





from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
     from transformers import (
         TFBertModel,
         TFCLIPVisionModel,
         TFDeiTModel,
         TFRobertaModel,
         TFVisionTextDualEncoderModel,
         TFViTModel,
         VisionTextDualEncoderConfig,
     )

if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   if isinstance(lowerCAmelCase_,	collections.abc.Iterable	):
      return x
   return (x, x)



@require_tf
class 				_UpperCAmelCase    :

  """simple docstring"""


  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		str     )							->						Optional[int]:
     pass






  def 				lowercase		(				self :		str     )							->						List[Any]:
     pass






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






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[Any]					,  lowerCAmelCase_ :		List[str]=None					,  **lowerCAmelCase_ :		Dict     )							->						str:
     __lowerCAmelCase								=	VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	TFVisionTextDualEncoderModel(lowerCAmelCase_     )

     __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )

     self.assertEqual(output['text_embeds'].shape					,  (input_ids.shape[0], config.projection_dim)     )
     self.assertEqual(output['image_embeds'].shape					,  (pixel_values.shape[0], config.projection_dim)     )






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[Any]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Any=None					,  **lowerCAmelCase_ :		List[Any]     )							->						Optional[Any]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_vision_text_model(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_					,  text_model=lowerCAmelCase_     )

     __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )

     self.assertEqual(output['text_embeds'].shape					,  (input_ids.shape[0], model.config.projection_dim)     )
     self.assertEqual(output['image_embeds'].shape					,  (pixel_values.shape[0], model.config.projection_dim)     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		List[Any]					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		List[Any]=None					,  **lowerCAmelCase_ :		List[str]     )							->						Union[str, Any]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_vision_text_model(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	{'vision_model': vision_model, 'text_model': text_model}
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_     )

     __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )

     self.assertEqual(output['text_embeds'].shape					,  (input_ids.shape[0], model.config.projection_dim)     )
     self.assertEqual(output['image_embeds'].shape					,  (pixel_values.shape[0], model.config.projection_dim)     )






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		int=None					,  **lowerCAmelCase_ :		Optional[Any]     )							->						int:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_vision_text_model(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_					,  text_model=lowerCAmelCase_     )

     __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )
     __lowerCAmelCase								=	output[0].numpy()

     with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(lowerCAmelCase_     )
        __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_     )

        __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )
        __lowerCAmelCase								=	after_output[0].numpy()
        __lowerCAmelCase								=	np.amax(np.abs(out_a - out_a     )     )
        self.assertLessEqual(lowerCAmelCase_					,  1e-5     )






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Union[str, Any]=None					,  **lowerCAmelCase_ :		str     )							->						Optional[int]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_vision_text_model(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_					,  text_model=lowerCAmelCase_     )

     __lowerCAmelCase								=	model(
         input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  output_attentions=lowerCAmelCase_     )

     __lowerCAmelCase								=	output.vision_model_output.attentions
     self.assertEqual(len(lowerCAmelCase_     )					,  vision_config.num_hidden_layers     )

     # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
     __lowerCAmelCase								=	to_atuple(vision_model.config.image_size     )
     __lowerCAmelCase								=	to_atuple(vision_model.config.patch_size     )
     __lowerCAmelCase								=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     __lowerCAmelCase								=	num_patches + 1
     self.assertEqual(vision_attentions[0].shape[-3:]					,  (vision_config.num_attention_heads, seq_len, seq_len)     )

     __lowerCAmelCase								=	output.text_model_output.attentions
     self.assertEqual(len(lowerCAmelCase_     )					,  text_config.num_hidden_layers     )

     self.assertEqual(
         text_attentions[0].shape[-3:]					,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,  )






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		np.ndarray					,  lowerCAmelCase_ :		np.ndarray					,  lowerCAmelCase_ :		float     )							->						List[Any]:
     __lowerCAmelCase								=	np.abs((a - b)     ).max()
     self.assertLessEqual(lowerCAmelCase_					,  lowerCAmelCase_					,  f"""Difference between torch and flax is {diff} (>= {tol})."""     )






  def 				lowercase		(				self :		List[str]     )							->						str:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_model(**lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						int:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict     )							->						str:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase_     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						List[str]:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     self.check_save_load(**lowerCAmelCase_     )






  def 				lowercase		(				self :		int     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**lowerCAmelCase_     )






  @slow
  def 				lowercase		(				self :		List[str]     )							->						Dict:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_pretrained_model_and_inputs()

     __lowerCAmelCase								=	model_a(**lowerCAmelCase_     )
     __lowerCAmelCase								=	outputs[0].numpy()

     with tempfile.TemporaryDirectory() as tmp_dirname:
        model_a.save_pretrained(lowerCAmelCase_     )
        __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_     )

        __lowerCAmelCase								=	model_a(**lowerCAmelCase_     )
        __lowerCAmelCase								=	after_outputs[0].numpy()
        __lowerCAmelCase								=	np.amax(np.abs(out_a - out_a     )     )
        self.assertLessEqual(lowerCAmelCase_					,  1e-5     )





@require_tf
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Dict     )							->						List[Any]:
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         'hf-internal-testing/tiny-random-vit'					,  'hf-internal-testing/tiny-random-bert'     )
     __lowerCAmelCase								=	1_3
     __lowerCAmelCase								=	floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]     )
     __lowerCAmelCase								=	ids_tensor([batch_size, 4]					,  model.text_model.config.vocab_size     )
     __lowerCAmelCase								=	random_attention_mask([batch_size, 4]     )
     __lowerCAmelCase								=	{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

     return model, inputs






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]     )							->						Dict:
     __lowerCAmelCase								=	TFViTModel(lowerCAmelCase_					,  name='vision_model'     )
     __lowerCAmelCase								=	TFBertModel(lowerCAmelCase_					,  name='text_model'     )
     return vision_model, text_model






  def 				lowercase		(				self :		List[str]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	TFViTModelTester(self     )
     __lowerCAmelCase								=	TFBertModelTester(self     )
     __lowerCAmelCase								=	vit_model_tester.prepare_config_and_inputs()
     __lowerCAmelCase								=	bert_model_tester.prepare_config_and_inputs()

     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	vision_config_and_inputs

     (
         (
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	
     )								=	text_config_and_inputs

     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": input_mask,
         "input_ids": input_ids,
         "text_token_type_ids": token_type_ids,
         "text_sequence_labels": sequence_labels,
         "text_token_labels": token_labels,
         "text_choice_labels": choice_labels,
     }





@require_tf
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Dict     )							->						str:
     # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
     # just reinitialize it.
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         'Rocketknight1/tiny-random-deit-tf'					,  'hf-internal-testing/tiny-random-roberta'     )
     __lowerCAmelCase								=	1_3
     __lowerCAmelCase								=	floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]     )
     __lowerCAmelCase								=	ids_tensor([batch_size, 4]					,  model.text_model.config.vocab_size     )
     __lowerCAmelCase								=	random_attention_mask([batch_size, 4]     )
     __lowerCAmelCase								=	{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

     return model, inputs






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		str=None					,  **lowerCAmelCase_ :		Dict     )							->						Any:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_vision_text_model(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_					,  text_model=lowerCAmelCase_     )

     __lowerCAmelCase								=	model(
         input_ids=lowerCAmelCase_					,  pixel_values=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  output_attentions=lowerCAmelCase_     )

     __lowerCAmelCase								=	output.vision_model_output.attentions
     self.assertEqual(len(lowerCAmelCase_     )					,  vision_config.num_hidden_layers     )

     # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
     __lowerCAmelCase								=	to_atuple(vision_model.config.image_size     )
     __lowerCAmelCase								=	to_atuple(vision_model.config.patch_size     )
     __lowerCAmelCase								=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     __lowerCAmelCase								=	num_patches + 2
     self.assertEqual(vision_attentions[0].shape[-3:]					,  (vision_config.num_attention_heads, seq_len, seq_len)     )

     __lowerCAmelCase								=	output.text_model_output.attentions
     self.assertEqual(len(lowerCAmelCase_     )					,  text_config.num_hidden_layers     )

     self.assertEqual(
         text_attentions[0].shape[-3:]					,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,  )






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Tuple     )							->						Any:
     __lowerCAmelCase								=	TFDeiTModel(lowerCAmelCase_					,  name='vision_model'     )
     __lowerCAmelCase								=	TFRobertaModel(lowerCAmelCase_					,  name='text_model'     )
     return vision_model, text_model






  def 				lowercase		(				self :		List[str]     )							->						Optional[Any]:
     __lowerCAmelCase								=	TFDeiTModelTester(self     )
     __lowerCAmelCase								=	TFRobertaModelTester(self     )
     __lowerCAmelCase								=	vit_model_tester.prepare_config_and_inputs()
     __lowerCAmelCase								=	bert_model_tester.prepare_config_and_inputs()

     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	vision_config_and_inputs

     (
         (
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	
     )								=	text_config_and_inputs

     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": input_mask,
         "input_ids": input_ids,
         "text_token_type_ids": token_type_ids,
         "text_sequence_labels": sequence_labels,
         "text_token_labels": token_labels,
         "text_choice_labels": choice_labels,
     }





@require_tf
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		List[str]     )							->						List[Any]:
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         'Rocketknight1/tiny-random-clip-tf'					,  'hf-internal-testing/tiny-random-bert'     )
     __lowerCAmelCase								=	1_3
     __lowerCAmelCase								=	floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]     )
     __lowerCAmelCase								=	ids_tensor([batch_size, 4]					,  model.text_model.config.vocab_size     )
     __lowerCAmelCase								=	random_attention_mask([batch_size, 4]     )
     __lowerCAmelCase								=	{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

     return model, inputs






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		List[Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	TFCLIPVisionModel(lowerCAmelCase_					,  name='vision_model'     )
     __lowerCAmelCase								=	TFBertModel(lowerCAmelCase_					,  name='text_model'     )
     return vision_model, text_model






  def 				lowercase		(				self :		List[str]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	TFCLIPVisionModelTester(self     )
     __lowerCAmelCase								=	TFBertModelTester(self     )
     __lowerCAmelCase								=	clip_model_tester.prepare_config_and_inputs()
     __lowerCAmelCase								=	bert_model_tester.prepare_config_and_inputs()

     __lowerCAmelCase	,	__lowerCAmelCase								=	vision_config_and_inputs

     (
         (
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	(
         __lowerCAmelCase
     )	,	
     )								=	text_config_and_inputs

     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": input_mask,
         "input_ids": input_ids,
         "text_token_type_ids": token_type_ids,
         "text_sequence_labels": sequence_labels,
         "text_token_labels": token_labels,
         "text_choice_labels": choice_labels,
     }





@require_vision
@require_tf
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  @slow
  def 				lowercase		(				self :		Union[str, Any]     )							->						Tuple:
     __lowerCAmelCase								=	TFVisionTextDualEncoderModel.from_pretrained(
         'clip-italian/clip-italian'					,  logit_scale_init_value=1.0					,  from_pt=lowerCAmelCase_     )
     __lowerCAmelCase								=	VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'     )

     __lowerCAmelCase								=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     __lowerCAmelCase								=	processor(
         text=['una foto di un gatto', 'una foto di un cane']					,  images=lowerCAmelCase_					,  padding=lowerCAmelCase_					,  return_tensors='np'     )

     __lowerCAmelCase								=	model(**lowerCAmelCase_     )

     # verify the logits
     self.assertEqual(outputs.logits_per_image.shape					,  (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])     )
     self.assertEqual(
         outputs.logits_per_text.shape					,  (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])					,  )

     __lowerCAmelCase								=	np.array([[1.2_28_47_27, 0.3_10_41_22]]     )

     self.assertTrue(np.allclose(outputs.logits_per_image.numpy()					,  lowerCAmelCase_					,  atol=1e-3     )     )








from typing import TYPE_CHECKING

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


_snake_case  :    Union[str, Any]						    =  {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Union[str, Any]						    =  ['LayoutLMv3TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    List[str]						    =  [
         'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'LayoutLMv3ForQuestionAnswering',
         'LayoutLMv3ForSequenceClassification',
         'LayoutLMv3ForTokenClassification',
         'LayoutLMv3Model',
         'LayoutLMv3PreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[Any]						    =  [
         'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFLayoutLMv3ForQuestionAnswering',
         'TFLayoutLMv3ForSequenceClassification',
         'TFLayoutLMv3ForTokenClassification',
         'TFLayoutLMv3Model',
         'TFLayoutLMv3PreTrainedModel',
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Tuple						    =  ['LayoutLMv3FeatureExtractor']
     _snake_case  :    str						    =  ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
     from .configuration_layoutlmva import (
         LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LayoutLMvaConfig,
         LayoutLMvaOnnxConfig,
     )
     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_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlmva import (
              TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
              TFLayoutLMvaModel,
              TFLayoutLMvaPreTrainedModel,
          )

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

else:
     import sys

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

