


'''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     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Tuple  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       Optional[int]=2  ,		UpperCamelCase       :       List[Any]=3  ,		UpperCamelCase       :       int=4  ,		UpperCamelCase       :       Optional[int]=2  ,		UpperCamelCase       :       str=7  ,		UpperCamelCase       :       Any=True  ,		UpperCamelCase       :       str=True  ,		UpperCamelCase       :       Optional[int]=True  ,		UpperCamelCase       :       Optional[Any]=True  ,		UpperCamelCase       :       Dict=99  ,		UpperCamelCase       :       List[str]=36  ,		UpperCamelCase       :       Tuple=3  ,		UpperCamelCase       :       int=4  ,		UpperCamelCase       :       str=37  ,		UpperCamelCase       :       List[Any]="gelu"  ,		UpperCamelCase       :       Dict=0.1  ,		UpperCamelCase       :       Dict=0.1  ,		UpperCamelCase       :       int=512  ,		UpperCamelCase       :       int=16  ,		UpperCamelCase       :       Tuple=2  ,		UpperCamelCase       :       List[str]=0.02  ,		UpperCamelCase       :       str=6  ,		UpperCamelCase       :       str=6  ,		UpperCamelCase       :       Optional[int]=3  ,		UpperCamelCase       :       Union[str, Any]=4  ,		UpperCamelCase       :       int=None  ,		UpperCamelCase       :       Optional[Any]=1000  ,		):

             '''simple docstring'''







             lowercase__      =					parent
             lowercase__      =					batch_size
             lowercase__      =					num_channels
             lowercase__      =					image_size
             lowercase__      =					patch_size
             lowercase__      =					text_seq_length
             lowercase__      =					is_training
             lowercase__      =					use_input_mask
             lowercase__      =					use_token_type_ids
             lowercase__      =					use_labels
             lowercase__      =					vocab_size
             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__      =					max_position_embeddings
             lowercase__      =					type_vocab_size
             lowercase__      =					type_sequence_label_size
             lowercase__      =					initializer_range
             lowercase__      =					coordinate_size
             lowercase__      =					shape_size
             lowercase__      =					num_labels
             lowercase__      =					num_choices
             lowercase__      =					scope
             lowercase__      =					range_bbox

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



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







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

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

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

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

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

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

             lowercase__      =					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  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					LayoutLMvaModel(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()

             # text + image
             lowercase__      =					model(UpperCamelCase  ,		pixel_values=UpperCamelCase					)
             lowercase__      =					model(
                 UpperCamelCase  ,		bbox=UpperCamelCase  ,		pixel_values=UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase  ,		bbox=UpperCamelCase  ,		pixel_values=UpperCamelCase  ,		token_type_ids=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase  ,		bbox=UpperCamelCase  ,		pixel_values=UpperCamelCase					)

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

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

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



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Tuple					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					LayoutLMvaForQuestionAnswering(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(
                 UpperCamelCase  ,		bbox=UpperCamelCase  ,		pixel_values=UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		token_type_ids=UpperCamelCase  ,		start_positions=UpperCamelCase  ,		end_positions=UpperCamelCase  ,		)
             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  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,
             )      =					config_and_inputs
             lowercase__      =					{
                 '''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     __lowerCAmelCase		(lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	int       =				False
      lowerCAmelCase__   :	List[Any]       =				False
      lowerCAmelCase__   :	Optional[int]       =				False

      lowerCAmelCase__   :	str       =				(
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__   :	int       =				(
          {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
          if is_torch_available()
          else {}
      )



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             return True



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					LayoutLMvaModelTester(self					)
             lowercase__      =					ConfigTester(self  ,		config_class=UpperCamelCase  ,		hidden_size=37					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       List[str]=False					):

             '''simple docstring'''







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

             return inputs_dict



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*UpperCamelCase					)






      @slow
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







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






def _SCREAMING_SNAKE_CASE ()			->			str:






       """simple docstring"""







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

@require_torch
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      @cached_property
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







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






      @slow
      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''					).to(UpperCamelCase					)

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

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

             # forward pass
             lowercase__      =					model(
                 input_ids=input_ids.to(UpperCamelCase					)  ,		bbox=bbox.to(UpperCamelCase					)  ,		pixel_values=pixel_values.to(UpperCamelCase					)  ,		)

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

             lowercase__      =					torch.tensor(
                 [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]					).to(UpperCamelCase					)

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








'''simple docstring'''

import unittest

import numpy as np

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A = None    ,					)			->			np.ndarray:






       """simple docstring"""







       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)
       lowercase__      =					np.shape(A			)

       if shape_a[0] != shape_b[0]:
              lowercase__      =					(
                  '''Expected the same number of rows for A and B. '''
                  f"Instead found A of size {shape_a} and B of size {shape_b}"
              )
              raise ValueError(A			)

       if shape_b[1] != shape_c[1]:
              lowercase__      =					(
                  '''Expected the same number of columns for B and C. '''
                  f"Instead found B of size {shape_b} and C of size {shape_c}"
              )
              raise ValueError(A			)

       lowercase__      =					pseudo_inv
       if a_inv is None:
              try:
                     lowercase__      =					np.linalg.inv(A			)
              except np.linalg.LinAlgError:
                     raise ValueError(
                         '''Input matrix A is not invertible. Cannot compute Schur complement.'''			)

       return mat_c - mat_b.T @ a_inv @ mat_b
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1], [6, 3]]					)

             lowercase__      =					schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

             lowercase__      =					np.block([[a, b], [b.T, c]]					)

             lowercase__      =					np.linalg.det(UpperCamelCase					)
             lowercase__      =					np.linalg.det(UpperCamelCase					)
             lowercase__      =					np.linalg.det(UpperCamelCase					)

             self.assertAlmostEqual(UpperCamelCase  ,		det_a * det_s					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1], [6, 3]]					)

             with self.assertRaises(UpperCamelCase					):
                    schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
             lowercase__      =					np.array([[0, 3], [3, 0], [2, 3]]					)
             lowercase__      =					np.array([[2, 1, 3], [6, 3, 5]]					)

             with self.assertRaises(UpperCamelCase					):
                    schur_complement(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()




