"""simple docstring"""




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


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

# General docstring
_a      :		Optional[int]           =   'RegNetConfig'

# Base docstring
_a      :		Union[str, Any]           =   'facebook/regnet-y-040'
_a      :		Tuple           =   [1, 1_088, 7, 7]

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

_a      :		Union[str, Any]           =   [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__ = 3				,     a__ = 1				,     a__ = 1				,     a__ = "relu"				,     ):
       super().__init__()
       _lowerCAmelCase						:       str  			=     nn.Convad(
           a__				,     a__				,     kernel_size=a__				,     stride=a__				,     padding=kernel_size // 2				,     groups=a__				,     bias=a__				,     )
       _lowerCAmelCase						:       str  			=     nn.BatchNormad(a__  )
       _lowerCAmelCase						:       Optional[int]  			=     ACTaFN[activation] if activation is not None else nn.Identity()






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     self.convolution(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.normalization(a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.activation(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Optional[int]  			=     RegNetConvLayer(
           config.num_channels				,     config.embedding_size				,     kernel_size=3				,     stride=2				,     activation=config.hidden_act  )
       _lowerCAmelCase						:       List[Any]  			=     config.num_channels






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     pixel_values.shape[1]
       if 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."""  )
       _lowerCAmelCase						:       List[Any]  			=     self.embedder(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__ = 2  ):
       super().__init__()
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Convad(a__				,     a__				,     kernel_size=1				,     stride=a__				,     bias=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     nn.BatchNormad(a__  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       str  			=     self.convolution(a__  )
       _lowerCAmelCase						:       Tuple  			=     self.normalization(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__  ):
       super().__init__()

       _lowerCAmelCase						:       Dict  			=     nn.AdaptiveAvgPoolad((1, 1)  )
       _lowerCAmelCase						:       Tuple  			=     nn.Sequential(
           nn.Convad(a__				,     a__				,     kernel_size=1  )				,     nn.ReLU()				,     nn.Convad(a__				,     a__				,     kernel_size=1  )				,     nn.Sigmoid()				,     )






     def     __A  (		self				,     a__  ):
       # b c h w -> b c 1 1
       _lowerCAmelCase						:       Tuple  			=     self.pooler(a__  )
       _lowerCAmelCase						:       int  			=     self.attention(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_state * attention
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 1  ):
       super().__init__()
       _lowerCAmelCase						:       Optional[Any]  			=     in_channels != out_channels or stride != 1
       _lowerCAmelCase						:       Optional[Any]  			=     max(1				,     out_channels // config.groups_width  )
       _lowerCAmelCase						:       Tuple  			=     (
           RegNetShortCut(a__				,     a__				,     stride=a__  ) if should_apply_shortcut else nn.Identity()
       )
       _lowerCAmelCase						:       str  			=     nn.Sequential(
           RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=config.hidden_act  )				,     RegNetConvLayer(a__				,     a__				,     stride=a__				,     groups=a__				,     activation=config.hidden_act  )				,     RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=a__  )				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     ACTaFN[config.hidden_act]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Any  			=     hidden_state
       _lowerCAmelCase						:       Any  			=     self.layer(a__  )
       _lowerCAmelCase						:       str  			=     self.shortcut(a__  )
       hidden_state += residual
       _lowerCAmelCase						:       int  			=     self.activation(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 1  ):
       super().__init__()
       _lowerCAmelCase						:       Dict  			=     in_channels != out_channels or stride != 1
       _lowerCAmelCase						:       List[Any]  			=     max(1				,     out_channels // config.groups_width  )
       _lowerCAmelCase						:       List[Any]  			=     (
           RegNetShortCut(a__				,     a__				,     stride=a__  ) if should_apply_shortcut else nn.Identity()
       )
       _lowerCAmelCase						:       Tuple  			=     nn.Sequential(
           RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=config.hidden_act  )				,     RegNetConvLayer(a__				,     a__				,     stride=a__				,     groups=a__				,     activation=config.hidden_act  )				,     RegNetSELayer(a__				,     reduced_channels=int(round(in_channels / 4  )  )  )				,     RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=a__  )				,     )
       _lowerCAmelCase						:       Dict  			=     ACTaFN[config.hidden_act]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     hidden_state
       _lowerCAmelCase						:       Optional[int]  			=     self.layer(a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.shortcut(a__  )
       hidden_state += residual
       _lowerCAmelCase						:       List[str]  			=     self.activation(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 2				,     a__ = 2				,     ):
       super().__init__()

       _lowerCAmelCase						:       List[Any]  			=     RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

       _lowerCAmelCase						:       str  			=     nn.Sequential(
           # downsampling is done in the first layer with stride of 2
           layer(
               a__				,     a__				,     a__				,     stride=a__				,     )				,     *[layer(a__				,     a__				,     a__  ) for _ in range(depth - 1  )]				,     )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.layers(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Optional[int]  			=     nn.ModuleList([]  )
       # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
       self.stages.append(
           RegNetStage(
               a__				,     config.embedding_size				,     config.hidden_sizes[0]				,     stride=2 if config.downsample_in_first_stage else 1				,     depth=config.depths[0]				,     )  )
       _lowerCAmelCase						:       List[str]  			=     zip(config.hidden_sizes				,     config.hidden_sizes[1:]  )
       for (in_channels, out_channels), depth in zip(a__				,     config.depths[1:]  ):
         self.stages.append(RegNetStage(a__				,     a__				,     a__				,     depth=a__  )  )






     def     __A  (		self				,     a__				,     a__ = False				,     a__ = True  ):
       _lowerCAmelCase						:       Any  			=     () if output_hidden_states else None

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

         _lowerCAmelCase						:       Union[str, Any]  			=     stage_module(a__  )

       if output_hidden_states:
         _lowerCAmelCase						:       Union[str, 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 BaseModelOutputWithNoAttention(last_hidden_state=a__				,     hidden_states=a__  )







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Tuple							=		RegNetConfig
     _UpperCamelCase							:			int							=		"regnet"
     _UpperCamelCase							:			List[str]							=		"pixel_values"
     _UpperCamelCase							:			Union[str, Any]							=		True






     def     __A  (		self				,     a__  ):
       if isinstance(a__				,     nn.Convad  ):
         nn.init.kaiming_normal_(module.weight				,     mode="""fan_out"""				,     nonlinearity="""relu"""  )
       elif isinstance(a__				,     (nn.BatchNormad, nn.GroupNorm)  ):
         nn.init.constant_(module.weight				,     1  )
         nn.init.constant_(module.bias				,     0  )






     def     __A  (		self				,     a__				,     a__=False  ):
       if isinstance(a__				,     a__  ):
         _lowerCAmelCase						:       Optional[int]  			=     value







_a      :		List[Any]           =   r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

_a      :		List[Any]           =   r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."    ,	SCREAMING_SNAKE_CASE_    ,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       List[Any]  			=     config
       _lowerCAmelCase						:       Any  			=     RegNetEmbeddings(a__  )
       _lowerCAmelCase						:       List[str]  			=     RegNetEncoder(a__  )
       _lowerCAmelCase						:       str  			=     nn.AdaptiveAvgPoolad((1, 1)  )
       # Initialize weights and apply final processing
       self.post_init()






     @add_start_docstrings_to_model_forward(a__  )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC				,     output_type=a__				,     config_class=_CONFIG_FOR_DOC				,     modality="""vision"""				,     expected_output=_EXPECTED_OUTPUT_SHAPE				,     )
     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None  ):
       _lowerCAmelCase						:       Any  			=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowerCAmelCase						:       Tuple  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       List[Any]  			=     self.embedder(a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     self.encoder(
           a__				,     output_hidden_states=a__				,     return_dict=a__  )

       _lowerCAmelCase						:       str  			=     encoder_outputs[0]

       _lowerCAmelCase						:       Optional[Any]  			=     self.pooler(a__  )

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

       return BaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=a__				,     pooler_output=a__				,     hidden_states=encoder_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    "    ,	SCREAMING_SNAKE_CASE_    ,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       Any  			=     config.num_labels
       _lowerCAmelCase						:       Optional[int]  			=     RegNetModel(a__  )
       # classification head
       _lowerCAmelCase						:       Optional[Any]  			=     nn.Sequential(
           nn.Flatten()				,     nn.Linear(config.hidden_sizes[-1]				,     config.num_labels  ) if config.num_labels > 0 else nn.Identity()				,     )
       # initialize weights and apply final processing
       self.post_init()






     @add_start_docstrings_to_model_forward(a__  )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT				,     output_type=a__				,     config_class=_CONFIG_FOR_DOC				,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,     )
     def     __A  (		self				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     ):
       _lowerCAmelCase						:       List[Any]  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       Union[str, Any]  			=     self.regnet(a__				,     output_hidden_states=a__				,     return_dict=a__  )

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

       _lowerCAmelCase						:       str  			=     self.classifier(a__  )

       _lowerCAmelCase						:       List[str]  			=     None

       if labels is not None:
         if self.config.problem_type is None:
           if self.num_labels == 1:
             _lowerCAmelCase						:       str  			=     """regression"""
           elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
             _lowerCAmelCase						:       Optional[int]  			=     """single_label_classification"""
           else:
             _lowerCAmelCase						:       Optional[int]  			=     """multi_label_classification"""
         if self.config.problem_type == "regression":
           _lowerCAmelCase						:       Optional[Any]  			=     MSELoss()
           if self.num_labels == 1:
             _lowerCAmelCase						:       int  			=     loss_fct(logits.squeeze()				,     labels.squeeze()  )
           else:
             _lowerCAmelCase						:       Dict  			=     loss_fct(a__				,     a__  )
         elif self.config.problem_type == "single_label_classification":
           _lowerCAmelCase						:       Any  			=     CrossEntropyLoss()
           _lowerCAmelCase						:       Dict  			=     loss_fct(logits.view(-1				,     self.num_labels  )				,     labels.view(-1  )  )
         elif self.config.problem_type == "multi_label_classification":
           _lowerCAmelCase						:       Dict  			=     BCEWithLogitsLoss()
           _lowerCAmelCase						:       Tuple  			=     loss_fct(a__				,     a__  )

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

       return ImageClassifierOutputWithNoAttention(loss=a__				,     logits=a__				,     hidden_states=outputs.hidden_states  )





"""simple docstring"""




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Dict:
  _lowerCAmelCase						:       List[Any]  			=     torch.exp(_lowerCamelCase	)
  _lowerCAmelCase						:       List[Any]  			=     torch.sum(_lowerCamelCase			,dim=1	)  # sum of exp(x_i)
  _lowerCAmelCase						:       Dict  			=     torch.sum(x * exp_x			,dim=1	)  # sum of x_i * exp(x_i)
  return torch.log(_lowerCamelCase	) - B / A
class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       int  			=     config.output_attentions
       _lowerCAmelCase						:       Any  			=     config.output_hidden_states
       _lowerCAmelCase						:       List[Any]  			=     nn.ModuleList([BertLayer(a__  ) for _ in range(config.num_hidden_layers  )]  )
       _lowerCAmelCase						:       Any  			=     nn.ModuleList([BertHighway(a__  ) for _ in range(config.num_hidden_layers  )]  )

       _lowerCAmelCase						:       str  			=     [-1 for _ in range(config.num_hidden_layers  )]






     def     __A  (		self				,     a__  ):
       if (type(a__  ) is float) or (type(a__  ) is int):
         for i in range(len(self.early_exit_entropy  )  ):
           _lowerCAmelCase						:       Tuple  			=     x
       else:
         _lowerCAmelCase						:       Optional[int]  			=     x






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     pooler.state_dict()
       for highway in self.highway:
         for name, param in highway.pooler.state_dict().items():
           param.copy_(loaded_model[name]  )






     def     __A  (		self				,     a__				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     ):
       _lowerCAmelCase						:       Any  			=     ()
       _lowerCAmelCase						:       Optional[int]  			=     ()
       _lowerCAmelCase						:       List[Any]  			=     ()
       for i, layer_module in enumerate(self.layer  ):
         if self.output_hidden_states:
           _lowerCAmelCase						:       str  			=     all_hidden_states + (hidden_states,)

         _lowerCAmelCase						:       List[str]  			=     layer_module(
             a__				,     a__				,     head_mask[i]				,     a__				,     a__  )
         _lowerCAmelCase						:       Union[str, Any]  			=     layer_outputs[0]

         if self.output_attentions:
           _lowerCAmelCase						:       Dict  			=     all_attentions + (layer_outputs[1],)

         _lowerCAmelCase						:       Optional[int]  			=     (hidden_states,)
         if self.output_hidden_states:
           _lowerCAmelCase						:       Union[str, Any]  			=     current_outputs + (all_hidden_states,)
         if self.output_attentions:
           _lowerCAmelCase						:       Optional[int]  			=     current_outputs + (all_attentions,)

         _lowerCAmelCase						:       Optional[Any]  			=     self.highway[i](a__  )
         # logits, pooled_output

         if not self.training:
           _lowerCAmelCase						:       Tuple  			=     highway_exit[0]
           _lowerCAmelCase						:       Any  			=     entropy(a__  )
           _lowerCAmelCase						:       Optional[Any]  			=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
           _lowerCAmelCase						:       Union[str, Any]  			=     all_highway_exits + (highway_exit,)

           if highway_entropy < self.early_exit_entropy[i]:
             _lowerCAmelCase						:       List[str]  			=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
             raise HighwayException(a__				,     i + 1  )
         else:
           _lowerCAmelCase						:       Dict  			=     all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[Any]  			=     all_hidden_states + (hidden_states,)

       _lowerCAmelCase						:       List[Any]  			=     (hidden_states,)
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[str]  			=     outputs + (all_hidden_states,)
       if self.output_attentions:
         _lowerCAmelCase						:       Any  			=     outputs + (all_attentions,)

       _lowerCAmelCase						:       Optional[int]  			=     outputs + (all_highway_exits,)
       return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       Any  			=     config

       _lowerCAmelCase						:       Tuple  			=     BertEmbeddings(a__  )
       _lowerCAmelCase						:       Tuple  			=     DeeBertEncoder(a__  )
       _lowerCAmelCase						:       List[str]  			=     BertPooler(a__  )

       self.init_weights()






     def     __A  (		self  ):
       self.encoder.init_highway_pooler(self.pooler  )






     def     __A  (		self  ):
       return self.embeddings.word_embeddings






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     value






     def     __A  (		self				,     a__  ):
       for layer, heads in heads_to_prune.items():
         self.encoder.layer[layer].attention.prune_heads(a__  )






     @add_start_docstrings_to_model_forward(a__  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     ):
       if input_ids is not None and inputs_embeds is not None:
         raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""  )
       elif input_ids is not None:
         _lowerCAmelCase						:       Any  			=     input_ids.size()
       elif inputs_embeds is not None:
         _lowerCAmelCase						:       List[str]  			=     inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

       _lowerCAmelCase						:       str  			=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
         _lowerCAmelCase						:       List[Any]  			=     torch.ones(a__				,     device=a__  )
       if encoder_attention_mask is None:
         _lowerCAmelCase						:       Optional[Any]  			=     torch.ones(a__				,     device=a__  )
       if token_type_ids is None:
         _lowerCAmelCase						:       Dict  			=     torch.zeros(a__				,     dtype=torch.long				,     device=a__  )

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       _lowerCAmelCase						:       torch.Tensor  			=     self.get_extended_attention_mask(a__				,     a__				,     a__  )

       # If a 2D ou 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if encoder_attention_mask.dim() == 3:
         _lowerCAmelCase						:       Dict  			=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
         _lowerCAmelCase						:       Tuple  			=     encoder_attention_mask[:, None, None, :]

       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
       _lowerCAmelCase						:       Optional[Any]  			=     (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

       # Prepare head mask if needed
       # 1.0 in head_mask indicate we keep the head
       # attention_probs has shape bsz x n_heads x N x N
       # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
       # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
       _lowerCAmelCase						:       Optional[int]  			=     self.get_head_mask(a__				,     self.config.num_hidden_layers  )

       _lowerCAmelCase						:       Dict  			=     self.embeddings(
           input_ids=a__				,     position_ids=a__				,     token_type_ids=a__				,     inputs_embeds=a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.encoder(
           a__				,     attention_mask=a__				,     head_mask=a__				,     encoder_hidden_states=a__				,     encoder_attention_mask=a__				,     )
       _lowerCAmelCase						:       Dict  			=     encoder_outputs[0]
       _lowerCAmelCase						:       Union[str, Any]  			=     self.pooler(a__  )

       _lowerCAmelCase						:       Dict  			=     (
           sequence_output,
           pooled_output,
       ) + encoder_outputs[
           1:
       ]  # add hidden_states and attentions if they are here
       return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     message
       _lowerCAmelCase						:       str  			=     exit_layer  # start from 1!







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Any  			=     BertPooler(a__  )
       _lowerCAmelCase						:       str  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Linear(config.hidden_size				,     config.num_labels  )






     def     __A  (		self				,     a__  ):
       # Pooler
       _lowerCAmelCase						:       Tuple  			=     encoder_outputs[0]
       _lowerCAmelCase						:       int  			=     self.pooler(a__  )
       # "return" pooler_output

       # BertModel
       _lowerCAmelCase						:       Union[str, Any]  			=     (pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       _lowerCAmelCase						:       Optional[int]  			=     bmodel_output[1]

       _lowerCAmelCase						:       Tuple  			=     self.dropout(a__  )
       _lowerCAmelCase						:       Dict  			=     self.classifier(a__  )

       return logits, pooled_output







@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       List[str]  			=     config.num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     config.num_hidden_layers

       _lowerCAmelCase						:       str  			=     DeeBertModel(a__  )
       _lowerCAmelCase						:       Tuple  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       List[Any]  			=     nn.Linear(config.hidden_size				,     self.config.num_labels  )

       self.init_weights()






     @add_start_docstrings_to_model_forward(a__  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=-1				,     a__=False				,     ):
       _lowerCAmelCase						:       Dict  			=     self.num_layers
       try:
         _lowerCAmelCase						:       str  			=     self.bert(
             a__				,     attention_mask=a__				,     token_type_ids=a__				,     position_ids=a__				,     head_mask=a__				,     inputs_embeds=a__				,     )
         # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

         _lowerCAmelCase						:       Any  			=     outputs[1]

         _lowerCAmelCase						:       Optional[int]  			=     self.dropout(a__  )
         _lowerCAmelCase						:       List[str]  			=     self.classifier(a__  )
         _lowerCAmelCase						:       Union[str, Any]  			=     (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
         _lowerCAmelCase						:       Tuple  			=     e.message
         _lowerCAmelCase						:       int  			=     e.exit_layer
         _lowerCAmelCase						:       Union[str, Any]  			=     outputs[0]

       if not self.training:
         _lowerCAmelCase						:       Tuple  			=     entropy(a__  )
         _lowerCAmelCase						:       Optional[int]  			=     []
         _lowerCAmelCase						:       Optional[Any]  			=     []
       if labels is not None:
         if self.num_labels == 1:
           #  We are doing regression
           _lowerCAmelCase						:       Tuple  			=     MSELoss()
           _lowerCAmelCase						:       int  			=     loss_fct(logits.view(-1  )				,     labels.view(-1  )  )
         else:
           _lowerCAmelCase						:       Any  			=     CrossEntropyLoss()
           _lowerCAmelCase						:       Optional[int]  			=     loss_fct(logits.view(-1				,     self.num_labels  )				,     labels.view(-1  )  )

         # work with highway exits
         _lowerCAmelCase						:       Optional[Any]  			=     []
         for highway_exit in outputs[-1]:
           _lowerCAmelCase						:       Dict  			=     highway_exit[0]
           if not self.training:
             highway_logits_all.append(a__  )
             highway_entropy.append(highway_exit[2]  )
           if self.num_labels == 1:
             #  We are doing regression
             _lowerCAmelCase						:       List[Any]  			=     MSELoss()
             _lowerCAmelCase						:       int  			=     loss_fct(highway_logits.view(-1  )				,     labels.view(-1  )  )
           else:
             _lowerCAmelCase						:       Optional[int]  			=     CrossEntropyLoss()
             _lowerCAmelCase						:       List[Any]  			=     loss_fct(highway_logits.view(-1				,     self.num_labels  )				,     labels.view(-1  )  )
           highway_losses.append(a__  )

         if train_highway:
           _lowerCAmelCase						:       List[Any]  			=     (sum(highway_losses[:-1]  ),) + outputs
           # exclude the final highway, of course
         else:
           _lowerCAmelCase						:       Any  			=     (loss,) + outputs
       if not self.training:
         _lowerCAmelCase						:       Dict  			=     outputs + ((original_entropy, highway_entropy), exit_layer)
         if output_layer >= 0:
           _lowerCAmelCase						:       Dict  			=     (
               (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
           )  # use the highway of the last layer

       return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




