


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     BeitForImageClassification,
     BeitForMaskedImageModeling,
     BeitForSemanticSegmentation,
     BeitModel,
 )
 from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 import PIL
 from PIL import Image

 from transformers import BeitImageProcessor




class  a     :
 def __init__( self      ,				__magic_name__      ,				__magic_name__=1_00      ,				__magic_name__=13      ,				__magic_name__=30      ,				__magic_name__=2      ,				__magic_name__=3      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=32      ,				__magic_name__=4      ,				__magic_name__=4      ,				__magic_name__=37      ,				__magic_name__="gelu"      ,				__magic_name__=0.1      ,				__magic_name__=0.1      ,				__magic_name__=10      ,				__magic_name__=0.0_2      ,				__magic_name__=3      ,				__magic_name__=None      ,				__magic_name__=[0, 1, 2, 3]      ,				)					->							int:
      _a			      =							parent
      _a			      =							1_00
      _a			      =							batch_size
      _a			      =							image_size
      _a			      =							patch_size
      _a			      =							num_channels
      _a			      =							is_training
      _a			      =							use_labels
      _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			      =							type_sequence_label_size
      _a			      =							initializer_range
      _a			      =							scope
      _a			      =							out_indices
      _a			      =							num_labels

      # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _a			      =							(image_size // patch_size) ** 2
      _a			      =							num_patches + 1
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_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.image_size, self.image_size]      ,				self.num_labels						)

      _a			      =							self.get_config()

      return config, pixel_values, labels, pixel_labels
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      return BeitConfig(
          vocab_size=self.vocab_size      ,				image_size=self.image_size      ,				patch_size=self.patch_size      ,				num_channels=self.num_channels      ,				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      ,				is_decoder=_UpperCAmelCase      ,				initializer_range=self.initializer_range      ,				out_indices=self.out_indices      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							str:
      _a			      =							BeitModel(config=_UpperCAmelCase						)
      model.to(_UpperCAmelCase						)
      model.eval()
      _a			      =							model(_UpperCAmelCase						)
      self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							BeitForMaskedImageModeling(config=_UpperCAmelCase						)
      model.to(_UpperCAmelCase						)
      model.eval()
      _a			      =							model(_UpperCAmelCase						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length - 1, self.vocab_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Any:
      _a			      =							self.type_sequence_label_size
      _a			      =							BeitForImageClassification(_UpperCAmelCase						)
      model.to(_UpperCAmelCase						)
      model.eval()
      _a			      =							model(_UpperCAmelCase      ,				labels=_UpperCAmelCase						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)						)

      # test greyscale images
      _a			      =							1
      _a			      =							BeitForImageClassification(_UpperCAmelCase						)
      model.to(_UpperCAmelCase						)
      model.eval()

      _a			      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
      _a			      =							model(_UpperCAmelCase      ,				labels=_UpperCAmelCase						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							List[Any]:
      _a			      =							self.num_labels
      _a			      =							BeitForSemanticSegmentation(_UpperCAmelCase						)
      model.to(_UpperCAmelCase						)
      model.eval()
      _a			      =							model(_UpperCAmelCase						)
      self.parent.assertEqual(
          result.logits.shape      ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
      _a			      =							model(_UpperCAmelCase      ,				labels=_UpperCAmelCase						)
      self.parent.assertEqual(
          result.logits.shape      ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)




 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							self.prepare_config_and_inputs()
      _a      ,   _a      ,   _a      ,   _a			      =							config_and_inputs
      _a			      =							{'pixel_values': pixel_values}
      return config, inputs_dict





@require_torch
class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	(
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase 					=	(
     {
         """feature-extraction""": BeitModel,
         """image-classification""": BeitForImageClassification,
         """image-segmentation""": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							BeitModelTester(self						)
      _a			      =							ConfigTester(self      ,				config_class=_UpperCAmelCase      ,				has_text_modality=_UpperCAmelCase      ,				hidden_size=37						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      self.config_tester.run_common_tests()
 @unittest.skip(reason='BEiT does not use inputs_embeds'						)
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      pass
 @require_torch_multi_gpu
 @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'						)
 def 							__UpperCAmelCase		( self						)					->							str:
      pass
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a			      =							model_class(_UpperCAmelCase						)
           self.assertIsInstance(model.get_input_embeddings()      ,				(nn.Module)						)
           _a			      =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_UpperCAmelCase      ,				nn.Linear						)						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a			      =							model_class(_UpperCAmelCase						)
           _a			      =							inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a			      =							[*signature.parameters.keys()]

           _a			      =							['pixel_values']
           self.assertListEqual(arg_names[:1]      ,				_UpperCAmelCase						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCAmelCase						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:




      if not self.model_tester.is_training:
           return

      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a			      =							True

      for model_class in self.all_model_classes:
           # we don't test BeitForMaskedImageModeling
           if model_class in [*get_values(_UpperCAmelCase						), BeitForMaskedImageModeling]:
                continue

           _a			      =							model_class(_UpperCAmelCase						)
           model.to(_UpperCAmelCase						)
           model.train()
           _a			      =							self._prepare_for_class(_UpperCAmelCase      ,				_UpperCAmelCase      ,				return_labels=_UpperCAmelCase						)
           _a			      =							model(**_UpperCAmelCase						).loss
           loss.backward()
 def 							__UpperCAmelCase		( self						)					->							str:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
           return

      _a			      =							False
      _a			      =							True

      for model_class in self.all_model_classes:
           # we don't test BeitForMaskedImageModeling
           if (
               model_class in [*get_values(_UpperCAmelCase						), BeitForMaskedImageModeling]
               or not model_class.supports_gradient_checkpointing
           ):
                continue

           _a			      =							model_class(_UpperCAmelCase						)
           model.gradient_checkpointing_enable()
           model.to(_UpperCAmelCase						)
           model.train()
           _a			      =							self._prepare_for_class(_UpperCAmelCase      ,				_UpperCAmelCase      ,				return_labels=_UpperCAmelCase						)
           _a			      =							model(**_UpperCAmelCase						).loss
           loss.backward()
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      _a			      =							_config_zero_init(_UpperCAmelCase						)
      for model_class in self.all_model_classes:
           _a			      =							model_class(config=_UpperCAmelCase						)
           for name, param in model.named_parameters():
                # we skip lambda parameters as these require special initial values
                # determined by config.layer_scale_init_value
                if "lambda" in name:
                     continue
                if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()      ,				[0.0, 1.0]      ,				msg=f'Parameter {name} of model {model_class} seems not properly initialized'      ,				)




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




      for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a			      =							BeitModel.from_pretrained(_UpperCAmelCase						)
           self.assertIsNotNone(_UpperCAmelCase						)






def 	_A				()   ->  List[Any]:




     '''simple docstring'''



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






@require_torch
@require_vision
class  a     (     unittest.TestCase       ):
 @cached_property
 def 							__UpperCAmelCase		( self						)					->							int:
      return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'						) if is_vision_available() else None
 @slow
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'						).to(_UpperCAmelCase						)

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

      # prepare bool_masked_pos
      _a			      =							torch.ones((1, 1_96)      ,				dtype=torch.bool						).to(_UpperCAmelCase						)

      # forward pass
      with torch.no_grad():
           _a			      =							model(pixel_values=_UpperCAmelCase      ,				bool_masked_pos=_UpperCAmelCase						)
      _a			      =							outputs.logits

      # verify the logits
      _a			      =							torch.Size((1, 1_96, 81_92)						)
      self.assertEqual(logits.shape      ,				_UpperCAmelCase						)

      _a			      =							torch.tensor(
          [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]						).to(_UpperCAmelCase						)

      self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]      ,				_UpperCAmelCase      ,				atol=1e-2						)						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'						).to(_UpperCAmelCase						)

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

      # forward pass
      with torch.no_grad():
           _a			      =							model(**_UpperCAmelCase						)
      _a			      =							outputs.logits

      # verify the logits
      _a			      =							torch.Size((1, 10_00)						)
      self.assertEqual(logits.shape      ,				_UpperCAmelCase						)

      _a			      =							torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]						).to(_UpperCAmelCase						)

      self.assertTrue(torch.allclose(logits[0, :3]      ,				_UpperCAmelCase      ,				atol=1e-4						)						)

      _a			      =							2_81
      self.assertEqual(logits.argmax(-1						).item()      ,				_UpperCAmelCase						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'						).to(
          _UpperCAmelCase						)

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

      # forward pass
      with torch.no_grad():
           _a			      =							model(**_UpperCAmelCase						)
      _a			      =							outputs.logits

      # verify the logits
      _a			      =							torch.Size((1, 2_18_41)						)
      self.assertEqual(logits.shape      ,				_UpperCAmelCase						)

      _a			      =							torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]						).to(_UpperCAmelCase						)

      self.assertTrue(torch.allclose(logits[0, :3]      ,				_UpperCAmelCase      ,				atol=1e-4						)						)

      _a			      =							23_96
      self.assertEqual(logits.argmax(-1						).item()      ,				_UpperCAmelCase						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a			      =							BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
      _a			      =							model.to(_UpperCAmelCase						)

      _a			      =							BeitImageProcessor(do_resize=_UpperCAmelCase      ,				size=6_40      ,				do_center_crop=_UpperCAmelCase						)

      _a			      =							load_dataset('hf-internal-testing/fixtures_ade20k'      ,				split='test'						)
      _a			      =							Image.open(ds[0]['file']						)
      _a			      =							image_processor(images=_UpperCAmelCase      ,				return_tensors='pt'						).to(_UpperCAmelCase						)

      # forward pass
      with torch.no_grad():
           _a			      =							model(**_UpperCAmelCase						)
      _a			      =							outputs.logits

      # verify the logits
      _a			      =							torch.Size((1, 1_50, 1_60, 1_60)						)
      self.assertEqual(logits.shape      ,				_UpperCAmelCase						)

      _a			      =							version.parse(PIL.__version__						) < version.parse('9.0.0'						)

      if is_pillow_less_than_a:
           _a			      =							torch.tensor(
               [
                   [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                   [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                   [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
               ]      ,				device=_UpperCAmelCase      ,				)
      else:
           _a			      =							torch.tensor(
               [
                   [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                   [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                   [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
               ]      ,				device=_UpperCAmelCase      ,				)

      self.assertTrue(torch.allclose(logits[0, :3, :3, :3]      ,				_UpperCAmelCase      ,				atol=1e-4						)						)




 @slow
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
      _a			      =							model.to(_UpperCAmelCase						)

      _a			      =							BeitImageProcessor(do_resize=_UpperCAmelCase      ,				size=6_40      ,				do_center_crop=_UpperCAmelCase						)

      _a			      =							load_dataset('hf-internal-testing/fixtures_ade20k'      ,				split='test'						)
      _a			      =							Image.open(ds[0]['file']						)
      _a			      =							image_processor(images=_UpperCAmelCase      ,				return_tensors='pt'						).to(_UpperCAmelCase						)

      # forward pass
      with torch.no_grad():
           _a			      =							model(**_UpperCAmelCase						)

      _a			      =							outputs.logits.detach().cpu()

      _a			      =							image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase      ,				target_sizes=[(5_00, 3_00)]						)
      _a			      =							torch.Size((5_00, 3_00)						)
      self.assertEqual(segmentation[0].shape      ,				_UpperCAmelCase						)

      _a			      =							image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase						)
      _a			      =							torch.Size((1_60, 1_60)						)
      self.assertEqual(segmentation[0].shape      ,				_UpperCAmelCase						)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 a_					:    List[Any]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

