

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       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							List[Any]=2				,  _UpperCamelCase					:							Tuple=3				,  _UpperCamelCase					:							Union[str, Any]=4				,  _UpperCamelCase					:							Optional[Any]=2				,  _UpperCamelCase					:							Any=7				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							Tuple=True				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							Any=9_9				,  _UpperCamelCase					:							int=3_6				,  _UpperCamelCase					:							str=3				,  _UpperCamelCase					:							str=4				,  _UpperCamelCase					:							List[str]=3_7				,  _UpperCamelCase					:							Optional[int]="gelu"				,  _UpperCamelCase					:							Dict=0.1				,  _UpperCamelCase					:							Optional[int]=0.1				,  _UpperCamelCase					:							Dict=5_1_2				,  _UpperCamelCase					:							str=1_6				,  _UpperCamelCase					:							Optional[int]=2				,  _UpperCamelCase					:							int=0.02				,  _UpperCamelCase					:							Any=6				,  _UpperCamelCase					:							Optional[Any]=6				,  _UpperCamelCase					:							Optional[int]=3				,  _UpperCamelCase					:							Any=4				,  _UpperCamelCase					:							List[Any]=None				,  _UpperCamelCase					:							List[Any]=1_0_0_0				,  ) ->Dict:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      num_channels
									snake_case_  =      image_size
									snake_case_  =      patch_size
									snake_case_  =      text_seq_length
									snake_case_  =      is_training
									snake_case_  =      use_input_mask
									snake_case_  =      use_token_type_ids
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      intermediate_size
									snake_case_  =      hidden_act
									snake_case_  =      hidden_dropout_prob
									snake_case_  =      attention_probs_dropout_prob
									snake_case_  =      max_position_embeddings
									snake_case_  =      type_vocab_size
									snake_case_  =      type_sequence_label_size
									snake_case_  =      initializer_range
									snake_case_  =      coordinate_size
									snake_case_  =      shape_size
									snake_case_  =      num_labels
									snake_case_  =      num_choices
									snake_case_  =      scope
									snake_case_  =      range_bbox

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







			def 							snake_case__(	self					:							str				) ->Optional[Any]:
									snake_case_  =      ids_tensor([self.batch_size, self.text_seq_length]				,  self.vocab_size				)

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

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

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

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

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

									snake_case_  =      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 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							int				) ->Any:
									snake_case_  =      LayoutLMvaModel(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()

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

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







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str				) ->Dict:
									snake_case_  =      self.num_labels
									snake_case_  =      LayoutLMvaForSequenceClassification(_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      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 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]				) ->List[Any]:
									snake_case_  =      self.num_labels
									snake_case_  =      LayoutLMvaForTokenClassification(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      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 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Any				) ->Any:
									snake_case_  =      LayoutLMvaForQuestionAnswering(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      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 							snake_case__(	self					:							List[str]				) ->List[str]:
									snake_case_  =      self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       
									)  =      config_and_inputs
									snake_case_  =      {
									    '''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       snake_case_  (						__A      ,			__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Union[str, Any]		     =							False
			SCREAMING_SNAKE_CASE	:  Any		     =							False
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							False

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







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							List[str]				) ->int:
									# `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 							snake_case__(	self					:							Union[str, Any]				) ->Any:
									snake_case_  =      LayoutLMvaModelTester(self				)
									snake_case_  =      ConfigTester(self				,  config_class=_UpperCamelCase				,  hidden_size=3_7				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							int=False				) ->List[str]:
									snake_case_  =      copy.deepcopy(_UpperCamelCase				)
									if model_class in get_values(_UpperCamelCase				):
															snake_case_  =      {
															    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				):
																					snake_case_  =      torch.ones(self.model_tester.batch_size				,  dtype=torch.long				,  device=_UpperCamelCase				)
															elif model_class in get_values(_UpperCamelCase				):
																					snake_case_  =      torch.zeros(
																					    self.model_tester.batch_size				,  dtype=torch.long				,  device=_UpperCamelCase				)
																					snake_case_  =      torch.zeros(
																					    self.model_tester.batch_size				,  dtype=torch.long				,  device=_UpperCamelCase				)
															elif model_class in [
															    *get_values(_UpperCamelCase				),
															]:
																					snake_case_  =      torch.zeros(
																					    self.model_tester.batch_size				,  dtype=torch.long				,  device=_UpperCamelCase				)
															elif model_class in [
															    *get_values(_UpperCamelCase				),
															]:
																					snake_case_  =      torch.zeros(
																					    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,  dtype=torch.long				,  device=_UpperCamelCase				,  )

									return inputs_dict







			def 							snake_case__(	self					:							Tuple				) ->List[str]:
									self.config_tester.run_common_tests()







			def 							snake_case__(	self					:							Any				) ->Optional[int]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase				)







			def 							snake_case__(	self					:							int				) ->Optional[int]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															snake_case_  =      type
															self.model_tester.create_and_check_model(*_UpperCamelCase				)







			def 							snake_case__(	self					:							Union[str, Any]				) ->str:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[Any]				) ->Any:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase				)







			def 							snake_case__(	self					:							str				) ->Optional[Any]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase				)







			@slow
			def 							snake_case__(	self					:							Tuple				) ->Optional[Any]:
									for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_  =      LayoutLMvaModel.from_pretrained(_UpperCamelCase				)
															self.assertIsNotNone(_UpperCamelCase				)






def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
						return image





@require_torch
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			@cached_property
			def 							snake_case__(	self					:							Optional[int]				) ->str:
									return LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase				) if is_vision_available() else None







			@slow
			def 							snake_case__(	self					:							Dict				) ->int:
									snake_case_  =      LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''				).to(_UpperCamelCase				)

									snake_case_  =      self.default_image_processor
									snake_case_  =      prepare_img()
									snake_case_  =      image_processor(images=_UpperCamelCase				,  return_tensors='''pt'''				).pixel_values.to(_UpperCamelCase				)

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

									# forward pass
									snake_case_  =      model(
									    input_ids=input_ids.to(_UpperCamelCase				)				,  bbox=bbox.to(_UpperCamelCase				)				,  pixel_values=pixel_values.to(_UpperCamelCase				)				,  )

									# verify the logits
									snake_case_  =      torch.Size((1, 1_9_9, 7_6_8)				)
									self.assertEqual(outputs.last_hidden_state.shape				,  _UpperCamelCase				)

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

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




"""simple docstring"""






import operator




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None	)						->				list:
  _snake_case   	=      operator.lt if reverse else operator.gt
  _snake_case   	=      solution or []

  if not arr:
    return solution

  _snake_case   	=      [arr.pop(0	)]
  for i, item in enumerate(lowerCAmelCase_	):
    if _operator(lowerCAmelCase_  ,						sublist[-1]	):
      sublist.append(lowerCAmelCase_	)
      arr.pop(lowerCAmelCase_	)

    #  merging sublist into solution list
  if not solution:
    solution.extend(lowerCAmelCase_	)
  else:
    while sublist:
      _snake_case   	=      sublist.pop(0	)
      for i, xx in enumerate(lowerCAmelCase_	):
        if not _operator(lowerCAmelCase_  ,						lowerCAmelCase_	):
          solution.insert(lowerCAmelCase_  ,						lowerCAmelCase_	)
          break
      else:
        solution.append(lowerCAmelCase_	)

  strand_sort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





