





"""simple docstring"""



from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


a_				=    logging.get_logger(__name__)

# General docstring
a_				=    """ResNetConfig"""

# Base docstring
a_				=    """microsoft/resnet-50"""
a_				=    [1, 2048, 7, 7]

# Image classification docstring
a_				=    """microsoft/resnet-50"""
a_				=    """tiger cat"""

a_				=    [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = 3      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = "relu"      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     __A					:		List[str]						=  kernel_size // 2
     __A					:		int						=  tf.keras.layers.ConvaD(
         __lowerCamelCase      ,		kernel_size=__lowerCamelCase      ,		strides=__lowerCamelCase      ,		padding='''valid'''      ,		use_bias=__lowerCamelCase      ,		name='''convolution'''						)
     # Use same default momentum and epsilon as PyTorch equivalent
     __A					:		Optional[int]						=  tf.keras.layers.BatchNormalization(epsilon=1e-5      ,		momentum=0.9      ,		name='''normalization'''						)
     __A					:		Union[str, Any]						=  ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Union[str, Any]						=  (self.pad_value, self.pad_value)
     __A					:		List[str]						=  tf.pad(__lowerCamelCase      ,		[(0, 0), height_pad, width_pad, (0, 0)]						)
     __A					:		List[str]						=  self.conv(__lowerCamelCase						)
     return hidden_state







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.convolution(__lowerCamelCase						)
     __A					:		Dict						=  self.normalization(__lowerCamelCase      ,		training=__lowerCamelCase						)
     __A					:		str						=  self.activation(__lowerCamelCase						)
     return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     __A					:		List[str]						=  TFResNetConvLayer(
         config.embedding_size      ,		kernel_size=7      ,		stride=2      ,		activation=config.hidden_act      ,		name='''embedder'''      ,		)
     __A					:		Any						=  tf.keras.layers.MaxPoolaD(pool_size=3      ,		strides=2      ,		padding='''valid'''      ,		name='''pooler'''						)
     __A					:		Tuple						=  config.num_channels







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



     '''simple docstring'''




     __A				, __A				, __A				, __A					:		Any						=  shape_list(__lowerCamelCase						)
     if tf.executing_eagerly() and num_channels != self.num_channels:
         raise ValueError(
             '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''						)
     __A					:		List[Any]						=  pixel_values
     __A					:		Any						=  self.embedder(__lowerCamelCase						)
     __A					:		str						=  tf.pad(__lowerCamelCase      ,		[[0, 0], [1, 1], [1, 1], [0, 0]]						)
     __A					:		Dict						=  self.pooler(__lowerCamelCase						)
     return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = 2      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     __A					:		Optional[int]						=  tf.keras.layers.ConvaD(
         __lowerCamelCase      ,		kernel_size=1      ,		strides=__lowerCamelCase      ,		use_bias=__lowerCamelCase      ,		name='''convolution'''						)
     # Use same default momentum and epsilon as PyTorch equivalent
     __A					:		Optional[int]						=  tf.keras.layers.BatchNormalization(epsilon=1e-5      ,		momentum=0.9      ,		name='''normalization'''						)







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



     '''simple docstring'''




     __A					:		Tuple						=  x
     __A					:		Any						=  self.convolution(__lowerCamelCase						)
     __A					:		int						=  self.normalization(__lowerCamelCase      ,		training=__lowerCamelCase						)
     return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = "relu"      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     __A					:		int						=  in_channels != out_channels or stride != 1
     __A					:		Union[str, Any]						=  TFResNetConvLayer(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''layer.0'''						)
     __A					:		Tuple						=  TFResNetConvLayer(__lowerCamelCase      ,		activation=__lowerCamelCase      ,		name='''layer.1'''						)
     __A					:		Any						=  (
         TFResNetShortCut(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''shortcut'''						)
         if should_apply_shortcut
         else tf.keras.layers.Activation('''linear'''      ,		name='''shortcut'''						)
     )
     __A					:		List[Any]						=  ACTaFN[activation]







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



     '''simple docstring'''




     __A					:		Optional[Any]						=  hidden_state
     __A					:		Tuple						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
     __A					:		Optional[int]						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
     __A					:		Dict						=  self.shortcut(__lowerCamelCase      ,		training=__lowerCamelCase						)
     hidden_state += residual
     __A					:		int						=  self.activation(__lowerCamelCase						)
     return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = "relu"      ,		__lowerCamelCase = 4      ,		**__lowerCamelCase      ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     __A					:		Optional[int]						=  in_channels != out_channels or stride != 1
     __A					:		str						=  out_channels // reduction
     __A					:		Tuple						=  TFResNetConvLayer(__lowerCamelCase      ,		kernel_size=1      ,		name='''layer.0'''						)
     __A					:		Dict						=  TFResNetConvLayer(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''layer.1'''						)
     __A					:		Any						=  TFResNetConvLayer(__lowerCamelCase      ,		kernel_size=1      ,		activation=__lowerCamelCase      ,		name='''layer.2'''						)
     __A					:		str						=  (
         TFResNetShortCut(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''shortcut'''						)
         if should_apply_shortcut
         else tf.keras.layers.Activation('''linear'''      ,		name='''shortcut'''						)
     )
     __A					:		Dict						=  ACTaFN[activation]







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



     '''simple docstring'''




     __A					:		List[str]						=  hidden_state
     __A					:		int						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
     __A					:		int						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
     __A					:		Tuple						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
     __A					:		Optional[Any]						=  self.shortcut(__lowerCamelCase      ,		training=__lowerCamelCase						)
     hidden_state += residual
     __A					:		int						=  self.activation(__lowerCamelCase						)
     return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 2      ,		__lowerCamelCase = 2      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)

     __A					:		List[Any]						=  TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

     __A					:		List[str]						=  [layer(__lowerCamelCase      ,		__lowerCamelCase      ,		stride=__lowerCamelCase      ,		activation=config.hidden_act      ,		name='''layers.0'''						)]
     layers += [
         layer(__lowerCamelCase      ,		__lowerCamelCase      ,		activation=config.hidden_act      ,		name=F"""layers.{i + 1}"""						)
         for i in range(depth - 1						)
     ]
     __A					:		Optional[int]						=  layers







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



     '''simple docstring'''




     for layer in self.stage_layers:
         __A					:		Tuple						=  layer(__lowerCamelCase      ,		training=__lowerCamelCase						)
     return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
     __A					:		Union[str, Any]						=  [
         TFResNetStage(
             __lowerCamelCase      ,		config.embedding_size      ,		config.hidden_sizes[0]      ,		stride=2 if config.downsample_in_first_stage else 1      ,		depth=config.depths[0]      ,		name='''stages.0'''      ,		)
     ]
     for i, (in_channels, out_channels, depth) in enumerate(
         zip(config.hidden_sizes      ,		config.hidden_sizes[1:]      ,		config.depths[1:]						)						):
         self.stages.append(TFResNetStage(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		depth=__lowerCamelCase      ,		name=F"""stages.{i + 1}"""						)						)







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False      ,		__lowerCamelCase = True      ,		__lowerCamelCase = False      ,		):



     '''simple docstring'''




     __A					:		Any						=  () if output_hidden_states else None

     for stage_module in self.stages:
         if output_hidden_states:
             __A					:		Tuple						=  hidden_states + (hidden_state,)

         __A					:		Optional[Any]						=  stage_module(__lowerCamelCase      ,		training=__lowerCamelCase						)

     if output_hidden_states:
         __A					:		Any						=  hidden_states + (hidden_state,)

     if not return_dict:
         return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

     return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowerCamelCase      ,		hidden_states=__lowerCamelCase						)






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  ResNetConfig
 _lowerCamelCase				 =  """resnet"""
 _lowerCamelCase				 =  """pixel_values"""

 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,		dtype=tf.floataa						)}






a_				=    R"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


a_				=    R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@keras_serializable
class 	__snake_case      (	tf.keras.layers.Layer  ):





 """simple docstring"""


 _lowerCamelCase				 =  ResNetConfig

 def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase						)
     __A					:		str						=  config
     __A					:		List[Any]						=  TFResNetEmbeddings(__lowerCamelCase      ,		name='''embedder'''						)
     __A					:		Optional[int]						=  TFResNetEncoder(__lowerCamelCase      ,		name='''encoder'''						)
     __A					:		str						=  tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCamelCase						)







 @unpack_inputs
 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = False      ,		):



     '''simple docstring'''




     __A					:		Union[str, Any]						=  (
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     __A					:		Tuple						=  return_dict if return_dict is not None else self.config.use_return_dict

     # TF 2.0 image layers can't use NCHW format when running on CPU.
     # We transpose to NHWC format and then transpose back after the full forward pass.
     # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
     __A					:		Tuple						=  tf.transpose(__lowerCamelCase      ,		perm=[0, 2, 3, 1]						)
     __A					:		Any						=  self.embedder(__lowerCamelCase      ,		training=__lowerCamelCase						)

     __A					:		List[str]						=  self.encoder(
         __lowerCamelCase      ,		output_hidden_states=__lowerCamelCase      ,		return_dict=__lowerCamelCase      ,		training=__lowerCamelCase						)

     __A					:		Optional[Any]						=  encoder_outputs[0]

     __A					:		Tuple						=  self.pooler(__lowerCamelCase						)

     # Transpose all the outputs to the NCHW format
     # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
     __A					:		Optional[Any]						=  tf.transpose(__lowerCamelCase      ,		(0, 3, 1, 2)						)
     __A					:		List[str]						=  tf.transpose(__lowerCamelCase      ,		(0, 3, 1, 2)						)
     __A					:		List[str]						=  ()
     for hidden_state in encoder_outputs[1:]:
         __A					:		Optional[Any]						=  hidden_states + tuple(tf.transpose(__lowerCamelCase      ,		(0, 3, 1, 2)						) for h in hidden_state						)

     if not return_dict:
         return (last_hidden_state, pooled_output) + hidden_states

     __A					:		Dict						=  hidden_states if output_hidden_states else None

     return TFBaseModelOutputWithPoolingAndNoAttention(
         last_hidden_state=__lowerCamelCase      ,		pooler_output=__lowerCamelCase      ,		hidden_states=__lowerCamelCase      ,		)






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""							,							SCREAMING_SNAKE_CASE__							,							)
class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(__lowerCamelCase      ,		**__lowerCamelCase						)
     __A					:		List[Any]						=  TFResNetMainLayer(config=__lowerCamelCase      ,		name='''resnet'''						)







 @add_start_docstrings_to_model_forward(__lowerCamelCase						)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC      ,		output_type=__lowerCamelCase      ,		config_class=_CONFIG_FOR_DOC      ,		modality='''vision'''      ,		expected_output=_EXPECTED_OUTPUT_SHAPE      ,		)
 @unpack_inputs
 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = False      ,		):



     '''simple docstring'''




     __A					:		Union[str, Any]						=  (
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     __A					:		str						=  return_dict if return_dict is not None else self.config.use_return_dict

     __A					:		int						=  self.resnet(
         pixel_values=__lowerCamelCase      ,		output_hidden_states=__lowerCamelCase      ,		return_dict=__lowerCamelCase      ,		training=__lowerCamelCase      ,		)
     return resnet_outputs






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """							,							SCREAMING_SNAKE_CASE__							,							)
class 	__snake_case      (	SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



     '''simple docstring'''




     super().__init__(__lowerCamelCase      ,		**__lowerCamelCase						)
     __A					:		Dict						=  config.num_labels
     __A					:		List[str]						=  TFResNetMainLayer(__lowerCamelCase      ,		name='''resnet'''						)
     # classification head
     __A					:		List[Any]						=  (
         tf.keras.layers.Dense(config.num_labels      ,		name='''classifier.1'''						)
         if config.num_labels > 0
         else tf.keras.layers.Activation('''linear'''      ,		name='''classifier.1'''						)
     )

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Optional[Any]						=  tf.keras.layers.Flatten()(__lowerCamelCase						)
     __A					:		Optional[Any]						=  self.classifier_layer(__lowerCamelCase						)
     return logits







 @add_start_docstrings_to_model_forward(__lowerCamelCase						)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT      ,		output_type=__lowerCamelCase      ,		config_class=_CONFIG_FOR_DOC      ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,		)
 @unpack_inputs
 def       UpperCamelCase__(					self      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = False      ,		):



     '''simple docstring'''




     __A					:		Optional[Any]						=  return_dict if return_dict is not None else self.config.use_return_dict

     __A					:		List[Any]						=  self.resnet(
         __lowerCamelCase      ,		output_hidden_states=__lowerCamelCase      ,		return_dict=__lowerCamelCase      ,		training=__lowerCamelCase						)

     __A					:		str						=  outputs.pooler_output if return_dict else outputs[1]

     __A					:		Union[str, Any]						=  self.classifier(__lowerCamelCase						)

     __A					:		Tuple						=  None if labels is None else self.hf_compute_loss(__lowerCamelCase      ,		__lowerCamelCase						)

     if not return_dict:
         __A					:		List[str]						=  (logits,) + outputs[2:]
         return (loss,) + output if loss is not None else output

     return TFImageClassifierOutputWithNoAttention(loss=__lowerCamelCase      ,		logits=__lowerCamelCase      ,		hidden_states=outputs.hidden_states						)











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_				=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    ["""LayoutLMTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlm import (
                  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMForMaskedLM,
                  LayoutLMForQuestionAnswering,
                  LayoutLMForSequenceClassification,
                  LayoutLMForTokenClassification,
                  LayoutLMModel,
                  LayoutLMPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlm import (
                  TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMForMaskedLM,
                  TFLayoutLMForQuestionAnswering,
                  TFLayoutLMForSequenceClassification,
                  TFLayoutLMForTokenClassification,
                  TFLayoutLMMainLayer,
                  TFLayoutLMModel,
                  TFLayoutLMPreTrainedModel,
              )

else:
       import sys

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




