"""simple docstring"""




from ..utils import DummyObject, requires_backends
class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[str]		 =		["flax", "transformers"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Any		 =		["flax", "transformers"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Any		 =		["flax", "transformers"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )







class 						__A							(			metaclass=SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		["flax", "transformers"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )






     @classmethod
     def    __A  (		cls    ,					*a__    ,					**a__  ):
       requires_backends(cls    ,					["""flax""", """transformers"""]  )





"""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							:






     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__=0.0_2    ,					a__=6    ,					a__=6    ,					a__=3    ,					a__=4    ,					a__=None    ,					a__=1000    ,					):
       _lowerCAmelCase						:	Union[str, Any]        =					parent
       _lowerCAmelCase						:	Optional[Any]        =					batch_size
       _lowerCAmelCase						:	List[Any]        =					num_channels
       _lowerCAmelCase						:	str        =					image_size
       _lowerCAmelCase						:	str        =					patch_size
       _lowerCAmelCase						:	str        =					text_seq_length
       _lowerCAmelCase						:	List[Any]        =					is_training
       _lowerCAmelCase						:	List[str]        =					use_input_mask
       _lowerCAmelCase						:	List[Any]        =					use_token_type_ids
       _lowerCAmelCase						:	Optional[Any]        =					use_labels
       _lowerCAmelCase						:	Any        =					vocab_size
       _lowerCAmelCase						:	str        =					hidden_size
       _lowerCAmelCase						:	Dict        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	str        =					intermediate_size
       _lowerCAmelCase						:	str        =					hidden_act
       _lowerCAmelCase						:	Optional[Any]        =					hidden_dropout_prob
       _lowerCAmelCase						:	Any        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Tuple        =					max_position_embeddings
       _lowerCAmelCase						:	Dict        =					type_vocab_size
       _lowerCAmelCase						:	Tuple        =					type_sequence_label_size
       _lowerCAmelCase						:	Union[str, Any]        =					initializer_range
       _lowerCAmelCase						:	Dict        =					coordinate_size
       _lowerCAmelCase						:	Optional[int]        =					shape_size
       _lowerCAmelCase						:	str        =					num_labels
       _lowerCAmelCase						:	Optional[Any]        =					num_choices
       _lowerCAmelCase						:	str        =					scope
       _lowerCAmelCase						:	Dict        =					range_bbox

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.vocab_size  )

       _lowerCAmelCase						:	Tuple        =					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]:
             _lowerCAmelCase						:	Optional[Any]        =					bbox[i, j, 3]
             _lowerCAmelCase						:	List[str]        =					bbox[i, j, 1]
             _lowerCAmelCase						:	List[Any]        =					t
           if bbox[i, j, 2] < bbox[i, j, 0]:
             _lowerCAmelCase						:	int        =					bbox[i, j, 2]
             _lowerCAmelCase						:	Optional[int]        =					bbox[i, j, 0]
             _lowerCAmelCase						:	Optional[int]        =					t

       _lowerCAmelCase						:	Dict        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:	List[str]        =					None
       if self.use_input_mask:
         _lowerCAmelCase						:	int        =					random_attention_mask([self.batch_size, self.text_seq_length]  )

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

       _lowerCAmelCase						:	int        =					None
       _lowerCAmelCase						:	int        =					None
       if self.use_labels:
         _lowerCAmelCase						:	List[str]        =					ids_tensor([self.batch_size]    ,					self.type_sequence_label_size  )
         _lowerCAmelCase						:	str        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.num_labels  )

       _lowerCAmelCase						:	str        =					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__  ):
       _lowerCAmelCase						:	int        =					LayoutLMvaModel(config=a__  )
       model.to(a__  )
       model.eval()

       # text + image
       _lowerCAmelCase						:	Optional[Any]        =					model(a__    ,					pixel_values=a__  )
       _lowerCAmelCase						:	Any        =					model(
           a__    ,					bbox=a__    ,					pixel_values=a__    ,					attention_mask=a__    ,					token_type_ids=a__  )
       _lowerCAmelCase						:	List[Any]        =					model(a__    ,					bbox=a__    ,					pixel_values=a__    ,					token_type_ids=a__  )
       _lowerCAmelCase						:	Tuple        =					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
       _lowerCAmelCase						:	Dict        =					model(a__  )
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,					(self.batch_size, self.text_seq_length, self.hidden_size)  )

       # image only
       _lowerCAmelCase						:	Optional[Any]        =					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__  ):
       _lowerCAmelCase						:	int        =					self.num_labels
       _lowerCAmelCase						:	int        =					LayoutLMvaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Tuple        =					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__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.num_labels
       _lowerCAmelCase						:	str        =					LayoutLMvaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	List[str]        =					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__  ):
       _lowerCAmelCase						:	Dict        =					LayoutLMvaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	str        =					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  ):
       _lowerCAmelCase						:	List[str]        =					self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     
       )						:	Any        =					config_and_inputs
       _lowerCAmelCase						:	Union[str, Any]        =					{
           """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							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Tuple		 =		False
     _UpperCamelCase							:      Any		 =		False
     _UpperCamelCase							:      Any		 =		False

     _UpperCamelCase							:      int		 =		(
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:      Union[str, Any]		 =		(
         {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
       # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
       # the sequence dimension of the text embedding only.
       # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
       return True






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					LayoutLMvaModelTester(self  )
       _lowerCAmelCase						:	int        =					ConfigTester(self    ,					config_class=a__    ,					hidden_size=37  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__=False  ):
       _lowerCAmelCase						:	List[str]        =					copy.deepcopy(a__  )
       if model_class in get_values(a__  ):
         _lowerCAmelCase						:	Optional[int]        =					{
             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__  ):
           _lowerCAmelCase						:	List[Any]        =					torch.ones(self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in get_values(a__  ):
           _lowerCAmelCase						:	Optional[int]        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
           _lowerCAmelCase						:	Any        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in [
             *get_values(a__  ),
         ]:
           _lowerCAmelCase						:	Optional[int]        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in [
             *get_values(a__  ),
         ]:
           _lowerCAmelCase						:	int        =					torch.zeros(
               (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,					dtype=torch.long    ,					device=a__    ,					)

       return inputs_dict






     def    __A  (		self  ):
       self.config_tester.run_common_tests()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCAmelCase						:	int        =					type
         self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*a__  )






     @slow
     def    __A  (		self  ):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:	str        =					LayoutLMvaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Any        =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image






@require_torch
class 						__A							(			unittest.TestCase		):






     @cached_property
     def    __A  (		self  ):
       return LayoutLMvaImageProcessor(apply_ocr=a__  ) if is_vision_available() else None






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""  ).to(a__  )

       _lowerCAmelCase						:	Dict        =					self.default_image_processor
       _lowerCAmelCase						:	Optional[int]        =					prepare_img()
       _lowerCAmelCase						:	Dict        =					image_processor(images=a__    ,					return_tensors="""pt"""  ).pixel_values.to(a__  )

       _lowerCAmelCase						:	Optional[Any]        =					torch.tensor([[1, 2]]  )
       _lowerCAmelCase						:	Dict        =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]  ).unsqueeze(0  )

       # forward pass
       _lowerCAmelCase						:	str        =					model(
           input_ids=input_ids.to(a__  )    ,					bbox=bbox.to(a__  )    ,					pixel_values=pixel_values.to(a__  )    ,					)

       # verify the logits
       _lowerCAmelCase						:	Optional[int]        =					torch.Size((1, 199, 768)  )
       self.assertEqual(outputs.last_hidden_state.shape    ,					a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					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  )  )




