


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							_UpperCamelCase (						A,A,A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							List[str]    = StableDiffusionInstructPixaPixPipeline
   a_				:							Optional[int]    = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
   a_				:							Any    = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a_				:							Dict    = IMAGE_TO_IMAGE_IMAGE_PARAMS
   a_				:							Union[str, Any]    = IMAGE_TO_IMAGE_IMAGE_PARAMS

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         torch.manual_seed(0							)
         __lowerCamelCase     :      Tuple       						=						UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  sample_size=3_2       ,  in_channels=8       ,  out_channels=4       ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,  cross_attention_dim=3_2       ,  )
         __lowerCamelCase     :      List[Any]       						=						PNDMScheduler(skip_prk_steps=_lowerCamelCase							)
         torch.manual_seed(0							)
         __lowerCamelCase     :      Tuple       						=						AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,  latent_channels=4       ,  )
         torch.manual_seed(0							)
         __lowerCamelCase     :      List[str]       						=						CLIPTextConfig(
             bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  )
         __lowerCamelCase     :      Any       						=						CLIPTextModel(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

         __lowerCamelCase     :      Optional[Any]       						=						{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : List[str]=0							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						floats_tensor((1, 3, 3_2, 3_2)       ,  rng=random.Random(_lowerCamelCase							)							).to(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						image.cpu().permute(0       ,  2       ,  3       ,  1							)[0]
         __lowerCamelCase     :      List[Any]       						=						Image.fromarray(np.uinta(_lowerCamelCase							)							).convert("""RGB"""							)
         if str(_lowerCamelCase							).startswith("""mps"""							):
               __lowerCamelCase     :      List[Any]       						=						torch.manual_seed(_lowerCamelCase							)
         else:
               __lowerCamelCase     :      List[Any]       						=						torch.Generator(device=_lowerCamelCase							).manual_seed(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[Any]       						=						{
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """image_guidance_scale""": 1,
             """output_type""": """numpy""",
         }
         return inputs

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase     :      Dict       						=						self.get_dummy_components()
         __lowerCamelCase     :      Any       						=						StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						sd_pipe.to(_lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						self.get_dummy_inputs(_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						sd_pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      Any       						=						image[0, -3:, -3:, -1]
         assert image.shape == (1, 3_2, 3_2, 3)
         __lowerCamelCase     :      Tuple       						=						np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase     :      Dict       						=						self.get_dummy_components()
         __lowerCamelCase     :      int       						=						StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						sd_pipe.to(_lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      List[str]       						=						self.get_dummy_inputs(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						"""french fries"""
         __lowerCamelCase     :      Union[str, Any]       						=						sd_pipe(**_lowerCamelCase       ,  negative_prompt=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						output.images
         __lowerCamelCase     :      List[Any]       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         __lowerCamelCase     :      Optional[Any]       						=						np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase     :      Any       						=						self.get_dummy_components()
         __lowerCamelCase     :      Optional[Any]       						=						StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						sd_pipe.to(_lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						self.get_dummy_inputs(_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						[inputs["""prompt"""]] * 2

         __lowerCamelCase     :      List[str]       						=						np.array(inputs["""image"""]							).astype(np.floataa							) / 255.0
         __lowerCamelCase     :      Tuple       						=						torch.from_numpy(_lowerCamelCase							).unsqueeze(0							).to(_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						image / 2 + 0.5
         __lowerCamelCase     :      Any       						=						image.permute(0       ,  3       ,  1       ,  2							)
         __lowerCamelCase     :      List[Any]       						=						image.repeat(2       ,  1       ,  1       ,  1							)

         __lowerCamelCase     :      Optional[Any]       						=						sd_pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      int       						=						image[-1, -3:, -3:, -1]

         assert image.shape == (2, 3_2, 3_2, 3)
         __lowerCamelCase     :      List[Any]       						=						np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase     :      Dict       						=						self.get_dummy_components()
         __lowerCamelCase     :      List[str]       						=						EulerAncestralDiscreteScheduler(
             beta_start=0.00_085       ,  beta_end=0.012       ,  beta_schedule="""scaled_linear"""							)
         __lowerCamelCase     :      Optional[int]       						=						StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase							)
         __lowerCamelCase     :      Optional[Any]       						=						sd_pipe.to(_lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						self.get_dummy_inputs(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						sd_pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      str       						=						image[0, -3:, -3:, -1]

         __lowerCamelCase     :      int       						=						[round(_lowerCamelCase       ,  4							) for x in image_slice.flatten().tolist()]
         print(""",""".join([str(_lowerCamelCase							) for x in slice]							)							)

         assert image.shape == (1, 3_2, 3_2, 3)
         __lowerCamelCase     :      Optional[int]       						=						np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         super().test_inference_batch_single_identical(expected_max_diff=3E-3							)


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.get_dummy_components()
         __lowerCamelCase     :      Any       						=						StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						VaeImageProcessor(do_resize=_lowerCamelCase       ,  do_normalize=_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						pipe(**self.get_dummy_inputs_by_type(_lowerCamelCase       ,  input_image_type="""pt"""							)							)[0]

         __lowerCamelCase     :      Tuple       						=						components["""vae"""]
         __lowerCamelCase     :      Union[str, Any]       						=						self.get_dummy_inputs_by_type(_lowerCamelCase       ,  input_image_type="""pt"""							)

         for image_param in self.image_latents_params:
               if image_param in inputs.keys():
                     __lowerCamelCase     :      str       						=						vae.encode(inputs[image_param]							).latent_dist.mode()

         __lowerCamelCase     :      Union[str, Any]       						=						pipe(**_lowerCamelCase							)[0]

         __lowerCamelCase     :      int       						=						np.abs(out - out_latents_inputs							).max()
         self.assertLess(_lowerCamelCase       ,  1E-4       ,  """passing latents as image input generate different result from passing image"""							)



@slow
@require_torch_gpu
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : Tuple=0							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						torch.manual_seed(_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""							)
         __lowerCamelCase     :      Optional[Any]       						=						{
             """prompt""": """turn him into a cyborg""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """image_guidance_scale""": 1.0,
             """output_type""": """numpy""",
         }
         return inputs

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""       ,  safety_checker=_lowerCamelCase							)
         pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)
         pipe.enable_attention_slicing()

         __lowerCamelCase     :      List[Any]       						=						self.get_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      int       						=						image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      Optional[int]       						=						np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]							)

         assert np.abs(expected_slice - image_slice							).max() < 1E-3

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""       ,  safety_checker=_lowerCamelCase							)
         __lowerCamelCase     :      List[Any]       						=						LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)
         pipe.enable_attention_slicing()

         __lowerCamelCase     :      Tuple       						=						self.get_inputs()
         __lowerCamelCase     :      List[Any]       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      List[Any]       						=						image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      Dict       						=						np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]							)

         assert np.abs(expected_slice - image_slice							).max() < 1E-3

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""       ,  safety_checker=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						DDIMScheduler.from_config(pipe.scheduler.config							)
         pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)
         pipe.enable_attention_slicing()

         __lowerCamelCase     :      int       						=						self.get_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      int       						=						image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]							)

         assert np.abs(expected_slice - image_slice							).max() < 1E-3

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						0

         def callback_fn(_lowerCamelCase  : int       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : torch.FloatTensor							) -> None:
               __lowerCamelCase     :      List[str]       						=						True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                     __lowerCamelCase     :      Optional[Any]       						=						latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 6_4, 6_4)
                     __lowerCamelCase     :      Optional[Any]       						=						latents[0, -3:, -3:, -1]
                     __lowerCamelCase     :      List[Any]       						=						np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]							)

                     assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
               elif step == 2:
                     __lowerCamelCase     :      List[Any]       						=						latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 6_4, 6_4)
                     __lowerCamelCase     :      List[Any]       						=						latents[0, -3:, -3:, -1]
                     __lowerCamelCase     :      List[Any]       						=						np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]							)

                     assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2

         __lowerCamelCase     :      List[Any]       						=						False

         __lowerCamelCase     :      Optional[int]       						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""       ,  safety_checker=_lowerCamelCase       ,  torch_dtype=torch.floataa							)
         __lowerCamelCase     :      List[str]       						=						pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)
         pipe.enable_attention_slicing()

         __lowerCamelCase     :      List[str]       						=						self.get_inputs()
         pipe(**_lowerCamelCase       ,  callback=_lowerCamelCase       ,  callback_steps=1							)
         assert callback_fn.has_been_called
         assert number_of_steps == 3

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase     :      Dict       						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""       ,  safety_checker=_lowerCamelCase       ,  torch_dtype=torch.floataa							)
         __lowerCamelCase     :      Any       						=						pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)
         pipe.enable_attention_slicing(1							)
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase     :      Dict       						=						self.get_inputs()
         __lowerCamelCase     :      Tuple       						=						pipe(**_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 1_0**9


   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         __lowerCamelCase     :      List[Any]       						=						inputs["""image"""].resize((5_0_4, 5_0_4)							)

         __lowerCamelCase     :      Any       						=						"""timbrooks/instruct-pix2pix"""
         __lowerCamelCase     :      int       						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             _lowerCamelCase       ,  safety_checker=_lowerCamelCase       ,  )
         pipe.to(_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)
         pipe.enable_attention_slicing()

         __lowerCamelCase     :      str       						=						pipe(**_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						output.images[0]

         __lowerCamelCase     :      Union[str, Any]       						=						image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert image.shape == (5_0_4, 5_0_4, 3)
         __lowerCamelCase     :      Union[str, Any]       						=						np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase    :						Union[str, Any]     =							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2TokenizerFast']

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2FeatureExtractor']
    __UpperCamelCase    :						Union[str, Any]     =							['LayoutLMv2ImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Dict     =							[
        'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LayoutLMv2ForQuestionAnswering',
        'LayoutLMv2ForSequenceClassification',
        'LayoutLMv2ForTokenClassification',
        'LayoutLMv2Layer',
        'LayoutLMv2Model',
        'LayoutLMv2PreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaLayer,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



