

'''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			,				_UpperCamelCase			,				_UpperCamelCase=2			,				_UpperCamelCase=3			,				_UpperCamelCase=4			,				_UpperCamelCase=2			,				_UpperCamelCase=7			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=99			,				_UpperCamelCase=36			,				_UpperCamelCase=3			,				_UpperCamelCase=4			,				_UpperCamelCase=37			,				_UpperCamelCase="gelu"			,				_UpperCamelCase=0.1			,				_UpperCamelCase=0.1			,				_UpperCamelCase=512			,				_UpperCamelCase=16			,				_UpperCamelCase=2			,				_UpperCamelCase=0.0_2			,				_UpperCamelCase=6			,				_UpperCamelCase=6			,				_UpperCamelCase=3			,				_UpperCamelCase=4			,				_UpperCamelCase=None			,				_UpperCamelCase=1000			,				):





      """simple docstring"""





      _lowercase			:      List[str]			     =   parent
      _lowercase			:      Dict			     =   batch_size
      _lowercase			:      List[Any]			     =   num_channels
      _lowercase			:      int			     =   image_size
      _lowercase			:      str			     =   patch_size
      _lowercase			:      Optional[Any]			     =   text_seq_length
      _lowercase			:      str			     =   is_training
      _lowercase			:      List[str]			     =   use_input_mask
      _lowercase			:      Any			     =   use_token_type_ids
      _lowercase			:      List[Any]			     =   use_labels
      _lowercase			:      List[Any]			     =   vocab_size
      _lowercase			:      List[Any]			     =   hidden_size
      _lowercase			:      Any			     =   num_hidden_layers
      _lowercase			:      List[str]			     =   num_attention_heads
      _lowercase			:      Tuple			     =   intermediate_size
      _lowercase			:      Any			     =   hidden_act
      _lowercase			:      Optional[int]			     =   hidden_dropout_prob
      _lowercase			:      Optional[int]			     =   attention_probs_dropout_prob
      _lowercase			:      List[Any]			     =   max_position_embeddings
      _lowercase			:      Dict			     =   type_vocab_size
      _lowercase			:      Optional[int]			     =   type_sequence_label_size
      _lowercase			:      List[Any]			     =   initializer_range
      _lowercase			:      Any			     =   coordinate_size
      _lowercase			:      Optional[Any]			     =   shape_size
      _lowercase			:      Any			     =   num_labels
      _lowercase			:      Union[str, Any]			     =   num_choices
      _lowercase			:      Any			     =   scope
      _lowercase			:      List[Any]			     =   range_bbox

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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      str			     =   ids_tensor([self.batch_size, self.text_seq_length]			,				self.vocab_size  )

      _lowercase			:      Any			     =   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			:      int			     =   bbox[i, j, 3]
               _lowercase			:      Union[str, Any]			     =   bbox[i, j, 1]
               _lowercase			:      List[str]			     =   t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               _lowercase			:      List[str]			     =   bbox[i, j, 2]
               _lowercase			:      Union[str, Any]			     =   bbox[i, j, 0]
               _lowercase			:      List[Any]			     =   t

      _lowercase			:      Tuple			     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

      _lowercase			:      List[Any]			     =   None
      if self.use_input_mask:
         _lowercase			:      Optional[Any]			     =   random_attention_mask([self.batch_size, self.text_seq_length]  )

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

      _lowercase			:      Union[str, Any]			     =   None
      _lowercase			:      Tuple			     =   None
      if self.use_labels:
         _lowercase			:      List[str]			     =   ids_tensor([self.batch_size]			,				self.type_sequence_label_size  )
         _lowercase			:      str			     =   ids_tensor([self.batch_size, self.text_seq_length]			,				self.num_labels  )

      _lowercase			:      Tuple			     =   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 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   LayoutLMvaModel(config=UpperCamelCase_  )
      model.to(UpperCamelCase_  )
      model.eval()

      # text + image
      _lowercase			:      List[str]			     =   model(UpperCamelCase_			,				pixel_values=UpperCamelCase_  )
      _lowercase			:      int			     =   model(
          UpperCamelCase_			,				bbox=UpperCamelCase_			,				pixel_values=UpperCamelCase_			,				attention_mask=UpperCamelCase_			,				token_type_ids=UpperCamelCase_  )
      _lowercase			:      Union[str, Any]			     =   model(UpperCamelCase_			,				bbox=UpperCamelCase_			,				pixel_values=UpperCamelCase_			,				token_type_ids=UpperCamelCase_  )
      _lowercase			:      str			     =   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			:      Dict			     =   model(UpperCamelCase_  )
      self.parent.assertEqual(
          result.last_hidden_state.shape			,				(self.batch_size, self.text_seq_length, self.hidden_size)  )

      # image only
      _lowercase			:      List[str]			     =   model(pixel_values=UpperCamelCase_  )
      self.parent.assertEqual(
          result.last_hidden_state.shape			,				(self.batch_size, self.image_seq_length, self.hidden_size)  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[Any]			     =   self.num_labels
      _lowercase			:      Tuple			     =   LayoutLMvaForSequenceClassification(UpperCamelCase_  )
      model.to(UpperCamelCase_  )
      model.eval()
      _lowercase			:      int			     =   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 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   self.num_labels
      _lowercase			:      int			     =   LayoutLMvaForTokenClassification(config=UpperCamelCase_  )
      model.to(UpperCamelCase_  )
      model.eval()
      _lowercase			:      Tuple			     =   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 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   LayoutLMvaForQuestionAnswering(config=UpperCamelCase_  )
      model.to(UpperCamelCase_  )
      model.eval()
      _lowercase			:      int			     =   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 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Tuple			     =   self.prepare_config_and_inputs()
      (
          _lowercase				
      )			:      Any			     =   config_and_inputs
      _lowercase			:      Optional[int]			     =   {
          '''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  ):
   _SCREAMING_SNAKE_CASE    :     str	       =							False
   _SCREAMING_SNAKE_CASE    :     Tuple	       =							False
   _SCREAMING_SNAKE_CASE    :     Optional[int]	       =							False

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


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      return True


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      str			     =   LayoutLMvaModelTester(self  )
      _lowercase			:      Optional[Any]			     =   ConfigTester(self			,				config_class=UpperCamelCase_			,				hidden_size=37  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase=False  ):





      """simple docstring"""





      _lowercase			:      int			     =   copy.deepcopy(UpperCamelCase_  )
      if model_class in get_values(UpperCamelCase_  ):
         _lowercase			:      Dict			     =   {
             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			:      Optional[int]			     =   torch.ones(self.model_tester.batch_size			,				dtype=torch.long			,				device=UpperCamelCase_  )
         elif model_class in get_values(UpperCamelCase_  ):
            _lowercase			:      int			     =   torch.zeros(
                self.model_tester.batch_size			,				dtype=torch.long			,				device=UpperCamelCase_  )
            _lowercase			:      int			     =   torch.zeros(
                self.model_tester.batch_size			,				dtype=torch.long			,				device=UpperCamelCase_  )
         elif model_class in [
             *get_values(UpperCamelCase_  ),
         ]:
            _lowercase			:      Union[str, Any]			     =   torch.zeros(
                self.model_tester.batch_size			,				dtype=torch.long			,				device=UpperCamelCase_  )
         elif model_class in [
             *get_values(UpperCamelCase_  ),
         ]:
            _lowercase			:      int			     =   torch.zeros(
                (self.model_tester.batch_size, self.model_tester.text_seq_length)			,				dtype=torch.long			,				device=UpperCamelCase_			,				)

      return inputs_dict


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase_  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowercase			:      Optional[Any]			     =   type
         self.model_tester.create_and_check_model(*UpperCamelCase_  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Optional[Any]			     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_  )






   @slow
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowercase			:      Optional[Any]			     =   LayoutLMvaModel.from_pretrained(UpperCamelCase_  )
         self.assertIsNotNone(UpperCamelCase_  )







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


@require_torch
class    a__  (							unittest.TestCase  ):


   @cached_property
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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






   @slow
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[Any]			     =   LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"  ).to(UpperCamelCase_  )

      _lowercase			:      List[str]			     =   self.default_image_processor
      _lowercase			:      Optional[Any]			     =   prepare_img()
      _lowercase			:      List[str]			     =   image_processor(images=UpperCamelCase_			,				return_tensors="pt"  ).pixel_values.to(UpperCamelCase_  )

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

      # forward pass
      _lowercase			:      str			     =   model(
          input_ids=input_ids.to(UpperCamelCase_  )			,				bbox=bbox.to(UpperCamelCase_  )			,				pixel_values=pixel_values.to(UpperCamelCase_  )			,				)

      # verify the logits
      _lowercase			:      Tuple			     =   torch.Size((1, 199, 768)  )
      self.assertEqual(outputs.last_hidden_state.shape			,				UpperCamelCase_  )

      _lowercase			:      Tuple			     =   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(UpperCamelCase_  )

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










'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


def 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp