simple docstring'''






             __UpperCamelCase       =			self.num_labels
             __UpperCamelCase       =			LayoutLMvaForSequenceClassification(_A			)
             model.to(_A			)
             model.eval()
             __UpperCamelCase       =			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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





             '''simple docstring'''






             __UpperCamelCase       =			self.num_labels
             __UpperCamelCase       =			LayoutLMvaForTokenClassification(config=_A			)
             model.to(_A			)
             model.eval()
             __UpperCamelCase       =			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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





             '''simple docstring'''






             __UpperCamelCase       =			LayoutLMvaForQuestionAnswering(config=_A			)
             model.to(_A			)
             model.eval()
             __UpperCamelCase       =			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'''






             __UpperCamelCase       =			self.prepare_config_and_inputs()
             (
                 __UpperCamelCase					
             )       =			config_and_inputs
             __UpperCamelCase       =			{
                 '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        SCREAMING_SNAKE_CASE__    (							a__   ,		a__   ,		unittest.TestCase    ):

       """simple docstring"""






       _snake_case     		=  False
       _snake_case     		=  False
       _snake_case     		=  False

       _snake_case     		=  (
           (
               LayoutLMvaModel,
               LayoutLMvaForSequenceClassification,
               LayoutLMvaForTokenClassification,
               LayoutLMvaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       _snake_case     		=  (
           {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
           if is_torch_available()
           else {}
       )



       def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[Any]:





             '''simple docstring'''






             return True



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





             '''simple docstring'''






             __UpperCamelCase       =			LayoutLMvaModelTester(self			)
             __UpperCamelCase       =			ConfigTester(self     ,	config_class=_A     ,	hidden_size=37			)



       def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False			)->   List[Any]:





             '''simple docstring'''






             __UpperCamelCase       =			copy.deepcopy(_A			)
             if model_class in get_values(_A			):
                   __UpperCamelCase       =			{
                       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			):
                         __UpperCamelCase       =			torch.ones(self.model_tester.batch_size     ,	dtype=torch.long     ,	device=_A			)
                   elif model_class in get_values(_A			):
                         __UpperCamelCase       =			torch.zeros(
                             self.model_tester.batch_size     ,	dtype=torch.long     ,	device=_A			)
                         __UpperCamelCase       =			torch.zeros(
                             self.model_tester.batch_size     ,	dtype=torch.long     ,	device=_A			)
                   elif model_class in [
                       *get_values(_A			),
                   ]:
                         __UpperCamelCase       =			torch.zeros(
                             self.model_tester.batch_size     ,	dtype=torch.long     ,	device=_A			)
                   elif model_class in [
                       *get_values(_A			),
                   ]:
                         __UpperCamelCase       =			torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,	dtype=torch.long     ,	device=_A     ,	)

             return inputs_dict



       def     A__		(  self			)->   int:





             '''simple docstring'''






             self.config_tester.run_common_tests()



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





             '''simple docstring'''






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



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





             '''simple docstring'''






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



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





             '''simple docstring'''






             __UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*_A			)



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





             '''simple docstring'''






             __UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*_A			)



       def     A__		(  self			)->   Tuple:





             '''simple docstring'''






             __UpperCamelCase       =			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]:
                   __UpperCamelCase       =			LayoutLMvaModel.from_pretrained(_A			)
                   self.assertIsNotNone(_A			)





def 	A_ (							)      ->    List[str]:



      '''simple docstring'''

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






@require_torch
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

       """simple docstring"""






       @cached_property
       def     A__		(  self			)->   List[str]:





             '''simple docstring'''






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



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





             '''simple docstring'''






             __UpperCamelCase       =			LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''			).to(_A			)

             __UpperCamelCase       =			self.default_image_processor
             __UpperCamelCase       =			prepare_img()
             __UpperCamelCase       =			image_processor(images=_A     ,	return_tensors='''pt'''			).pixel_values.to(_A			)

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

             # forward pass
             __UpperCamelCase       =			model(
                 input_ids=input_ids.to(_A			)     ,	bbox=bbox.to(_A			)     ,	pixel_values=pixel_values.to(_A			)     ,	)

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

             __UpperCamelCase       =			torch.tensor(
                 [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]			).to(_A			)

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






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




