





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				:







  def __init__(     self    :str       ,				_lowercase    :str       ,				_lowercase    :str=2       ,				_lowercase    :Optional[Any]=3       ,				_lowercase    :List[str]=4       ,				_lowercase    :str=2       ,				_lowercase    :Union[str, Any]=7       ,				_lowercase    :Any=True       ,				_lowercase    :Tuple=True       ,				_lowercase    :Union[str, Any]=True       ,				_lowercase    :Optional[int]=True       ,				_lowercase    :List[Any]=99       ,				_lowercase    :Optional[int]=36       ,				_lowercase    :List[Any]=3       ,				_lowercase    :List[str]=4       ,				_lowercase    :Union[str, Any]=37       ,				_lowercase    :int="gelu"       ,				_lowercase    :Union[str, Any]=0.1       ,				_lowercase    :Dict=0.1       ,				_lowercase    :str=5_12       ,				_lowercase    :Tuple=16       ,				_lowercase    :Dict=2       ,				_lowercase    :Optional[int]=0.02       ,				_lowercase    :List[Any]=6       ,				_lowercase    :Optional[int]=6       ,				_lowercase    :int=3       ,				_lowercase    :int=4       ,				_lowercase    :Optional[int]=None       ,				_lowercase    :Tuple=10_00       ,				):






     '''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    :Dict   ):






     '''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    :Optional[Any]       ,				_lowercase    :Optional[int]       ,				_lowercase    :Optional[int]       ,				_lowercase    :Dict       ,				_lowercase    :List[str]       ,				_lowercase    :str       ,				_lowercase    :List[Any]       ,				_lowercase    :int       ,				_lowercase    :Optional[Any]   ):






     '''simple docstring'''





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

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

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

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

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







  def     UpperCAmelCase	(     self    :Any       ,				_lowercase    :Union[str, Any]       ,				_lowercase    :Optional[int]       ,				_lowercase    :List[Any]       ,				_lowercase    :Any       ,				_lowercase    :Optional[int]       ,				_lowercase    :Optional[int]       ,				_lowercase    :Any       ,				_lowercase    :Optional[int]   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :Any       ,				_lowercase    :int       ,				_lowercase    :str       ,				_lowercase    :Optional[int]       ,				_lowercase    :Tuple       ,				_lowercase    :List[str]       ,				_lowercase    :str       ,				_lowercase    :Optional[int]       ,				_lowercase    :Dict   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :Any       ,				_lowercase    :Optional[int]       ,				_lowercase    :Tuple       ,				_lowercase    :List[Any]       ,				_lowercase    :int       ,				_lowercase    :Optional[Any]       ,				_lowercase    :str       ,				_lowercase    :Dict       ,				_lowercase    :List[Any]   ):






     '''simple docstring'''





     lowercase__					  =    LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
     model.to(SCREAMING_SNAKE_CASE_   )
     model.eval()
     lowercase__					  =    model(
         SCREAMING_SNAKE_CASE_       ,				bbox=SCREAMING_SNAKE_CASE_       ,				pixel_values=SCREAMING_SNAKE_CASE_       ,				attention_mask=SCREAMING_SNAKE_CASE_       ,				token_type_ids=SCREAMING_SNAKE_CASE_       ,				start_positions=SCREAMING_SNAKE_CASE_       ,				end_positions=SCREAMING_SNAKE_CASE_       ,				)
     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    :str   ):






     '''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   ):
  __lowerCamelCase						=     False
  __lowerCamelCase						=     False
  __lowerCamelCase						=     False

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







  def     UpperCAmelCase	(     self    :Optional[Any]       ,				_lowercase    :Dict       ,				_lowercase    :List[Any]       ,				_lowercase    :Optional[Any]       ,				_lowercase    :Any       ,				_lowercase    :Any   ):






     '''simple docstring'''





     return True







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :List[Any]       ,				_lowercase    :Tuple       ,				_lowercase    :Optional[Any]       ,				_lowercase    :int=False   ):






     '''simple docstring'''





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

     return inputs_dict







  def     UpperCAmelCase	(     self    :List[str]   ):






     '''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(*SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :Optional[int]   ):






     '''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(*SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :List[str]   ):






     '''simple docstring'''





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







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





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







  @slow
  def     UpperCAmelCase	(     self    :Optional[int]   ):






     '''simple docstring'''





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


def _A  (       ):
   lowercase__					  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
   return image







@require_torch
class    lowerCAmelCase				(  unittest.TestCase   ):







  @cached_property
  def     UpperCAmelCase	(     self    :Tuple   ):






     '''simple docstring'''





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







  @slow
  def     UpperCAmelCase	(     self    :Dict   ):






     '''simple docstring'''





     lowercase__					  =    LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"   ).to(SCREAMING_SNAKE_CASE_   )

     lowercase__					  =    self.default_image_processor
     lowercase__					  =    prepare_img()
     lowercase__					  =    image_processor(images=SCREAMING_SNAKE_CASE_       ,				return_tensors="pt"   ).pixel_values.to(SCREAMING_SNAKE_CASE_   )

     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(SCREAMING_SNAKE_CASE_   )       ,				bbox=bbox.to(SCREAMING_SNAKE_CASE_   )       ,				pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE_   )       ,				)

     # verify the logits
     lowercase__					  =    torch.Size((1, 1_99, 7_68)   )
     self.assertEqual(outputs.last_hidden_state.shape       ,				SCREAMING_SNAKE_CASE_   )

     lowercase__					  =    torch.tensor(
         [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]   ).to(SCREAMING_SNAKE_CASE_   )

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








"""simple docstring"""




from __future__ import annotations



def 							_lowerCamelCase			(			__a,				__a = None     ):
		SCREAMING_SNAKE_CASE_    			=			word_bank or []
		# create a table
		SCREAMING_SNAKE_CASE_    			=			len(__a     ) + 1

		SCREAMING_SNAKE_CASE_    			=			[]
		for _ in range(__a     ):
				table.append([]     )
		# seed value
		SCREAMING_SNAKE_CASE_    			=			[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(__a     ):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(__a     )] == word:
										SCREAMING_SNAKE_CASE_    			=			[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(__a     )] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(__a     )]:
				combination.reverse()

		return table[len(__a     )]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)