
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class       lowerCAmelCase_		(				A__				,				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''nat'''

							_snake_case						      =		{
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__( self    ,				snake_case_=4    ,				snake_case_=3    ,				snake_case_=64    ,				snake_case_=[3, 4, 6, 5]    ,				snake_case_=[2, 4, 8, 16]    ,				snake_case_=7    ,				snake_case_=3.0    ,				snake_case_=True    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=0.1    ,				snake_case_="gelu"    ,				snake_case_=0.02    ,				snake_case_=1e-5    ,				snake_case_=0.0    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_    ,				)       ->  List[str]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  embed_dim
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  len(snake_case_				)
													__lowerCAmelCase     =  num_heads
													__lowerCAmelCase     =  kernel_size
													__lowerCAmelCase     =  mlp_ratio
													__lowerCAmelCase     =  qkv_bias
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  attention_probs_dropout_prob
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  initializer_range
													# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													__lowerCAmelCase     =  int(embed_dim * 2 ** (len(snake_case_				) - 1)				)
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  ["""stem"""] + [f"""stage{idx}""" for idx in range(1    ,				len(snake_case_				) + 1				)]
													__lowerCAmelCase   ,     __lowerCAmelCase     =  get_aligned_output_features_output_indices(
													    out_features=snake_case_    ,				out_indices=snake_case_    ,				stage_names=self.stage_names				)


"""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  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  torch.exp(_lowerCAmelCase						)
						__lowerCAmelCase     =  torch.sum(_lowerCAmelCase ,    dim=1						)  # sum of exp(x_i)
						__lowerCAmelCase     =  torch.sum(x * exp_x ,    dim=1						)  # sum of x_i * exp(x_i)
						return torch.log(_lowerCAmelCase						) - B / A
class       lowerCAmelCase_		(				nn.Module ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Optional[Any]:
													super().__init__()
													__lowerCAmelCase     =  config.output_attentions
													__lowerCAmelCase     =  config.output_hidden_states
													__lowerCAmelCase     =  nn.ModuleList([BertLayer(snake_case_				) for _ in range(config.num_hidden_layers				)]				)
													__lowerCAmelCase     =  nn.ModuleList([BertHighway(snake_case_				) for _ in range(config.num_hidden_layers				)]				)

													__lowerCAmelCase     =  [-1 for _ in range(config.num_hidden_layers				)]




							def    A__						( self    ,				snake_case_				)       ->  List[Any]:
													if (type(snake_case_				) is float) or (type(snake_case_				) is int):
																			for i in range(len(self.early_exit_entropy				)				):
																									__lowerCAmelCase     =  x
													else:
																			__lowerCAmelCase     =  x




							def    A__						( self    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  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    ,				snake_case_    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				)       ->  List[str]:
													__lowerCAmelCase     =  ()
													__lowerCAmelCase     =  ()
													__lowerCAmelCase     =  ()
													for i, layer_module in enumerate(self.layer				):
																			if self.output_hidden_states:
																									__lowerCAmelCase     =  all_hidden_states + (hidden_states,)

																			__lowerCAmelCase     =  layer_module(
																			    snake_case_    ,				snake_case_    ,				head_mask[i]    ,				snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  layer_outputs[0]

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

																			__lowerCAmelCase     =  (hidden_states,)
																			if self.output_hidden_states:
																									__lowerCAmelCase     =  current_outputs + (all_hidden_states,)
																			if self.output_attentions:
																									__lowerCAmelCase     =  current_outputs + (all_attentions,)

																			__lowerCAmelCase     =  self.highway[i](snake_case_				)
																			# logits, pooled_output

																			if not self.training:
																									__lowerCAmelCase     =  highway_exit[0]
																									__lowerCAmelCase     =  entropy(snake_case_				)
																									__lowerCAmelCase     =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																									__lowerCAmelCase     =  all_highway_exits + (highway_exit,)

																									if highway_entropy < self.early_exit_entropy[i]:
																															__lowerCAmelCase     =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																															raise HighwayException(snake_case_    ,				i + 1				)
																			else:
																									__lowerCAmelCase     =  all_highway_exits + (highway_exit,)

        # Add last layer
													if self.output_hidden_states:
																			__lowerCAmelCase     =  all_hidden_states + (hidden_states,)

													__lowerCAmelCase     =  (hidden_states,)
													if self.output_hidden_states:
																			__lowerCAmelCase     =  outputs + (all_hidden_states,)
													if self.output_attentions:
																			__lowerCAmelCase     =  outputs + (all_attentions,)

													__lowerCAmelCase     =  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). '''				,				A__				,				)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Tuple:
													super().__init__(snake_case_				)
													__lowerCAmelCase     =  config

													__lowerCAmelCase     =  BertEmbeddings(snake_case_				)
													__lowerCAmelCase     =  DeeBertEncoder(snake_case_				)
													__lowerCAmelCase     =  BertPooler(snake_case_				)

													self.init_weights()




							def    A__						( self				)       ->  Dict:
													self.encoder.init_highway_pooler(self.pooler				)




							def    A__						( self				)       ->  List[Any]:
													return self.embeddings.word_embeddings




							def    A__						( self    ,				snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  value




							def    A__						( self    ,				snake_case_				)       ->  List[str]:
													for layer, heads in heads_to_prune.items():
																			self.encoder.layer[layer].attention.prune_heads(snake_case_				)




							@add_start_docstrings_to_model_forward(snake_case_				)
							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				)       ->  Optional[Any]:
													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     =  input_ids.size()
													elif inputs_embeds is not None:
																			__lowerCAmelCase     =  inputs_embeds.size()[:-1]
													else:
																			raise ValueError("""You have to specify either input_ids or inputs_embeds"""				)

													__lowerCAmelCase     =  input_ids.device if input_ids is not None else inputs_embeds.device

													if attention_mask is None:
																			__lowerCAmelCase     =  torch.ones(snake_case_    ,				device=snake_case_				)
													if encoder_attention_mask is None:
																			__lowerCAmelCase     =  torch.ones(snake_case_    ,				device=snake_case_				)
													if token_type_ids is None:
																			__lowerCAmelCase     =  torch.zeros(snake_case_    ,				dtype=torch.long    ,				device=snake_case_				)

													# 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     =  self.get_extended_attention_mask(snake_case_    ,				snake_case_    ,				snake_case_				)

													# 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     =  encoder_attention_mask[:, None, :, :]
													if encoder_attention_mask.dim() == 2:
																			__lowerCAmelCase     =  encoder_attention_mask[:, None, None, :]

													__lowerCAmelCase     =  encoder_extended_attention_mask.to(
													    dtype=next(self.parameters()				).dtype				)  # fp16 compatibility
													__lowerCAmelCase     =  (1.0 - encoder_extended_attention_mask) * -10_000.0

													# 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     =  self.get_head_mask(snake_case_    ,				self.config.num_hidden_layers				)

													__lowerCAmelCase     =  self.embeddings(
													    input_ids=snake_case_    ,				position_ids=snake_case_    ,				token_type_ids=snake_case_    ,				inputs_embeds=snake_case_				)
													__lowerCAmelCase     =  self.encoder(
													    snake_case_    ,				attention_mask=snake_case_    ,				head_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				encoder_attention_mask=snake_case_    ,				)
													__lowerCAmelCase     =  encoder_outputs[0]
													__lowerCAmelCase     =  self.pooler(snake_case_				)

													__lowerCAmelCase     =  (
													    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       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_				)       ->  Optional[int]:
													__lowerCAmelCase     =  message
													__lowerCAmelCase     =  exit_layer  # start from 1!
class       lowerCAmelCase_		(				nn.Module ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  List[Any]:
													super().__init__()
													__lowerCAmelCase     =  BertPooler(snake_case_				)
													__lowerCAmelCase     =  nn.Dropout(config.hidden_dropout_prob				)
													__lowerCAmelCase     =  nn.Linear(config.hidden_size    ,				config.num_labels				)




							def    A__						( self    ,				snake_case_				)       ->  Optional[int]:
													# Pooler
													__lowerCAmelCase     =  encoder_outputs[0]
													__lowerCAmelCase     =  self.pooler(snake_case_				)
													# "return" pooler_output

													# BertModel
													__lowerCAmelCase     =  (pooler_input, pooler_output) + encoder_outputs[1:]
													# "return" bmodel_output

													# Dropout and classification
													__lowerCAmelCase     =  bmodel_output[1]

													__lowerCAmelCase     =  self.dropout(snake_case_				)
													__lowerCAmelCase     =  self.classifier(snake_case_				)

													return logits, pooled_output
@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''				,				A__				,				)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Optional[Any]:
													super().__init__(snake_case_				)
													__lowerCAmelCase     =  config.num_labels
													__lowerCAmelCase     =  config.num_hidden_layers

													__lowerCAmelCase     =  DeeBertModel(snake_case_				)
													__lowerCAmelCase     =  nn.Dropout(config.hidden_dropout_prob				)
													__lowerCAmelCase     =  nn.Linear(config.hidden_size    ,				self.config.num_labels				)

													self.init_weights()




							@add_start_docstrings_to_model_forward(snake_case_				)
							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=-1    ,				snake_case_=False    ,				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.num_layers
													try:
																			__lowerCAmelCase     =  self.bert(
																			    snake_case_    ,				attention_mask=snake_case_    ,				token_type_ids=snake_case_    ,				position_ids=snake_case_    ,				head_mask=snake_case_    ,				inputs_embeds=snake_case_    ,				)
																			# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																			__lowerCAmelCase     =  outputs[1]

																			__lowerCAmelCase     =  self.dropout(snake_case_				)
																			__lowerCAmelCase     =  self.classifier(snake_case_				)
																			__lowerCAmelCase     =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
													except HighwayException as e:
																			__lowerCAmelCase     =  e.message
																			__lowerCAmelCase     =  e.exit_layer
																			__lowerCAmelCase     =  outputs[0]

													if not self.training:
																			__lowerCAmelCase     =  entropy(snake_case_				)
																			__lowerCAmelCase     =  []
																			__lowerCAmelCase     =  []
													if labels is not None:
																			if self.num_labels == 1:
																									#  We are doing regression
																									__lowerCAmelCase     =  MSELoss()
																									__lowerCAmelCase     =  loss_fct(logits.view(-1				)    ,				labels.view(-1				)				)
																			else:
																									__lowerCAmelCase     =  CrossEntropyLoss()
																									__lowerCAmelCase     =  loss_fct(logits.view(-1    ,				self.num_labels				)    ,				labels.view(-1				)				)

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

																			if train_highway:
																									__lowerCAmelCase     =  (sum(highway_losses[:-1]				),) + outputs
																									# exclude the final highway, of course
																			else:
																									__lowerCAmelCase     =  (loss,) + outputs
													if not self.training:
																			__lowerCAmelCase     =  outputs + ((original_entropy, highway_entropy), exit_layer)
																			if output_layer >= 0:
																									__lowerCAmelCase     =  (
																									    (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)
