



'''simple docstring'''

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor

class      __magic_name__						:







							def __init__(							self :	Tuple     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[Any]=13     ,_UpperCAmelCase :	Optional[Any]=30     ,_UpperCAmelCase :	Tuple=2     ,_UpperCAmelCase :	List[Any]=3     ,_UpperCAmelCase :	int=True     ,_UpperCAmelCase :	str=True     ,_UpperCAmelCase :	Optional[Any]=32     ,_UpperCAmelCase :	Optional[int]=2     ,_UpperCAmelCase :	Union[str, Any]=4     ,_UpperCAmelCase :	List[Any]=37     ,_UpperCAmelCase :	List[str]="gelu"     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	List[Any]=10     ,_UpperCAmelCase :	Optional[int]=0.02     ,_UpperCAmelCase :	Any=3     ,_UpperCAmelCase :	Union[str, Any]=0.6     ,_UpperCAmelCase :	Optional[Any]=None     ,):
									_a					:				Optional[int]				=							parent
									_a					:				List[Any]				=							batch_size
									_a					:				Tuple				=							image_size
									_a					:				int				=							patch_size
									_a					:				List[Any]				=							num_channels
									_a					:				int				=							is_training
									_a					:				List[Any]				=							use_labels
									_a					:				List[str]				=							hidden_size
									_a					:				Any				=							num_hidden_layers
									_a					:				Dict				=							num_attention_heads
									_a					:				List[str]				=							intermediate_size
									_a					:				Any				=							hidden_act
									_a					:				int				=							hidden_dropout_prob
									_a					:				Union[str, Any]				=							attention_probs_dropout_prob
									_a					:				List[Any]				=							type_sequence_label_size
									_a					:				Union[str, Any]				=							initializer_range
									_a					:				Optional[Any]				=							mask_ratio
									_a					:				Dict				=							scope

									# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
									# (we add 1 for the [CLS] token)
									_a					:				Dict				=							(image_size // patch_size) ** 2
									_a					:				List[Any]				=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)







							def 							__lowercase      (							self :	str		):
									_a					:				int				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

									_a					:				Dict				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	Union[str, Any]		):
									return ViTMAEConfig(
									    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     ,decoder_hidden_size=self.hidden_size     ,decoder_num_hidden_layers=self.num_hidden_layers     ,decoder_num_attention_heads=self.num_attention_heads     ,decoder_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     ,mask_ratio=self.mask_ratio     ,)







							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Dict		):
									_a					:				Optional[Any]				=							TFViTMAEModel(config=_UpperCamelCase		)
									_a					:				Tuple				=							model(_UpperCamelCase     ,training=_UpperCamelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Dict		):
									_a					:				str				=							TFViTMAEForPreTraining(_UpperCamelCase		)
									_a					:				List[Any]				=							model(_UpperCamelCase     ,training=_UpperCamelCase		)
									# expected sequence length = num_patches
									_a					:				List[Any]				=							(self.image_size // self.patch_size) ** 2
									_a					:				Union[str, Any]				=							self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, num_patches, expected_num_channels)		)

									# test greyscale images
									_a					:				Optional[Any]				=							1
									_a					:				int				=							TFViTMAEForPreTraining(_UpperCamelCase		)

									_a					:				List[Any]				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									_a					:				Union[str, Any]				=							model(_UpperCamelCase     ,training=_UpperCamelCase		)
									_a					:				Any				=							self.patch_size**2
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, num_patches, expected_num_channels)		)







							def 							__lowercase      (							self :	int		):
									_a					:				Tuple				=							self.prepare_config_and_inputs()
									(_a)					:				Dict				=							config_and_inputs
									_a					:				Tuple				=							{"""pixel_values""": pixel_values}
									return config, inputs_dict

@require_tf
class      __magic_name__						(   lowercase__      ,						lowercase__      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							lowerCAmelCase	:   int           = {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Tuple           = False
							lowerCAmelCase	:   Optional[int]           = False
							lowerCAmelCase	:   List[str]           = False







							def 							__lowercase      (							self :	List[str]		):
									_a					:				Dict				=							TFViTMAEModelTester(self		)
									_a					:				List[str]				=							ConfigTester(self     ,config_class=_UpperCamelCase     ,has_text_modality=_UpperCamelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	Optional[int]		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='ViTMAE does not use inputs_embeds'		)
							def 							__lowercase      (							self :	List[str]		):
									pass







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Union[str, Any]				=							model_class(_UpperCamelCase		)
											self.assertIsInstance(model.get_input_embeddings()     ,(tf.keras.layers.Layer)		)
											_a					:				Optional[Any]				=							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCamelCase     ,tf.keras.layers.Layer		)		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Dict				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Any				=							model_class(_UpperCamelCase		)
											_a					:				Optional[int]				=							inspect.signature(model.call		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				List[Any]				=							[*signature.parameters.keys()]

											_a					:				int				=							["""pixel_values"""]
											self.assertListEqual(arg_names[:1]     ,_UpperCamelCase		)







							def 							__lowercase      (							self :	Any		):
									_a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_UpperCamelCase		)







							def 							__lowercase      (							self :	List[Any]		):
									# make the mask reproducible
									np.random.seed(2		)

									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				Union[str, Any]				=							int((config.image_size // config.patch_size) ** 2		)
									_a					:				str				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

									for model_class in self.all_model_classes:
											_a					:				Optional[int]				=							model_class(_UpperCamelCase		)
											_a					:				int				=							self._prepare_for_class(_UpperCamelCase     ,_UpperCamelCase		)

											_a					:				Any				=							model(_UpperCamelCase     ,noise=_UpperCamelCase		)

											_a					:				List[Any]				=							copy.deepcopy(self._prepare_for_class(_UpperCamelCase     ,_UpperCamelCase		)		)
											_a					:				Dict				=							model(**_UpperCamelCase     ,noise=_UpperCamelCase		)
											_a					:				Optional[Any]				=							outputs_dict[0].numpy()
											_a					:				Dict				=							outputs_keywords[0].numpy()

											self.assertLess(np.sum(np.abs(output_dict - output_keywords		)		)     ,1E-6		)







							def 							__lowercase      (							self :	Optional[int]		):
									# make the mask reproducible
									np.random.seed(2		)

									_a					:				int				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				int				=							int((config.image_size // config.patch_size) ** 2		)
									_a					:				Dict				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

									def prepare_numpy_arrays(_UpperCAmelCase :	Tuple		):
											_a					:				Union[str, Any]				=							{}
											for k, v in inputs_dict.items():
													if tf.is_tensor(_UpperCamelCase		):
															_a					:				Any				=							v.numpy()
													else:
															_a					:				int				=							np.array(_UpperCamelCase		)

											return inputs_np_dict

									for model_class in self.all_model_classes:
											_a					:				List[str]				=							model_class(_UpperCamelCase		)

											_a					:				Tuple				=							self._prepare_for_class(_UpperCamelCase     ,_UpperCamelCase		)
											_a					:				str				=							prepare_numpy_arrays(_UpperCamelCase		)

											_a					:				Dict				=							model(_UpperCamelCase     ,noise=_UpperCamelCase		)
											_a					:				int				=							model(**_UpperCamelCase     ,noise=_UpperCamelCase		)
											self.assert_outputs_same(_UpperCamelCase     ,_UpperCamelCase		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
									# make masks reproducible
									np.random.seed(2		)

									_a					:				int				=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2		)
									_a					:				Optional[Any]				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
									_a					:				int				=							tf.constant(_UpperCamelCase		)

									# Add `noise` argument.
									# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
									_a					:				Dict				=							tf_noise

									super().check_pt_tf_models(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase		)







							def 							__lowercase      (							self :	Dict		):
									# make mask reproducible
									np.random.seed(2		)

									_a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				Optional[int]				=							{
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__		),)
									    for module_member_name in dir(_UpperCamelCase		)
									    if module_member_name.endswith('MainLayer'		)
									    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
									    and module_member_name[: -len('MainLayer'		)] == model_class.__name__[: -len('Model'		)]
									    for module_member in (getattr(_UpperCamelCase     ,_UpperCamelCase		),)
									    if isinstance(_UpperCamelCase     ,_UpperCamelCase		)
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(_UpperCamelCase     ,'_keras_serializable'     ,_UpperCamelCase		)
									}

									_a					:				Optional[int]				=							int((config.image_size // config.patch_size) ** 2		)
									_a					:				str				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
									_a					:				Any				=							tf.convert_to_tensor(_UpperCamelCase		)
									inputs_dict.update({'noise': noise}		)

									for main_layer_class in tf_main_layer_classes:
											_a					:				Dict				=							main_layer_class(_UpperCamelCase		)

											_a					:				Union[str, Any]				=							{
											    name: tf.keras.Input(tensor.shape[1:]     ,dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
											}

											_a					:				Any				=							tf.keras.Model(_UpperCamelCase     ,outputs=main_layer(_UpperCamelCase		)		)
											_a					:				List[Any]				=							model(_UpperCamelCase		)

											with tempfile.TemporaryDirectory() as tmpdirname:
													_a					:				Optional[int]				=							os.path.join(_UpperCamelCase     ,'keras_model.h5'		)
													model.save(_UpperCamelCase		)
													_a					:				Optional[int]				=							tf.keras.models.load_model(
													    _UpperCamelCase     ,custom_objects={main_layer_class.__name__: main_layer_class}		)
													assert isinstance(_UpperCamelCase     ,tf.keras.Model		)
													_a					:				Tuple				=							model(_UpperCamelCase		)
													self.assert_outputs_same(_UpperCamelCase     ,_UpperCamelCase		)







							@slow
							def 							__lowercase      (							self :	Dict		):
									# make mask reproducible
									np.random.seed(2		)

									_a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				List[Any]				=							int((config.image_size // config.patch_size) ** 2		)
									_a					:				List[str]				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

									for model_class in self.all_model_classes:
											_a					:				int				=							model_class(_UpperCamelCase		)
											_a					:				List[Any]				=							self._prepare_for_class(_UpperCamelCase     ,_UpperCamelCase		)
											_a					:				Any				=							model(_UpperCamelCase     ,noise=_UpperCamelCase		)

											if model_class.__name__ == "TFViTMAEModel":
													_a					:				List[Any]				=							outputs.last_hidden_state.numpy()
													_a					:				Optional[Any]				=							0
											else:
													_a					:				int				=							outputs.logits.numpy()
													_a					:				Dict				=							0

											with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(_UpperCamelCase     ,saved_model=_UpperCamelCase		)
													_a					:				int				=							model_class.from_pretrained(_UpperCamelCase		)
													_a					:				Union[str, Any]				=							model(_UpperCamelCase     ,noise=_UpperCamelCase		)

													if model_class.__name__ == "TFViTMAEModel":
															_a					:				Optional[int]				=							after_outputs["""last_hidden_state"""].numpy()
															_a					:				List[Any]				=							0
													else:
															_a					:				Any				=							after_outputs["""logits"""].numpy()
															_a					:				Any				=							0

													_a					:				Optional[Any]				=							np.amax(np.abs(out_a - out_a		)		)
													self.assertLessEqual(_UpperCamelCase     ,1E-5		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									# make mask reproducible
									np.random.seed(2		)

									_a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				Tuple				=							int((config.image_size // config.patch_size) ** 2		)
									_a					:				str				=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

									for model_class in self.all_model_classes:
											_a					:				str				=							model_class(_UpperCamelCase		)
											_a					:				Dict				=							self._prepare_for_class(_UpperCamelCase     ,_UpperCamelCase		)

											_a					:				List[Any]				=							model(_UpperCamelCase     ,noise=_UpperCamelCase		)
											_a					:				List[Any]				=							model.get_config()
											# make sure that returned config is jsonifiable, which is required by keras
											json.dumps(_UpperCamelCase		)
											_a					:				Optional[Any]				=							model_class.from_config(model.get_config()		)
											# make sure it also accepts a normal config
											_a					:				str				=							model_class.from_config(model.config		)
											_a					:				List[str]				=							new_model(_UpperCamelCase		)  # Build model
											new_model.set_weights(model.get_weights()		)
											_a					:				Optional[Any]				=							new_model(_UpperCamelCase     ,noise=_UpperCamelCase		)

											self.assert_outputs_same(_UpperCamelCase     ,_UpperCamelCase		)







							@unittest.skip(
							    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
							def 							__lowercase      (							self :	Tuple		):
									pass







							@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'		)
							def 							__lowercase      (							self :	Dict		):
									pass







							@slow
							def 							__lowercase      (							self :	int		):
									_a					:				List[str]				=							TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'		)
									self.assertIsNotNone(_UpperCamelCase		)



def 					__lowerCamelCase					(				)      ->				List[Any]:
		_a					:				str				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							@cached_property
							def 							__lowercase      (							self :	List[str]		):
									return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'		) if is_vision_available() else None







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									# make random mask reproducible across the PT and TF model
									np.random.seed(2		)

									_a					:				int				=							TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'		)

									_a					:				Any				=							self.default_image_processor
									_a					:				str				=							prepare_img()
									_a					:				Dict				=							image_processor(images=_UpperCamelCase     ,return_tensors='tf'		)

									# prepare a noise vector that will be also used for testing the TF model
									# (this way we can ensure that the PT and TF models operate on the same inputs)
									_a					:				Any				=							ViTMAEConfig()
									_a					:				Optional[int]				=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
									_a					:				Any				=							np.random.uniform(size=(1, num_patches)		)

									# forward pass
									_a					:				Optional[Any]				=							model(**_UpperCamelCase     ,noise=_UpperCamelCase		)

									# verify the logits
									_a					:				Optional[Any]				=							tf.convert_to_tensor([1, 196, 768]		)
									self.assertEqual(outputs.logits.shape     ,_UpperCamelCase		)

									_a					:				Any				=							tf.convert_to_tensor(
									    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]		)

									tf.debugging.assert_near(outputs.logits[0, :3, :3]     ,_UpperCamelCase     ,atol=1E-4		)









'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase						      =       {
    '''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:
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecAudioForAudioFrameClassification''',
							    '''Data2VecAudioForCTC''',
							    '''Data2VecAudioForSequenceClassification''',
							    '''Data2VecAudioForXVector''',
							    '''Data2VecAudioModel''',
							    '''Data2VecAudioPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecTextForCausalLM''',
							    '''Data2VecTextForMaskedLM''',
							    '''Data2VecTextForMultipleChoice''',
							    '''Data2VecTextForQuestionAnswering''',
							    '''Data2VecTextForSequenceClassification''',
							    '''Data2VecTextForTokenClassification''',
							    '''Data2VecTextModel''',
							    '''Data2VecTextPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecVisionForImageClassification''',
							    '''Data2VecVisionForMaskedImageModeling''',
							    '''Data2VecVisionForSemanticSegmentation''',
							    '''Data2VecVisionModel''',
							    '''Data2VecVisionPreTrainedModel''',
							]

if is_tf_available():
							__lowerCAmelCase						      =       [
							    '''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

							__lowerCAmelCase						      =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




