




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__							:	Optional[int]											=		logging.get_logger(__name__)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		snake_case_						       =	[144, 192, 240]
		snake_case_						       =	[16, 32, 64, 96, 128, 160, 640]
	elif "mobilevit_xs" in mobilevit_name:
		snake_case_						       =	[96, 120, 144]
		snake_case_						       =	[16, 32, 48, 64, 80, 96, 384]
	elif "mobilevit_xxs" in mobilevit_name:
		snake_case_						       =	[64, 80, 96]
		snake_case_						       =	[16, 16, 24, 48, 64, 80, 320]
		snake_case_						       =	0.05
		snake_case_						       =	2.0

	if mobilevit_name.startswith("deeplabv3_"				):
		snake_case_						       =	512
		snake_case_						       =	16
		snake_case_						       =	21
		snake_case_						       =	"pascal-voc-id2label.json"
	else:
		snake_case_						       =	1000
		snake_case_						       =	"imagenet-1k-id2label.json"

	snake_case_						       =	"huggingface/label-files"
	snake_case_						       =	json.load(open(hf_hub_download(_A	,      _A	,      repo_type="dataset"				)	,      "r"				)				)
	snake_case_						       =	{int(_A				): v for k, v in idalabel.items()}
	snake_case_						       =	idalabel
	snake_case_						       =	{v: k for k, v in idalabel.items()}

	return config


def 		lowerCamelCase__       (						_A	,      _A=False				):







	'''simple docstring'''





	for i in range(1	,      6				):
		if f"layer_{i}." in name:
			snake_case_						       =	name.replace(f"layer_{i}."	,      f"encoder.layer.{i - 1}."				)

	if "conv_1." in name:
		snake_case_						       =	name.replace("conv_1."	,      "conv_stem."				)
	if ".block." in name:
		snake_case_						       =	name.replace(".block."	,      "."				)

	if "exp_1x1" in name:
		snake_case_						       =	name.replace("exp_1x1"	,      "expand_1x1"				)
	if "red_1x1" in name:
		snake_case_						       =	name.replace("red_1x1"	,      "reduce_1x1"				)
	if ".local_rep.conv_3x3." in name:
		snake_case_						       =	name.replace(".local_rep.conv_3x3."	,      ".conv_kxk."				)
	if ".local_rep.conv_1x1." in name:
		snake_case_						       =	name.replace(".local_rep.conv_1x1."	,      ".conv_1x1."				)
	if ".norm." in name:
		snake_case_						       =	name.replace(".norm."	,      ".normalization."				)
	if ".conv." in name:
		snake_case_						       =	name.replace(".conv."	,      ".convolution."				)
	if ".conv_proj." in name:
		snake_case_						       =	name.replace(".conv_proj."	,      ".conv_projection."				)

	for i in range(0	,      2				):
		for j in range(0	,      4				):
			if f".{i}.{j}." in name:
				snake_case_						       =	name.replace(f".{i}.{j}."	,      f".{i}.layer.{j}."				)

	for i in range(2	,      6				):
		for j in range(0	,      4				):
			if f".{i}.{j}." in name:
				snake_case_						       =	name.replace(f".{i}.{j}."	,      f".{i}."				)
				if "expand_1x1" in name:
					snake_case_						       =	name.replace("expand_1x1"	,      "downsampling_layer.expand_1x1"				)
				if "conv_3x3" in name:
					snake_case_						       =	name.replace("conv_3x3"	,      "downsampling_layer.conv_3x3"				)
				if "reduce_1x1" in name:
					snake_case_						       =	name.replace("reduce_1x1"	,      "downsampling_layer.reduce_1x1"				)

	for i in range(2	,      5				):
		if f".global_rep.{i}.weight" in name:
			snake_case_						       =	name.replace(f".global_rep.{i}.weight"	,      ".layernorm.weight"				)
		if f".global_rep.{i}.bias" in name:
			snake_case_						       =	name.replace(f".global_rep.{i}.bias"	,      ".layernorm.bias"				)

	if ".global_rep." in name:
		snake_case_						       =	name.replace(".global_rep."	,      ".transformer."				)
	if ".pre_norm_mha.0." in name:
		snake_case_						       =	name.replace(".pre_norm_mha.0."	,      ".layernorm_before."				)
	if ".pre_norm_mha.1.out_proj." in name:
		snake_case_						       =	name.replace(".pre_norm_mha.1.out_proj."	,      ".attention.output.dense."				)
	if ".pre_norm_ffn.0." in name:
		snake_case_						       =	name.replace(".pre_norm_ffn.0."	,      ".layernorm_after."				)
	if ".pre_norm_ffn.1." in name:
		snake_case_						       =	name.replace(".pre_norm_ffn.1."	,      ".intermediate.dense."				)
	if ".pre_norm_ffn.4." in name:
		snake_case_						       =	name.replace(".pre_norm_ffn.4."	,      ".output.dense."				)
	if ".transformer." in name:
		snake_case_						       =	name.replace(".transformer."	,      ".transformer.layer."				)

	if ".aspp_layer." in name:
		snake_case_						       =	name.replace(".aspp_layer."	,      "."				)
	if ".aspp_pool." in name:
		snake_case_						       =	name.replace(".aspp_pool."	,      "."				)
	if "seg_head." in name:
		snake_case_						       =	name.replace("seg_head."	,      "segmentation_head."				)
	if "segmentation_head.classifier.classifier." in name:
		snake_case_						       =	name.replace("segmentation_head.classifier.classifier."	,      "segmentation_head.classifier."				)

	if "classifier.fc." in name:
		snake_case_						       =	name.replace("classifier.fc."	,      "classifier."				)
	elif (not base_model) and ("segmentation_head." not in name):
		snake_case_						       =	"mobilevit." + name

	return name


def 		lowerCamelCase__       (						_A	,      _A	,      _A=False				):







	'''simple docstring'''





	if base_model:
		snake_case_						       =	""
	else:
		snake_case_						       =	"mobilevit."

	for key in orig_state_dict.copy().keys():
		snake_case_						       =	orig_state_dict.pop(_A				)

		if key[:8] == "encoder.":
			snake_case_						       =	key[8:]

		if "qkv" in key:
			snake_case_						       =	key.split("."				)
			snake_case_						       =	int(key_split[0][6:]				) - 1
			snake_case_						       =	int(key_split[3]				)
			snake_case_						       =	model.get_submodule(f"{model_prefix}encoder.layer.{layer_num}"				)
			snake_case_						       =	layer.transformer.layer[transformer_num].attention.attention.all_head_size
			snake_case_						       =	(
			    f"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
			)
			if "weight" in key:
				snake_case_						       =	val[:dim, :]
				snake_case_						       =	val[dim : dim * 2, :]
				snake_case_						       =	val[-dim:, :]
			else:
				snake_case_						       =	val[:dim]
				snake_case_						       =	val[dim : dim * 2]
				snake_case_						       =	val[-dim:]
		else:
			snake_case_						       =	val

	return orig_state_dict


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_						       =	Image.open(requests.get(_A	,      stream=_A				).raw				)
	return im


@torch.no_grad()
def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A=False				):







	'''simple docstring'''





	snake_case_						       =	get_mobilevit_config(_A				)

	# load original state_dict
	snake_case_						       =	torch.load(_A	,      map_location="cpu"				)

	# load 🤗 model
	if mobilevit_name.startswith("deeplabv3_"				):
		snake_case_						       =	MobileViTForSemanticSegmentation(_A				).eval()
	else:
		snake_case_						       =	MobileViTForImageClassification(_A				).eval()

	snake_case_						       =	convert_state_dict(_A	,      _A				)
	model.load_state_dict(_A				)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	snake_case_						       =	MobileViTImageProcessor(crop_size=config.image_size	,      size=config.image_size + 32				)
	snake_case_						       =	image_processor(images=prepare_img()	,      return_tensors="pt"				)
	snake_case_						       =	model(**_A				)
	snake_case_						       =	outputs.logits

	if mobilevit_name.startswith("deeplabv3_"				):
		assert logits.shape == (1, 21, 32, 32)

		if mobilevit_name == "deeplabv3_mobilevit_s":
			snake_case_						       =	torch.tensor(
			    [
			        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
			        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
			        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
			    ]				)
		elif mobilevit_name == "deeplabv3_mobilevit_xs":
			snake_case_						       =	torch.tensor(
			    [
			        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
			        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
			        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
			    ]				)
		elif mobilevit_name == "deeplabv3_mobilevit_xxs":
			snake_case_						       =	torch.tensor(
			    [
			        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
			        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
			        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
			    ]				)
		else:
			raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}"				)

		assert torch.allclose(logits[0, :3, :3, :3]	,      _A	,      atol=1E-4				)
	else:
		assert logits.shape == (1, 1000)

		if mobilevit_name == "mobilevit_s":
			snake_case_						       =	torch.tensor([-0.98_66, 0.23_92, -1.12_41]				)
		elif mobilevit_name == "mobilevit_xs":
			snake_case_						       =	torch.tensor([-2.47_61, -0.93_99, -1.95_87]				)
		elif mobilevit_name == "mobilevit_xxs":
			snake_case_						       =	torch.tensor([-1.93_64, -1.23_27, -0.46_53]				)
		else:
			raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}"				)

		assert torch.allclose(logits[0, :3]	,      _A	,      atol=1E-4				)

	Path(_A				).mkdir(exist_ok=_A				)
	print(f"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"				)
	model.save_pretrained(_A				)
	print(f"Saving image processor to {pytorch_dump_folder_path}"				)
	image_processor.save_pretrained(_A				)

	if push_to_hub:
		snake_case_						       =	{
		    "mobilevit_s": "mobilevit-small",
		    "mobilevit_xs": "mobilevit-x-small",
		    "mobilevit_xxs": "mobilevit-xx-small",
		    "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
		    "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
		    "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
		}

		print("Pushing to the hub..."				)
		snake_case_						       =	model_mapping[mobilevit_name]
		image_processor.push_to_hub(_A	,      organization="apple"				)
		model.push_to_hub(_A	,      organization="apple"				)


if __name__ == "__main__":
		lowercase__							:	Any											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--mobilevit_name",
		    default="mobilevit_s",
		    type=str,
		    help=(
		        "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
		        " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
		    ),
		)
		parser.add_argument(
		    "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
		)

		lowercase__							:	List[str]											=		parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)






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 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	torch.exp(_A				)
	snake_case_						       =	torch.sum(_A	,      dim=1				)  # sum of exp(x_i)
	snake_case_						       =	torch.sum(x * exp_x	,      dim=1				)  # sum of x_i * exp(x_i)
	return torch.log(_A				) - B / A
class 						UpperCAmelCase  (							nn.Module     ):







						'''simple docstring'''





						def __init__(		self				:	Optional[int]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							super().__init__()
							snake_case_						       =	config.output_attentions
							snake_case_						       =	config.output_hidden_states
							snake_case_						       =	nn.ModuleList([BertLayer(__lowercase		) for _ in range(config.num_hidden_layers		)]		)
							snake_case_						       =	nn.ModuleList([BertHighway(__lowercase		) for _ in range(config.num_hidden_layers		)]		)

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






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							if (type(__lowercase		) is float) or (type(__lowercase		) is int):
								for i in range(len(self.early_exit_entropy		)		):
									snake_case_						       =	x
							else:
								snake_case_						       =	x






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	pooler.state_dict()
							for highway in self.highway:
								for name, param in highway.pooler.state_dict().items():
									param.copy_(loaded_model[name]		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[str]			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Optional[int]=None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	List[str]=None			,      ):






							"""simple docstring"""






							snake_case_						       =	()
							snake_case_						       =	()
							snake_case_						       =	()
							for i, layer_module in enumerate(self.layer		):
								if self.output_hidden_states:
									snake_case_						       =	all_hidden_states + (hidden_states,)

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

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

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

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

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

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

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

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

							snake_case_						       =	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). '''			,  UpperCAmelCase__			,  )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							super().__init__(__lowercase		)
							snake_case_						       =	config

							snake_case_						       =	BertEmbeddings(__lowercase		)
							snake_case_						       =	DeeBertEncoder(__lowercase		)
							snake_case_						       =	BertPooler(__lowercase		)

							self.init_weights()






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							self.encoder.init_highway_pooler(self.pooler		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return self.embeddings.word_embeddings






						def 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	value






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






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






						@add_start_docstrings_to_model_forward(__lowercase		)
						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	List[str]=None			,      __lowercase				:	int=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	Dict=None			,      __lowercase				:	str=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	List[Any]=None			,      __lowercase				:	List[str]=None			,      ):






							"""simple docstring"""






							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:
								snake_case_						       =	input_ids.size()
							elif inputs_embeds is not None:
								snake_case_						       =	inputs_embeds.size()[:-1]
							else:
								raise ValueError("You have to specify either input_ids or inputs_embeds"		)

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

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

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

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

							snake_case_						       =	encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
							snake_case_						       =	(1.0 - encoder_extended_attention_mask) * -1_0000.0

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

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

							snake_case_						       =	(
							    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 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	Tuple			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	message
							snake_case_						       =	exit_layer  # start from 1!







class 						UpperCAmelCase  (							nn.Module     ):







						'''simple docstring'''





						def __init__(		self				:	int			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							super().__init__()
							snake_case_						       =	BertPooler(__lowercase		)
							snake_case_						       =	nn.Dropout(config.hidden_dropout_prob		)
							snake_case_						       =	nn.Linear(config.hidden_size			,      config.num_labels		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	encoder_outputs[0]
							snake_case_						       =	self.pooler(__lowercase		)
							# "return" pooler_output

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

							# Dropout and classification
							snake_case_						       =	bmodel_output[1]

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

							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. '''			,  UpperCAmelCase__			,  )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							super().__init__(__lowercase		)
							snake_case_						       =	config.num_labels
							snake_case_						       =	config.num_hidden_layers

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

							self.init_weights()






						@add_start_docstrings_to_model_forward(__lowercase		)
						def 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Dict=None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	Optional[int]=None			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	List[str]=None			,      __lowercase				:	int=-1			,      __lowercase				:	Tuple=False			,      ):






							"""simple docstring"""






							snake_case_						       =	self.num_layers
							try:
								snake_case_						       =	self.bert(
								    __lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      position_ids=__lowercase			,      head_mask=__lowercase			,      inputs_embeds=__lowercase			,      )
								# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

								snake_case_						       =	outputs[1]

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

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

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

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