





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

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

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

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

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



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

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					kernel_size // 2
        snake_case__     				=					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
        snake_case__     				=					tf.keras.layers.BatchNormalization(epsilon=1e-5 ,       momentum=0.9 ,       name="normalization"  )
        snake_case__     				=					ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					snake_case__     				=					(self.pad_value, self.pad_value)
        snake_case__     				=					tf.pad(__SCREAMING_SNAKE_CASE ,       [(0, 0), height_pad, width_pad, (0, 0)]  )
        snake_case__     				=					self.conv(__SCREAMING_SNAKE_CASE  )
        return hidden_state

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False  ):






        '''simple docstring'''

        snake_case__     				=					self.convolution(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.normalization(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.activation(__SCREAMING_SNAKE_CASE  )
        return hidden_state



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

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					TFResNetConvLayer(
            config.embedding_size ,       kernel_size=7 ,       stride=2 ,       activation=config.hidden_act ,       name="embedder" ,       )
        snake_case__     				=					tf.keras.layers.MaxPoolaD(pool_size=3 ,       strides=2 ,       padding="valid" ,       name="pooler"  )
        snake_case__     				=					config.num_channels

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False  ):






        '''simple docstring'''

        snake_case__				, snake_case__				, snake_case__				, snake_case__     				=					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."  )
        snake_case__     				=					pixel_values
        snake_case__     				=					self.embedder(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					tf.pad(__SCREAMING_SNAKE_CASE ,       [[0, 0], [1, 1], [1, 1], [0, 0]]  )
        snake_case__     				=					self.pooler(__SCREAMING_SNAKE_CASE  )
        return hidden_state



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

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					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
        snake_case__     				=					tf.keras.layers.BatchNormalization(epsilon=1e-5 ,       momentum=0.9 ,       name="normalization"  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False  ):






        '''simple docstring'''

        snake_case__     				=					x
        snake_case__     				=					self.convolution(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.normalization(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        return hidden_state



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

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					in_channels != out_channels or stride != 1
        snake_case__     				=					TFResNetConvLayer(__SCREAMING_SNAKE_CASE ,       stride=__SCREAMING_SNAKE_CASE ,       name="layer.0"  )
        snake_case__     				=					TFResNetConvLayer(__SCREAMING_SNAKE_CASE ,       activation=__SCREAMING_SNAKE_CASE ,       name="layer.1"  )
        snake_case__     				=					(
            TFResNetShortCut(__SCREAMING_SNAKE_CASE ,       stride=__SCREAMING_SNAKE_CASE ,       name="shortcut"  )
            if should_apply_shortcut
            else tf.keras.layers.Activation("linear" ,       name="shortcut"  )
        )
        snake_case__     				=					ACTaFN[activation]

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False  ):






        '''simple docstring'''

        snake_case__     				=					hidden_state
        snake_case__     				=					self.conva(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.conva(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.shortcut(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        hidden_state += residual
        snake_case__     				=					self.activation(__SCREAMING_SNAKE_CASE  )
        return hidden_state



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

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					in_channels != out_channels or stride != 1
        snake_case__     				=					out_channels // reduction
        snake_case__     				=					TFResNetConvLayer(__SCREAMING_SNAKE_CASE ,       kernel_size=1 ,       name="layer.0"  )
        snake_case__     				=					TFResNetConvLayer(__SCREAMING_SNAKE_CASE ,       stride=__SCREAMING_SNAKE_CASE ,       name="layer.1"  )
        snake_case__     				=					TFResNetConvLayer(__SCREAMING_SNAKE_CASE ,       kernel_size=1 ,       activation=__SCREAMING_SNAKE_CASE ,       name="layer.2"  )
        snake_case__     				=					(
            TFResNetShortCut(__SCREAMING_SNAKE_CASE ,       stride=__SCREAMING_SNAKE_CASE ,       name="shortcut"  )
            if should_apply_shortcut
            else tf.keras.layers.Activation("linear" ,       name="shortcut"  )
        )
        snake_case__     				=					ACTaFN[activation]

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False  ):






        '''simple docstring'''

        snake_case__     				=					hidden_state
        snake_case__     				=					self.conva(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.conva(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.conva(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					self.shortcut(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )
        hidden_state += residual
        snake_case__     				=					self.activation(__SCREAMING_SNAKE_CASE  )
        return hidden_state



class   lowercase_					(  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  )

        snake_case__     				=					TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

        snake_case__     				=					[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  )
        ]
        snake_case__     				=					layers

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False  ):






        '''simple docstring'''

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



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

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






        '''simple docstring'''

        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
        snake_case__     				=					[
            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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = False ,       __SCREAMING_SNAKE_CASE = True ,       __SCREAMING_SNAKE_CASE = False ,       ):






        '''simple docstring'''

        snake_case__     				=					() if output_hidden_states else None

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

         snake_case__     				=					stage_module(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )

        if output_hidden_states:
         snake_case__     				=					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   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				str									=  ResNetConfig
       snake_case__					:				Optional[int]									=  """resnet"""
       snake_case__					:				Union[str, Any]									=  """pixel_values"""

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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


a__       :  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   lowercase_					(  tf.keras.layers.Layer				):
       snake_case__					:				Optional[int]									=  ResNetConfig

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					config
        snake_case__     				=					TFResNetEmbeddings(__SCREAMING_SNAKE_CASE ,       name="embedder"  )
        snake_case__     				=					TFResNetEncoder(__SCREAMING_SNAKE_CASE ,       name="encoder"  )
        snake_case__     				=					tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE  )

       @unpack_inputs
       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = False ,       ):






        '''simple docstring'''

        snake_case__     				=					(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        snake_case__     				=					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)
        snake_case__     				=					tf.transpose(__SCREAMING_SNAKE_CASE ,       perm=[0, 2, 3, 1]  )
        snake_case__     				=					self.embedder(__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					self.encoder(
            __SCREAMING_SNAKE_CASE ,       output_hidden_states=__SCREAMING_SNAKE_CASE ,       return_dict=__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					encoder_outputs[0]

        snake_case__     				=					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)
        snake_case__     				=					tf.transpose(__SCREAMING_SNAKE_CASE ,       (0, 3, 1, 2)  )
        snake_case__     				=					tf.transpose(__SCREAMING_SNAKE_CASE ,       (0, 3, 1, 2)  )
        snake_case__     				=					()
        for hidden_state in encoder_outputs[1:]:
         snake_case__     				=					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

        snake_case__     				=					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   lowercase_					(  __lowerCAmelCase				):

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






        '''simple docstring'''

        super().__init__(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = False ,       ):






        '''simple docstring'''

        snake_case__     				=					(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        snake_case__     				=					return_dict if return_dict is not None else self.config.use_return_dict

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

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






        '''simple docstring'''

        super().__init__(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					config.num_labels
        snake_case__     				=					TFResNetMainLayer(__SCREAMING_SNAKE_CASE ,       name="resnet"  )
        # classification head
        snake_case__     				=					(
            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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					tf.keras.layers.Flatten()(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = False ,       ):






        '''simple docstring'''

        snake_case__     				=					return_dict if return_dict is not None else self.config.use_return_dict

        snake_case__     				=					self.resnet(
            __SCREAMING_SNAKE_CASE ,       output_hidden_states=__SCREAMING_SNAKE_CASE ,       return_dict=__SCREAMING_SNAKE_CASE ,       training=__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					outputs.pooler_output if return_dict else outputs[1]

        snake_case__     				=					self.classifier(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					None if labels is None else self.hf_compute_loss(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

        if not return_dict:
         snake_case__     				=					(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

