


"""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				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       List[str]=2				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=3				,       SCREAMING_SNAKE_CASE__				:       Tuple=4				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=2				,       SCREAMING_SNAKE_CASE__				:       Tuple=7				,       SCREAMING_SNAKE_CASE__				:       Tuple=True				,       SCREAMING_SNAKE_CASE__				:       Any=True				,       SCREAMING_SNAKE_CASE__				:       List[Any]=True				,       SCREAMING_SNAKE_CASE__				:       List[Any]=True				,       SCREAMING_SNAKE_CASE__				:       List[str]=99				,       SCREAMING_SNAKE_CASE__				:       int=36				,       SCREAMING_SNAKE_CASE__				:       Any=3				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=4				,       SCREAMING_SNAKE_CASE__				:       Tuple=37				,       SCREAMING_SNAKE_CASE__				:       Dict="gelu"				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=0.1				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=0.1				,       SCREAMING_SNAKE_CASE__				:       Tuple=5_12				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=16				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=2				,       SCREAMING_SNAKE_CASE__				:       int=0.02				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=6				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=6				,       SCREAMING_SNAKE_CASE__				:       str=3				,       SCREAMING_SNAKE_CASE__				:       str=4				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=None				,       SCREAMING_SNAKE_CASE__				:       List[Any]=10_00				,       ):
          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   _UpperCamelCase							(			self				:       Optional[Any]					):
          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   _UpperCamelCase							(			self				:       Any				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Optional[int]				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]					):
          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   _UpperCamelCase							(			self				:       int				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Optional[int]					):
          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   _UpperCamelCase							(			self				:       int				,       SCREAMING_SNAKE_CASE__				:       Optional[int]				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Optional[int]				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       str				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]					):
          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   _UpperCamelCase							(			self				:       str				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Optional[int]				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]					):
          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   _UpperCamelCase							(			self				:       str					):
          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   (   _a		,		_a		,		unittest.TestCase			):
    a_     :					int   							=     False
    a_     :					Tuple   							=     False
    a_     :					Any   							=     False

    a_     :					Any   							=     (
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    a_     :					Optional[Any]   							=     (
        {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
        if is_torch_available()
        else {}
    )
    def   _UpperCamelCase							(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Optional[int]				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       List[str]					):
          return True
    def   _UpperCamelCase							(			self				:       Optional[int]					):
          lowerCamelCase__							     =		LayoutLMvaModelTester(self					)
          lowerCamelCase__							     =		ConfigTester(self				,       config_class=A_				,       hidden_size=37					)
    def   _UpperCamelCase							(			self				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Dict=False					):
          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   _UpperCamelCase							(			self				:       int					):
          self.config_tester.run_common_tests()
    def   _UpperCamelCase							(			self				:       str					):
          lowerCamelCase__							     =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A_					)
    def   _UpperCamelCase							(			self				:       str					):
          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   _UpperCamelCase							(			self				:       List[Any]					):
          lowerCamelCase__							     =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A_					)
    def   _UpperCamelCase							(			self				:       Any					):
          lowerCamelCase__							     =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A_					)
    def   _UpperCamelCase							(			self				:       Dict					):
          lowerCamelCase__							     =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A_					)


    @slow
    def   _UpperCamelCase							(			self				:       List[str]					):


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





def 	snake_case    (					)->		Dict:
      '''simple docstring'''


      lowerCamelCase__							     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image






@require_torch
class 	_a   (   unittest.TestCase			):
    @cached_property
    def   _UpperCamelCase							(			self				:       List[str]					):
          return LayoutLMvaImageProcessor(apply_ocr=A_					) if is_vision_available() else None


    @slow
    def   _UpperCamelCase							(			self				:       str					):
          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, 1_99, 7_68)					)
          self.assertEqual(outputs.last_hidden_state.shape				,       A_					)

          lowerCamelCase__							     =		torch.tensor(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]					).to(A_					)

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







"""simple docstring"""

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def lowerCAmelCase_   (					UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						cva.getAffineTransform(UpperCamelCase__					, UpperCamelCase__			)
 return cva.warpAffine(UpperCamelCase__					, UpperCamelCase__					, (rows, cols)			)


if __name__ == "__main__":
       # read original image
       UpperCAmelCase__					=cva.imread(
           str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
       )
       # turn image in gray scale value
       UpperCAmelCase__					=cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       UpperCAmelCase__				,   UpperCAmelCase__					=gray_img.shape

       # set different points to rotate image
       UpperCAmelCase__					=np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       UpperCAmelCase__					=np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       UpperCAmelCase__					=np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       UpperCAmelCase__					=np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       UpperCAmelCase__					=[
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       UpperCAmelCase__					=plt.figure(1)
       UpperCAmelCase__					=["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
              plt.title(titles[i])
              plt.axis("off")
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()
