


'''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


__UpperCAmelCase     :Optional[Any]												=  logging.get_logger(__name__)

# General docstring
__UpperCAmelCase     :Optional[Any]												=  """ResNetConfig"""

# Base docstring
__UpperCAmelCase     :Optional[Any]												=  """microsoft/resnet-50"""
__UpperCAmelCase     :List[Any]												=  [1, 2_0_4_8, 7, 7]

# Image classification docstring
__UpperCAmelCase     :str												=  """microsoft/resnet-50"""
__UpperCAmelCase     :int												=  """tiger cat"""

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



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


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						List[Any]				,					snake_case	:						List[str] = 3				,					snake_case	:						Optional[Any] = 1				,					snake_case	:						Any = "relu"				,					**snake_case	:						List[Any]     )		->				Any:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Union[str, Any]    				=     kernel_size // 2
          __UpperCAmelCase      :					Optional[Any]    				=     tf.keras.layers.ConvaD(
              __SCREAMING_SNAKE_CASE				,					kernel_size=__SCREAMING_SNAKE_CASE				,					strides=__SCREAMING_SNAKE_CASE				,					padding='''valid'''				,					use_bias=__SCREAMING_SNAKE_CASE				,					name='''convolution'''     )
          # Use same default momentum and epsilon as PyTorch equivalent
          __UpperCAmelCase      :					Dict    				=     tf.keras.layers.BatchNormalization(epsilon=1E-5				,					momentum=0.9				,					name='''normalization'''     )
          __UpperCAmelCase      :					Optional[Any]    				=     ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''     )

     def 					lowerCamelCase__					(							self	:						int				,					snake_case	:						Any     )		->				str:
          __UpperCAmelCase      :					Optional[int]    				=     (self.pad_value, self.pad_value)
          __UpperCAmelCase      :					str    				=     tf.pad(__SCREAMING_SNAKE_CASE				,					[(0, 0), height_pad, width_pad, (0, 0)]     )
          __UpperCAmelCase      :					Union[str, Any]    				=     self.conv(__SCREAMING_SNAKE_CASE     )
          return hidden_state






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						int				,					snake_case	:						Dict = False     )		->				Optional[Any]:
          __UpperCAmelCase      :					Optional[int]    				=     self.convolution(__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Dict    				=     self.normalization(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Optional[int]    				=     self.activation(__SCREAMING_SNAKE_CASE     )
          return hidden_state



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


     """simple docstring"""






     def __init__(							self	:						Optional[Any]				,					snake_case	:						List[str]				,					**snake_case	:						Any     )		->				Union[str, Any]:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					List[str]    				=     TFResNetConvLayer(
              config.embedding_size				,					kernel_size=7				,					stride=2				,					activation=config.hidden_act				,					name='''embedder'''				,					)
          __UpperCAmelCase      :					List[str]    				=     tf.keras.layers.MaxPoolaD(pool_size=3				,					strides=2				,					padding='''valid'''				,					name='''pooler'''     )
          __UpperCAmelCase      :					int    				=     config.num_channels






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						List[str]				,					snake_case	:						Dict = False     )		->				Union[str, Any]:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					List[Any]    				=     shape_list(__SCREAMING_SNAKE_CASE     )
          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.'''     )
          __UpperCAmelCase      :					Union[str, Any]    				=     pixel_values
          __UpperCAmelCase      :					List[Any]    				=     self.embedder(__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					List[str]    				=     tf.pad(__SCREAMING_SNAKE_CASE				,					[[0, 0], [1, 1], [1, 1], [0, 0]]     )
          __UpperCAmelCase      :					Any    				=     self.pooler(__SCREAMING_SNAKE_CASE     )
          return hidden_state



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


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						List[str]				,					snake_case	:						List[Any] = 2				,					**snake_case	:						Union[str, Any]     )		->				str:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Any    				=     tf.keras.layers.ConvaD(
              __SCREAMING_SNAKE_CASE				,					kernel_size=1				,					strides=__SCREAMING_SNAKE_CASE				,					use_bias=__SCREAMING_SNAKE_CASE				,					name='''convolution'''     )
          # Use same default momentum and epsilon as PyTorch equivalent
          __UpperCAmelCase      :					str    				=     tf.keras.layers.BatchNormalization(epsilon=1E-5				,					momentum=0.9				,					name='''normalization'''     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						List[Any]				,					snake_case	:						str = False     )		->				Optional[Any]:
          __UpperCAmelCase      :					int    				=     x
          __UpperCAmelCase      :					str    				=     self.convolution(__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					str    				=     self.normalization(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          return hidden_state



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


     """simple docstring"""






     def __init__(							self	:						int				,					snake_case	:						List[str]				,					snake_case	:						int				,					snake_case	:						Any = 1				,					snake_case	:						List[str] = "relu"				,					**snake_case	:						str     )		->				Dict:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Optional[Any]    				=     in_channels != out_channels or stride != 1
          __UpperCAmelCase      :					Any    				=     TFResNetConvLayer(__SCREAMING_SNAKE_CASE				,					stride=__SCREAMING_SNAKE_CASE				,					name='''layer.0'''     )
          __UpperCAmelCase      :					Tuple    				=     TFResNetConvLayer(__SCREAMING_SNAKE_CASE				,					activation=__SCREAMING_SNAKE_CASE				,					name='''layer.1'''     )
          __UpperCAmelCase      :					Any    				=     (
              TFResNetShortCut(__SCREAMING_SNAKE_CASE				,					stride=__SCREAMING_SNAKE_CASE				,					name='''shortcut'''     )
              if should_apply_shortcut
              else tf.keras.layers.Activation('''linear'''				,					name='''shortcut'''     )
          )
          __UpperCAmelCase      :					Optional[Any]    				=     ACTaFN[activation]






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						str				,					snake_case	:						Optional[int] = False     )		->				str:
          __UpperCAmelCase      :					Tuple    				=     hidden_state
          __UpperCAmelCase      :					Union[str, Any]    				=     self.conva(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Tuple    				=     self.conva(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Optional[int]    				=     self.shortcut(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          hidden_state += residual
          __UpperCAmelCase      :					List[str]    				=     self.activation(__SCREAMING_SNAKE_CASE     )
          return hidden_state



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


     """simple docstring"""






     def __init__(							self	:						Dict				,					snake_case	:						int				,					snake_case	:						List[Any]				,					snake_case	:						Dict = 1				,					snake_case	:						List[Any] = "relu"				,					snake_case	:						Dict = 4				,					**snake_case	:						List[Any]				,					)		->				Tuple:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Union[str, Any]    				=     in_channels != out_channels or stride != 1
          __UpperCAmelCase      :					List[Any]    				=     out_channels // reduction
          __UpperCAmelCase      :					List[str]    				=     TFResNetConvLayer(__SCREAMING_SNAKE_CASE				,					kernel_size=1				,					name='''layer.0'''     )
          __UpperCAmelCase      :					Any    				=     TFResNetConvLayer(__SCREAMING_SNAKE_CASE				,					stride=__SCREAMING_SNAKE_CASE				,					name='''layer.1'''     )
          __UpperCAmelCase      :					List[str]    				=     TFResNetConvLayer(__SCREAMING_SNAKE_CASE				,					kernel_size=1				,					activation=__SCREAMING_SNAKE_CASE				,					name='''layer.2'''     )
          __UpperCAmelCase      :					str    				=     (
              TFResNetShortCut(__SCREAMING_SNAKE_CASE				,					stride=__SCREAMING_SNAKE_CASE				,					name='''shortcut'''     )
              if should_apply_shortcut
              else tf.keras.layers.Activation('''linear'''				,					name='''shortcut'''     )
          )
          __UpperCAmelCase      :					Optional[int]    				=     ACTaFN[activation]






     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						str				,					snake_case	:						Tuple = False     )		->				Dict:
          __UpperCAmelCase      :					Optional[Any]    				=     hidden_state
          __UpperCAmelCase      :					int    				=     self.conva(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Any    				=     self.conva(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					str    				=     self.conva(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Any    				=     self.shortcut(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          hidden_state += residual
          __UpperCAmelCase      :					Dict    				=     self.activation(__SCREAMING_SNAKE_CASE     )
          return hidden_state



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


     """simple docstring"""






     def __init__(							self	:						Optional[Any]				,					snake_case	:						Optional[int]				,					snake_case	:						List[str]				,					snake_case	:						Any				,					snake_case	:						Any = 2				,					snake_case	:						Union[str, Any] = 2				,					**snake_case	:						int     )		->				Optional[int]:
          super().__init__(**__SCREAMING_SNAKE_CASE     )

          __UpperCAmelCase      :					Union[str, Any]    				=     TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

          __UpperCAmelCase      :					Optional[int]    				=     [layer(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE				,					stride=__SCREAMING_SNAKE_CASE				,					activation=config.hidden_act				,					name='''layers.0'''     )]
          layers += [
              layer(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE				,					activation=config.hidden_act				,					name=f'layers.{i + 1}'     )
              for i in range(depth - 1     )
          ]
          __UpperCAmelCase      :					int    				=     layers






     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						Optional[int]				,					snake_case	:						Any = False     )		->				Optional[Any]:

          for layer in self.stage_layers:
               __UpperCAmelCase      :					Any    				=     layer(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )
          return hidden_state



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


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						Tuple				,					**snake_case	:						str     )		->				Any:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          __UpperCAmelCase      :					Union[str, Any]    				=     [
              TFResNetStage(
                  __SCREAMING_SNAKE_CASE				,					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(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE				,					depth=__SCREAMING_SNAKE_CASE				,					name=f'stages.{i + 1}'     )     )






     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						Union[str, Any]				,					snake_case	:						Any = False				,					snake_case	:						Tuple = True				,					snake_case	:						Optional[int] = False				,					)		->				List[Any]:
          __UpperCAmelCase      :					int    				=     () if output_hidden_states else None

          for stage_module in self.stages:
               if output_hidden_states:
                    __UpperCAmelCase      :					Union[str, Any]    				=     hidden_states + (hidden_state,)

               __UpperCAmelCase      :					Optional[Any]    				=     stage_module(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )

          if output_hidden_states:
               __UpperCAmelCase      :					List[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=__SCREAMING_SNAKE_CASE				,					hidden_states=__SCREAMING_SNAKE_CASE     )



class       a							(						__lowerCAmelCase			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str				  =  ResNetConfig
     SCREAMING_SNAKE_CASE			:					Optional[int]				  =  """resnet"""
     SCREAMING_SNAKE_CASE			:					Union[str, Any]				  =  """pixel_values"""

     @property
     def 					lowerCamelCase__					(							self	:						Dict     )		->				Dict:
          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)				,					dtype=tf.floataa     )}



__UpperCAmelCase     :Dict												=  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.
"""


__UpperCAmelCase     :Tuple												=  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       a							(						tf.keras.layers.Layer			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					Optional[int]				  =  ResNetConfig

     def __init__(							self	:						str				,					snake_case	:						Tuple				,					**snake_case	:						str     )		->				Any:
          super().__init__(**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Union[str, Any]    				=     config
          __UpperCAmelCase      :					Any    				=     TFResNetEmbeddings(__SCREAMING_SNAKE_CASE				,					name='''embedder'''     )
          __UpperCAmelCase      :					int    				=     TFResNetEncoder(__SCREAMING_SNAKE_CASE				,					name='''encoder'''     )
          __UpperCAmelCase      :					Any    				=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE     )






     @unpack_inputs
     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						Any				,					snake_case	:						List[str] = None				,					snake_case	:						int = None				,					snake_case	:						Union[str, Any] = False				,					)		->				Union[str, Any]:
          __UpperCAmelCase      :					str    				=     (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          __UpperCAmelCase      :					int    				=     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)
          __UpperCAmelCase      :					List[str]    				=     tf.transpose(__SCREAMING_SNAKE_CASE				,					perm=[0, 2, 3, 1]     )
          __UpperCAmelCase      :					Dict    				=     self.embedder(__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )

          __UpperCAmelCase      :					Dict    				=     self.encoder(
              __SCREAMING_SNAKE_CASE				,					output_hidden_states=__SCREAMING_SNAKE_CASE				,					return_dict=__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )

          __UpperCAmelCase      :					List[Any]    				=     encoder_outputs[0]

          __UpperCAmelCase      :					Any    				=     self.pooler(__SCREAMING_SNAKE_CASE     )

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

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

          __UpperCAmelCase      :					int    				=     hidden_states if output_hidden_states else None

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



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


     """simple docstring"""






     def __init__(							self	:						List[Any]				,					snake_case	:						int				,					**snake_case	:						Any     )		->				Tuple:
          super().__init__(__SCREAMING_SNAKE_CASE				,					**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Optional[Any]    				=     TFResNetMainLayer(config=__SCREAMING_SNAKE_CASE				,					name='''resnet'''     )






     @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE     )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC				,					output_type=__SCREAMING_SNAKE_CASE				,					config_class=_CONFIG_FOR_DOC				,					modality='''vision'''				,					expected_output=_EXPECTED_OUTPUT_SHAPE				,					)
     @unpack_inputs
     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						str				,					snake_case	:						Optional[Any] = None				,					snake_case	:						Union[str, Any] = None				,					snake_case	:						Union[str, Any] = False				,					)		->				Tuple:
          __UpperCAmelCase      :					Tuple    				=     (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          __UpperCAmelCase      :					Dict    				=     return_dict if return_dict is not None else self.config.use_return_dict

          __UpperCAmelCase      :					int    				=     self.resnet(
              pixel_values=__SCREAMING_SNAKE_CASE				,					output_hidden_states=__SCREAMING_SNAKE_CASE				,					return_dict=__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE				,					)
          return resnet_outputs



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


     """simple docstring"""






     def __init__(							self	:						str				,					snake_case	:						int				,					**snake_case	:						Optional[Any]     )		->				List[Any]:
          super().__init__(__SCREAMING_SNAKE_CASE				,					**__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					int    				=     config.num_labels
          __UpperCAmelCase      :					List[str]    				=     TFResNetMainLayer(__SCREAMING_SNAKE_CASE				,					name='''resnet'''     )
          # classification head
          __UpperCAmelCase      :					List[str]    				=     (
              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 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						Optional[Any]     )		->				List[str]:
          __UpperCAmelCase      :					Tuple    				=     tf.keras.layers.Flatten()(__SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Dict    				=     self.classifier_layer(__SCREAMING_SNAKE_CASE     )
          return logits






     @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE     )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT				,					output_type=__SCREAMING_SNAKE_CASE				,					config_class=_CONFIG_FOR_DOC				,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,					)
     @unpack_inputs
     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						Any = None				,					snake_case	:						Dict = None				,					snake_case	:						str = None				,					snake_case	:						Optional[Any] = None				,					snake_case	:						Union[str, Any] = False				,					)		->				List[Any]:
          __UpperCAmelCase      :					int    				=     return_dict if return_dict is not None else self.config.use_return_dict

          __UpperCAmelCase      :					Optional[int]    				=     self.resnet(
              __SCREAMING_SNAKE_CASE				,					output_hidden_states=__SCREAMING_SNAKE_CASE				,					return_dict=__SCREAMING_SNAKE_CASE				,					training=__SCREAMING_SNAKE_CASE     )

          __UpperCAmelCase      :					Union[str, Any]    				=     outputs.pooler_output if return_dict else outputs[1]

          __UpperCAmelCase      :					Dict    				=     self.classifier(__SCREAMING_SNAKE_CASE     )

          __UpperCAmelCase      :					str    				=     None if labels is None else self.hf_compute_loss(__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE     )

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

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






"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__       :  List[Any]       		=					logging.get_logger(__name__)

a__       :  int       		=					{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				int									=  """mpnet"""

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=3_0527 ,       __SCREAMING_SNAKE_CASE=768 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=3072 ,       __SCREAMING_SNAKE_CASE="gelu" ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=512 ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=1e-12 ,       __SCREAMING_SNAKE_CASE=32 ,       __SCREAMING_SNAKE_CASE=1 ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=2 ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,       bos_token_id=__SCREAMING_SNAKE_CASE ,       eos_token_id=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					vocab_size
        snake_case__     				=					hidden_size
        snake_case__     				=					num_hidden_layers
        snake_case__     				=					num_attention_heads
        snake_case__     				=					hidden_act
        snake_case__     				=					intermediate_size
        snake_case__     				=					hidden_dropout_prob
        snake_case__     				=					attention_probs_dropout_prob
        snake_case__     				=					max_position_embeddings
        snake_case__     				=					initializer_range
        snake_case__     				=					layer_norm_eps
        snake_case__     				=					relative_attention_num_buckets

