



'''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     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=2					,  A=3					,  A=4					,  A=2					,  A=7					,  A=True					,  A=True					,  A=True					,  A=True					,  A=99					,  A=36					,  A=3					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=6					,  A=6					,  A=3					,  A=4					,  A=None					,  A=1_000					,  )       ->	List[str]:


      """simple docstring"""



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

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



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


      """simple docstring"""



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

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

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

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

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

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

      _a           =    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					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    LayoutLMvaModel(config=A			)
      model.to(A			)
      model.eval()

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

      # image only
      _a           =    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					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.num_labels
      _a           =    LayoutLMvaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    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					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.num_labels
      _a           =    LayoutLMvaForTokenClassification(config=A			)
      model.to(A			)
      model.eval()
      _a           =    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					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    LayoutLMvaForQuestionAnswering(config=A			)
      model.to(A			)
      model.eval()
      _a           =    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			)       ->	int:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''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		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			False
 __lowerCamelCase       :			int		=			False
 __lowerCamelCase       :			int		=			False

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



 def    a__    (self					,  A					,  A					,  A					,  A					,  A			)       ->	Dict:


      """simple docstring"""



      return True



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    LayoutLMvaModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self					,  A					,  A					,  A=False			)       ->	List[str]:


      """simple docstring"""



      _a           =    copy.deepcopy(A			)
      if model_class in get_values(A			):
           _a           =    {
               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			):
                _a           =    torch.ones(self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
           elif model_class in get_values(A			):
                _a           =    torch.zeros(
                    self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
                _a           =    torch.zeros(
                    self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
           elif model_class in [
               *get_values(A			),
           ]:
                _a           =    torch.zeros(
                    self.model_tester.batch_size					,  dtype=torch.long					,  device=A			)
           elif model_class in [
               *get_values(A			),
           ]:
                _a           =    torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.text_seq_length)					,  dtype=torch.long					,  device=A					,  )

      return inputs_dict



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



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



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A			)



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A			)



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A			)



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


      """simple docstring"""



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





def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    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[int]:


      """simple docstring"""



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



 @slow
 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''			).to(A			)

      _a           =    self.default_image_processor
      _a           =    prepare_img()
      _a           =    image_processor(images=A					,  return_tensors='''pt'''			).pixel_values.to(A			)

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

      # forward pass
      _a           =    model(
          input_ids=input_ids.to(A			)					,  bbox=bbox.to(A			)					,  pixel_values=pixel_values.to(A			)					,  )

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

      _a           =    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			)			)








'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyVaaInpaintPipeline
 A__				= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							int  )      ->   int:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Optional[int]													=    {
           'in_channels': 9,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'image',
           'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
           'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
           'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
           'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
           'layers_per_block': 1,
           'encoder_hid_dim': self.text_embedder_hidden_size,
           'encoder_hid_dim_type': 'image_proj',
           'cross_attention_dim': self.cross_attention_dim,
           'attention_head_dim': 4,
           'resnet_time_scale_shift': 'scale_shift',
           'class_embed_type': None,
       }

       __snake_case :   Optional[Any]													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
 @property
 def A_   (   self						:							str  )      ->   Union[str, Any]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   int													=    self.dummy_unet
       __snake_case :   Dict													=    self.dummy_movq

       __snake_case :   int													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   Optional[Any]													=    {
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							str      ,     __a						:							Union[str, Any]      ,     __a						:							List[str]=0  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Any													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   int													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(seed + 1  )  ).to(
           __a  )
       # create init_image
       __snake_case :   Union[str, Any]													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   List[Any]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   Optional[Any]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[str]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Optional[int]													=    torch.manual_seed(__a  )
       else:
             __snake_case :   List[Any]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   Optional[Any]													=    {
           'image': init_image,
           'mask_image': mask,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 2,
           'guidance_scale': 4.0,
           'output_type': 'np',
       }
       return inputs
 def A_   (   self						:							List[str]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Tuple													=    'cpu'

       __snake_case :   Dict													=    self.get_dummy_components()

       __snake_case :   List[Any]													=    self.pipeline_class(**__a  )
       __snake_case :   List[Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Union[str, Any]													=    output.images

       __snake_case :   Optional[int]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

       __snake_case :   Optional[int]													=    image[0, -3:, -3:, -1]
       __snake_case :   Tuple													=    image_from_tuple[0, -3:, -3:, -1]

       print(f'''image.shape {image.shape}'''  )

       assert image.shape == (1, 64, 64, 3)

       __snake_case :   Union[str, Any]													=    np.array(
           [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]  )

       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





 def A_   (   self						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





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







       '''simple docstring'''
       __snake_case :   Dict													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'  )

       __snake_case :   Optional[int]													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   int													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Optional[Any]													=    0

       __snake_case :   Union[str, Any]													=    'a hat'

       __snake_case :   Optional[int]													=    KandinskyVaaPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   Optional[Any]													=    KandinskyVaaInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Dict													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Optional[int]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   List[str]													=    output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__a      ,     __a  )



