




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      :						Tuple		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]=2		,							A_      :						Optional[Any]=3		,							A_      :						List[str]=4		,							A_      :						Any=2		,							A_      :						List[Any]=7		,							A_      :						Optional[int]=True		,							A_      :						Union[str, Any]=True		,							A_      :						Dict=True		,							A_      :						Optional[int]=True		,							A_      :						Dict=99		,							A_      :						Any=36		,							A_      :						str=3		,							A_      :						str=4		,							A_      :						Optional[int]=37		,							A_      :						Tuple="gelu"		,							A_      :						Union[str, Any]=0.1		,							A_      :						List[str]=0.1		,							A_      :						str=512		,							A_      :						Union[str, Any]=16		,							A_      :						int=2		,							A_      :						Dict=0.02		,							A_      :						Optional[int]=6		,							A_      :						List[str]=6		,							A_      :						List[Any]=3		,							A_      :						Any=4		,							A_      :						List[str]=None		,							A_      :						Optional[Any]=1000		,							)     ->			List[Any]:

       """simple docstring"""






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

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

 def        a__				(       self      :						Union[str, Any]						)     ->			int:

       """simple docstring"""






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

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

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

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

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

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

       lowerCamelCase_	  =					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      :						Union[str, Any]		,							A_      :						Tuple		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						Tuple		,							A_      :						List[Any]		,							A_      :						List[str]		,							A_      :						Any						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					LayoutLMvaModel(config=A_						)
       model.to(A_						)
       model.eval()

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

       # image only
       lowerCamelCase_	  =					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      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						List[Any]		,							A_      :						Any		,							A_      :						List[Any]		,							A_      :						int		,							A_      :						List[str]		,							A_      :						List[Any]		,							A_      :						Optional[Any]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					LayoutLMvaForSequenceClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					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      :						Dict		,							A_      :						List[Any]		,							A_      :						Optional[Any]		,							A_      :						Optional[Any]		,							A_      :						List[Any]		,							A_      :						Union[str, Any]		,							A_      :						Tuple		,							A_      :						Dict		,							A_      :						Union[str, Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					LayoutLMvaForTokenClassification(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					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      :						Dict		,							A_      :						str		,							A_      :						Any		,							A_      :						int		,							A_      :						Optional[Any]		,							A_      :						List[str]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						Dict						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					LayoutLMvaForQuestionAnswering(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					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      :						Optional[Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       
       )	  =					config_and_inputs
       lowerCamelCase_	  =					{
           '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(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False

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

 def        a__				(       self      :						Dict		,							A_      :						str		,							A_      :						int		,							A_      :						Tuple		,							A_      :						Tuple		,							A_      :						Optional[int]						)     ->			Tuple:

       """simple docstring"""






       return True

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

       """simple docstring"""






       lowerCamelCase_	  =					LayoutLMvaModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=A_		,							hidden_size=37						)

 def        a__				(       self      :						Dict		,							A_      :						int		,							A_      :						Dict		,							A_      :						Union[str, Any]=False						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					copy.deepcopy(A_						)
       if model_class in get_values(A_						):
             lowerCamelCase_	  =					{
                 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_	  =					torch.ones(self.model_tester.batch_size		,							dtype=torch.long		,							device=A_						)
             elif model_class in get_values(A_						):
                   lowerCamelCase_	  =					torch.zeros(
                       self.model_tester.batch_size		,							dtype=torch.long		,							device=A_						)
                   lowerCamelCase_	  =					torch.zeros(
                       self.model_tester.batch_size		,							dtype=torch.long		,							device=A_						)
             elif model_class in [
                 *get_values(A_						),
             ]:
                   lowerCamelCase_	  =					torch.zeros(
                       self.model_tester.batch_size		,							dtype=torch.long		,							device=A_						)
             elif model_class in [
                 *get_values(A_						),
             ]:
                   lowerCamelCase_	  =					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						)     ->			List[Any]:

       """simple docstring"""






       self.config_tester.run_common_tests()

 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_						)

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

       """simple docstring"""






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

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_						)





 @slow
 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






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

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					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[Any]						)     ->			str:

       """simple docstring"""






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





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

       """simple docstring"""






       lowerCamelCase_	  =					LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'						).to(A_						)

       lowerCamelCase_	  =					self.default_image_processor
       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=A_		,							return_tensors='pt'						).pixel_values.to(A_						)

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

       # forward pass
       lowerCamelCase_	  =					model(
           input_ids=input_ids.to(A_						)		,							bbox=bbox.to(A_						)		,							pixel_values=pixel_values.to(A_						)		,							)

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

       lowerCamelCase_	  =					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						)						)









def     _SCREAMING_SNAKE_CASE    (							lowercase			: list[int]			,							lowercase			: list[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					len(lowercase  )
      print('The following activities are selected:'  )

      # The first activity is always selected
      lowerCamelCase_	  =					0
      print(lowercase			,							end=','  )

      # Consider rest of the activities
      for j in range(lowercase  ):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase			,							end=','  )
                  lowerCamelCase_	  =					j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase       :	Tuple									=     [1, 3, 0, 5, 8, 5]
 lowerCamelCase       :	int									=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)



