
"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  DPTConfig(embedding_type="""hybrid"""						)

						if "large" in checkpoint_url:
												__lowerCAmelCase     =  1024
												__lowerCAmelCase     =  4096
												__lowerCAmelCase     =  24
												__lowerCAmelCase     =  16
												__lowerCAmelCase     =  [5, 11, 17, 23]
												__lowerCAmelCase     =  [256, 512, 1024, 1024]
												__lowerCAmelCase     =  (1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												__lowerCAmelCase     =  768
												__lowerCAmelCase     =  [1, 1, 1, 0.5]
												__lowerCAmelCase     =  [256, 512, 768, 768]
												__lowerCAmelCase     =  150
												__lowerCAmelCase     =  16
												__lowerCAmelCase     =  (1, 384, 384)
												__lowerCAmelCase     =  False
												__lowerCAmelCase     =  """project"""

						if "ade" in checkpoint_url:
												__lowerCAmelCase     =  True
												__lowerCAmelCase     =  768
												__lowerCAmelCase     =  [1, 1, 1, 0.5]
												__lowerCAmelCase     =  150
												__lowerCAmelCase     =  16
												__lowerCAmelCase     =  """huggingface/label-files"""
												__lowerCAmelCase     =  """ade20k-id2label.json"""
												__lowerCAmelCase     =  json.load(open(cached_download(hf_hub_url(_lowerCAmelCase ,    _lowerCAmelCase ,    repo_type="""dataset"""						)						) ,    """r"""						)						)
												__lowerCAmelCase     =  {int(_lowerCAmelCase						): v for k, v in idalabel.items()}
												__lowerCAmelCase     =  idalabel
												__lowerCAmelCase     =  {v: k for k, v in idalabel.items()}
												__lowerCAmelCase     =  [1, 150, 480, 480]

						return config, expected_shape

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(_lowerCAmelCase ,    _lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase						):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												__lowerCAmelCase     =  name.replace("""pretrained.model""" ,    """dpt.encoder"""						)
						if "pretrained.model" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.model""" ,    """dpt.embeddings"""						)
						if "patch_embed" in name:
												__lowerCAmelCase     =  name.replace("""patch_embed""" ,    """"""						)
						if "pos_embed" in name:
												__lowerCAmelCase     =  name.replace("""pos_embed""" ,    """position_embeddings"""						)
						if "attn.proj" in name:
												__lowerCAmelCase     =  name.replace("""attn.proj""" ,    """attention.output.dense"""						)
						if "proj" in name and "project" not in name:
												__lowerCAmelCase     =  name.replace("""proj""" ,    """projection"""						)
						if "blocks" in name:
												__lowerCAmelCase     =  name.replace("""blocks""" ,    """layer"""						)
						if "mlp.fc1" in name:
												__lowerCAmelCase     =  name.replace("""mlp.fc1""" ,    """intermediate.dense"""						)
						if "mlp.fc2" in name:
												__lowerCAmelCase     =  name.replace("""mlp.fc2""" ,    """output.dense"""						)
						if "norm1" in name and "backbone" not in name:
												__lowerCAmelCase     =  name.replace("""norm1""" ,    """layernorm_before"""						)
						if "norm2" in name and "backbone" not in name:
												__lowerCAmelCase     =  name.replace("""norm2""" ,    """layernorm_after"""						)
						if "scratch.output_conv" in name:
												__lowerCAmelCase     =  name.replace("""scratch.output_conv""" ,    """head"""						)
						if "scratch" in name:
												__lowerCAmelCase     =  name.replace("""scratch""" ,    """neck"""						)
						if "layer1_rn" in name:
												__lowerCAmelCase     =  name.replace("""layer1_rn""" ,    """convs.0"""						)
						if "layer2_rn" in name:
												__lowerCAmelCase     =  name.replace("""layer2_rn""" ,    """convs.1"""						)
						if "layer3_rn" in name:
												__lowerCAmelCase     =  name.replace("""layer3_rn""" ,    """convs.2"""						)
						if "layer4_rn" in name:
												__lowerCAmelCase     =  name.replace("""layer4_rn""" ,    """convs.3"""						)
						if "refinenet" in name:
												__lowerCAmelCase     =  int(name[len("""neck.refinenet"""						) : len("""neck.refinenet"""						) + 1]						)
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												__lowerCAmelCase     =  name.replace(f"""refinenet{layer_idx}""" ,    f"""fusion_stage.layers.{abs(layer_idx-4						)}"""						)
						if "out_conv" in name:
												__lowerCAmelCase     =  name.replace("""out_conv""" ,    """projection"""						)
						if "resConfUnit1" in name:
												__lowerCAmelCase     =  name.replace("""resConfUnit1""" ,    """residual_layer1"""						)
						if "resConfUnit2" in name:
												__lowerCAmelCase     =  name.replace("""resConfUnit2""" ,    """residual_layer2"""						)
						if "conv1" in name:
												__lowerCAmelCase     =  name.replace("""conv1""" ,    """convolution1"""						)
						if "conv2" in name:
												__lowerCAmelCase     =  name.replace("""conv2""" ,    """convolution2"""						)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess1.0.project.0""" ,    """neck.reassemble_stage.readout_projects.0.0"""						)
						if "pretrained.act_postprocess2.0.project.0" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess2.0.project.0""" ,    """neck.reassemble_stage.readout_projects.1.0"""						)
						if "pretrained.act_postprocess3.0.project.0" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess3.0.project.0""" ,    """neck.reassemble_stage.readout_projects.2.0"""						)
						if "pretrained.act_postprocess4.0.project.0" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess4.0.project.0""" ,    """neck.reassemble_stage.readout_projects.3.0"""						)

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess1.3""" ,    """neck.reassemble_stage.layers.0.projection"""						)
						if "pretrained.act_postprocess1.4" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess1.4""" ,    """neck.reassemble_stage.layers.0.resize"""						)
						if "pretrained.act_postprocess2.3" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess2.3""" ,    """neck.reassemble_stage.layers.1.projection"""						)
						if "pretrained.act_postprocess2.4" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess2.4""" ,    """neck.reassemble_stage.layers.1.resize"""						)
						if "pretrained.act_postprocess3.3" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess3.3""" ,    """neck.reassemble_stage.layers.2.projection"""						)
						if "pretrained.act_postprocess4.3" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess4.3""" ,    """neck.reassemble_stage.layers.3.projection"""						)
						if "pretrained.act_postprocess4.4" in name:
												__lowerCAmelCase     =  name.replace("""pretrained.act_postprocess4.4""" ,    """neck.reassemble_stage.layers.3.resize"""						)
						if "pretrained" in name:
												__lowerCAmelCase     =  name.replace("""pretrained""" ,    """dpt"""						)
						if "bn" in name:
												__lowerCAmelCase     =  name.replace("""bn""" ,    """batch_norm"""						)
						if "head" in name:
												__lowerCAmelCase     =  name.replace("""head""" ,    """head.head"""						)
						if "encoder.norm" in name:
												__lowerCAmelCase     =  name.replace("""encoder.norm""" ,    """layernorm"""						)
						if "auxlayer" in name:
												__lowerCAmelCase     =  name.replace("""auxlayer""" ,    """auxiliary_head.head"""						)
						if "backbone" in name:
												__lowerCAmelCase     =  name.replace("""backbone""" ,    """backbone.bit.encoder"""						)

						if ".." in name:
												__lowerCAmelCase     =  name.replace("""..""" ,    """."""						)

						if "stem.conv" in name:
												__lowerCAmelCase     =  name.replace("""stem.conv""" ,    """bit.embedder.convolution"""						)
						if "blocks" in name:
												__lowerCAmelCase     =  name.replace("""blocks""" ,    """layers"""						)
						if "convolution" in name and "backbone" in name:
												__lowerCAmelCase     =  name.replace("""convolution""" ,    """conv"""						)
						if "layer" in name and "backbone" in name:
												__lowerCAmelCase     =  name.replace("""layer""" ,    """layers"""						)
						if "backbone.bit.encoder.bit" in name:
												__lowerCAmelCase     =  name.replace("""backbone.bit.encoder.bit""" ,    """backbone.bit"""						)
						if "embedder.conv" in name:
												__lowerCAmelCase     =  name.replace("""embedder.conv""" ,    """embedder.convolution"""						)
						if "backbone.bit.encoder.stem.norm" in name:
												__lowerCAmelCase     =  name.replace("""backbone.bit.encoder.stem.norm""" ,    """backbone.bit.embedder.norm"""						)
						return name

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						for i in range(config.num_hidden_layers						):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												__lowerCAmelCase     =  state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""						)
												__lowerCAmelCase     =  state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""						)
												# next, add query, keys and values (in that order) to the state dict
												__lowerCAmelCase     =  in_proj_weight[: config.hidden_size, :]
												__lowerCAmelCase     =  in_proj_bias[: config.hidden_size]
												__lowerCAmelCase     =  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__lowerCAmelCase     =  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												__lowerCAmelCase     =  in_proj_weight[
												    -config.hidden_size :, :
												]
												__lowerCAmelCase     =  in_proj_bias[-config.hidden_size :]

def  lowercase     ():
						__lowerCAmelCase     =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__lowerCAmelCase     =  Image.open(requests.get(_lowerCAmelCase ,    stream=_lowerCAmelCase						).raw						)
						return im

@torch.no_grad()
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase   ,     __lowerCAmelCase     =  get_dpt_config(_lowerCAmelCase						)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						__lowerCAmelCase     =  torch.load(_lowerCAmelCase ,    map_location="""cpu"""						)
						# remove certain keys
						remove_ignore_keys_(_lowerCAmelCase						)
						# rename keys
						for key in state_dict.copy().keys():
												__lowerCAmelCase     =  state_dict.pop(_lowerCAmelCase						)
												__lowerCAmelCase     =  val
						# read in qkv matrices
						read_in_q_k_v(_lowerCAmelCase ,    _lowerCAmelCase						)

						# load HuggingFace model
						__lowerCAmelCase     =  DPTForSemanticSegmentation(_lowerCAmelCase						) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase						)
						model.load_state_dict(_lowerCAmelCase						)
						model.eval()

						# Check outputs on an image
						__lowerCAmelCase     =  480 if """ade""" in checkpoint_url else 384
						__lowerCAmelCase     =  DPTImageProcessor(size=_lowerCAmelCase						)

						__lowerCAmelCase     =  prepare_img()
						__lowerCAmelCase     =  image_processor(_lowerCAmelCase ,    return_tensors="""pt"""						)

						# forward pass
						__lowerCAmelCase     =  model(**_lowerCAmelCase						).logits if """ade""" in checkpoint_url else model(**_lowerCAmelCase						).predicted_depth

						if show_prediction:
												__lowerCAmelCase     =  (
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1						) ,    size=(image.size[1], image.size[0]) ,    mode="""bicubic""" ,    align_corners=_lowerCAmelCase ,    )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255						).show()

						if pytorch_dump_folder_path is not None:
												Path(_lowerCAmelCase						).mkdir(exist_ok=_lowerCAmelCase						)
												print(f"""Saving model to {pytorch_dump_folder_path}"""						)
												model.save_pretrained(_lowerCAmelCase						)
												print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
												image_processor.save_pretrained(_lowerCAmelCase						)

						if push_to_hub:
												model.push_to_hub("""ybelkada/dpt-hybrid-midas"""						)
												image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
			    type=str,
			    help='''URL of the original DPT checkpoint you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			)
			parser.add_argument(
			    '''--model_name''',
			    default='''dpt-large''',
			    type=str,
			    help='''Name of the model, in case you\'re pushing to the hub.''',
			)
			parser.add_argument(
			    '''--show_prediction''',
			    action='''store_true''',
			)

			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_dpt_checkpoint(
			    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
			)


"""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)
