
lowerCamelCase__    :		Dict          =   [
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager








from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCamelCase__    :		Union[str, Any]          =   logging.get_logger(__name__)

# General docstring
lowerCamelCase__    :		List[str]          =   """RegNetConfig"""

# Base docstring
lowerCamelCase__    :		str          =   """facebook/regnet-y-040"""
lowerCamelCase__    :		str          =   [1, 1_0_8_8, 7, 7]

# Image classification docstring
lowerCamelCase__    :		Union[str, Any]          =   """facebook/regnet-y-040"""
lowerCamelCase__    :		str          =   """tabby, tabby cat"""

lowerCamelCase__    :		Optional[Any]          =   [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



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


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   # The padding and conv has been verified in
   # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
   lowercase__  :   str    		=      tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2)
   lowercase__  :   Optional[int]    		=      tf.keras.layers.ConvaD(
       filters=SCREAMING_SNAKE_CASE_					,  kernel_size=SCREAMING_SNAKE_CASE_					,  strides=SCREAMING_SNAKE_CASE_					,  padding="""VALID"""					,  groups=SCREAMING_SNAKE_CASE_					,  use_bias=SCREAMING_SNAKE_CASE_					,  name="""convolution"""					,  )
   lowercase__  :   str    		=      tf.keras.layers.BatchNormalization(epsilon=1E-5					,  momentum=0.9					,  name="""normalization""")
   lowercase__  :   Any    		=      ACTaFN[activation] if activation is not None else tf.identity



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.convolution(self.padding(SCREAMING_SNAKE_CASE_))
   lowercase__  :   List[Any]    		=      self.normalization(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      self.activation(SCREAMING_SNAKE_CASE_)
   return hidden_state



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


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      config.num_channels
   lowercase__  :   int    		=      TFRegNetConvLayer(
       out_channels=config.embedding_size					,  kernel_size=3					,  stride=2					,  activation=config.hidden_act					,  name="""embedder"""					,  )



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      shape_list(SCREAMING_SNAKE_CASE_)[1]
   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.""")

   # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
   # So change the input format from `NCHW` to `NHWC`.
   # shape = (batch_size, in_height, in_width, in_channels=num_channels)
   lowercase__  :   Optional[int]    		=      tf.transpose(SCREAMING_SNAKE_CASE_					,  perm=(0, 2, 3, 1))
   lowercase__  :   Optional[int]    		=      self.embedder(SCREAMING_SNAKE_CASE_)
   return hidden_state



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


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      tf.keras.layers.ConvaD(
       filters=SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  strides=SCREAMING_SNAKE_CASE_					,  use_bias=SCREAMING_SNAKE_CASE_					,  name="""convolution""")
   lowercase__  :   str    		=      tf.keras.layers.BatchNormalization(epsilon=1E-5					,  momentum=0.9					,  name="""normalization""")



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = False):


   '''simple docstring'''




   return self.normalization(self.convolution(SCREAMING_SNAKE_CASE_)					,  training=SCREAMING_SNAKE_CASE_)



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


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_					,  name="""pooler""")
   lowercase__  :   Tuple    		=      [
       tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  activation="""relu"""					,  name="""attention.0"""),
       tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  activation="""sigmoid"""					,  name="""attention.2"""),
   ]



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self.pooler(SCREAMING_SNAKE_CASE_)
   for layer_module in self.attention:
     lowercase__  :   Optional[Any]    		=      layer_module(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      hidden_state * pooled
   return hidden_state



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


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = 1					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      in_channels != out_channels or stride != 1
   lowercase__  :   Any    		=      max(1					,  out_channels // config.groups_width)
   lowercase__  :   List[Any]    		=      (
       TFRegNetShortCut(SCREAMING_SNAKE_CASE_					,  stride=SCREAMING_SNAKE_CASE_					,  name="""shortcut""")
       if should_apply_shortcut
       else tf.keras.layers.Activation("""linear"""					,  name="""shortcut""")
   )
   # `self.layers` instead of `self.layer` because that is a reserved argument.
   lowercase__  :   Optional[int]    		=      [
       TFRegNetConvLayer(SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  activation=config.hidden_act					,  name="""layer.0"""),
       TFRegNetConvLayer(
           SCREAMING_SNAKE_CASE_					,  stride=SCREAMING_SNAKE_CASE_					,  groups=SCREAMING_SNAKE_CASE_					,  activation=config.hidden_act					,  name="""layer.1"""),
       TFRegNetConvLayer(SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  activation=SCREAMING_SNAKE_CASE_					,  name="""layer.2"""),
   ]
   lowercase__  :   Tuple    		=      ACTaFN[config.hidden_act]



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      hidden_state
   for layer_module in self.layers:
     lowercase__  :   Any    		=      layer_module(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      self.shortcut(SCREAMING_SNAKE_CASE_)
   hidden_state += residual
   lowercase__  :   Any    		=      self.activation(SCREAMING_SNAKE_CASE_)
   return hidden_state



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


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = 1					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      in_channels != out_channels or stride != 1
   lowercase__  :   str    		=      max(1					,  out_channels // config.groups_width)
   lowercase__  :   int    		=      (
       TFRegNetShortCut(SCREAMING_SNAKE_CASE_					,  stride=SCREAMING_SNAKE_CASE_					,  name="""shortcut""")
       if should_apply_shortcut
       else tf.keras.layers.Activation("""linear"""					,  name="""shortcut""")
   )
   lowercase__  :   Any    		=      [
       TFRegNetConvLayer(SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  activation=config.hidden_act					,  name="""layer.0"""),
       TFRegNetConvLayer(
           SCREAMING_SNAKE_CASE_					,  stride=SCREAMING_SNAKE_CASE_					,  groups=SCREAMING_SNAKE_CASE_					,  activation=config.hidden_act					,  name="""layer.1"""),
       TFRegNetSELayer(SCREAMING_SNAKE_CASE_					,  reduced_channels=int(round(in_channels / 4))					,  name="""layer.2"""),
       TFRegNetConvLayer(SCREAMING_SNAKE_CASE_					,  kernel_size=1					,  activation=SCREAMING_SNAKE_CASE_					,  name="""layer.3"""),
   ]
   lowercase__  :   Union[str, Any]    		=      ACTaFN[config.hidden_act]



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      hidden_state
   for layer_module in self.layers:
     lowercase__  :   Optional[Any]    		=      layer_module(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[int]    		=      self.shortcut(SCREAMING_SNAKE_CASE_)
   hidden_state += residual
   lowercase__  :   Optional[int]    		=      self.activation(SCREAMING_SNAKE_CASE_)
   return hidden_state



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


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
   lowercase__  :   Dict    		=      [
       # downsampling is done in the first layer with stride of 2
       layer(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  stride=SCREAMING_SNAKE_CASE_					,  name="""layers.0"""),
       *[layer(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  name=f'layers.{i+1}') for i in range(depth - 1)],
   ]



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   for layer_module in self.layers:
     lowercase__  :   Tuple    		=      layer_module(SCREAMING_SNAKE_CASE_)
   return hidden_state



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


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      []
   # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
   self.stages.append(
       TFRegNetStage(
           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"""					,  ))
   lowercase__  :   List[Any]    		=      zip(config.hidden_sizes					,  config.hidden_sizes[1:])
   for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE_					,  config.depths[1:])):
     self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  depth=SCREAMING_SNAKE_CASE_					,  name=f'stages.{i+1}'))



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = False					,  SCREAMING_SNAKE_CASE_ = True):


   '''simple docstring'''




   lowercase__  :   Any    		=      () if output_hidden_states else None

   for stage_module in self.stages:
     if output_hidden_states:
       lowercase__  :   List[Any]    		=      hidden_states + (hidden_state,)

     lowercase__  :   str    		=      stage_module(SCREAMING_SNAKE_CASE_)

   if output_hidden_states:
     lowercase__  :   Optional[int]    		=      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_)



@keras_serializable
class       _snake_case   (	tf.keras.layers.Layer							):
 __lowerCAmelCase  :     List[Any]        =			RegNetConfig


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


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   Dict    		=      config
   lowercase__  :   List[Any]    		=      TFRegNetEmbeddings(SCREAMING_SNAKE_CASE_					,  name="""embedder""")
   lowercase__  :   Any    		=      TFRegNetEncoder(SCREAMING_SNAKE_CASE_					,  name="""encoder""")
   lowercase__  :   Optional[int]    		=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_					,  name="""pooler""")



 @unpack_inputs
 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = False					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      (
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowercase__  :   str    		=      return_dict if return_dict is not None else self.config.use_return_dict

   lowercase__  :   Any    		=      self.embedder(SCREAMING_SNAKE_CASE_					,  training=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      self.encoder(
       SCREAMING_SNAKE_CASE_					,  output_hidden_states=SCREAMING_SNAKE_CASE_					,  return_dict=SCREAMING_SNAKE_CASE_					,  training=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[int]    		=      encoder_outputs[0]
   lowercase__  :   Tuple    		=      self.pooler(SCREAMING_SNAKE_CASE_)

   # Change to NCHW output format have uniformity in the modules
   lowercase__  :   Dict    		=      tf.transpose(SCREAMING_SNAKE_CASE_					,  perm=(0, 3, 1, 2))
   lowercase__  :   Optional[Any]    		=      tf.transpose(SCREAMING_SNAKE_CASE_					,  perm=(0, 3, 1, 2))

   # Change the other hidden state outputs to NCHW as well
   if output_hidden_states:
     lowercase__  :   Optional[int]    		=      tuple([tf.transpose(SCREAMING_SNAKE_CASE_					,  perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])

   if not return_dict:
     return (last_hidden_state, pooled_output) + encoder_outputs[1:]

   return TFBaseModelOutputWithPoolingAndNoAttention(
       last_hidden_state=SCREAMING_SNAKE_CASE_					,  pooler_output=SCREAMING_SNAKE_CASE_					,  hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states					,  )



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Dict        =			RegNetConfig
 __lowerCAmelCase  :     Any        =			'regnet'
 __lowerCAmelCase  :     List[str]        =			'pixel_values'


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)					,  dtype=tf.floataa)}

lowerCamelCase__    :		str          =   R"""
    Parameters:
    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.
        config ([`RegNetConfig`]): 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.
"""

lowerCamelCase__    :		Dict          =   R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__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.
"""



@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'  ,						UpperCAmelCase_  ,						)
class       _snake_case   (	UpperCAmelCase_							):


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


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      TFRegNetMainLayer(SCREAMING_SNAKE_CASE_					,  name="""regnet""")



 @unpack_inputs
 @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					,  )
 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_=False					,  ):


   '''simple docstring'''




   lowercase__  :   int    		=      (
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowercase__  :   List[str]    		=      return_dict if return_dict is not None else self.config.use_return_dict

   lowercase__  :   Dict    		=      self.regnet(
       pixel_values=SCREAMING_SNAKE_CASE_					,  output_hidden_states=SCREAMING_SNAKE_CASE_					,  return_dict=SCREAMING_SNAKE_CASE_					,  training=SCREAMING_SNAKE_CASE_					,  )
   if not return_dict:
     return (outputs[0],) + outputs[1:]

   return TFBaseModelOutputWithPoolingAndNoAttention(
       last_hidden_state=outputs.last_hidden_state					,  pooler_output=outputs.pooler_output					,  hidden_states=outputs.hidden_states					,  )



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


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


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      config.num_labels
   lowercase__  :   Optional[int]    		=      TFRegNetMainLayer(SCREAMING_SNAKE_CASE_					,  name="""regnet""")
   # classification head
   lowercase__  :   Any    		=      [
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(config.num_labels					,  name="""classifier.1""") if config.num_labels > 0 else tf.identity,
   ]



 @unpack_inputs
 @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					,  )
 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_=False					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      (
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowercase__  :   Optional[Any]    		=      return_dict if return_dict is not None else self.config.use_return_dict

   lowercase__  :   List[str]    		=      self.regnet(
       SCREAMING_SNAKE_CASE_					,  output_hidden_states=SCREAMING_SNAKE_CASE_					,  return_dict=SCREAMING_SNAKE_CASE_					,  training=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Dict    		=      outputs.pooler_output if return_dict else outputs[1]

   lowercase__  :   Optional[int]    		=      self.classifier[0](SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      self.classifier[1](SCREAMING_SNAKE_CASE_)

   lowercase__  :   str    		=      None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE_					,  logits=SCREAMING_SNAKE_CASE_)

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

   return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE_					,  logits=SCREAMING_SNAKE_CASE_					,  hidden_states=outputs.hidden_states)






