

'''simple docstring'''





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


A_	= logging.get_logger(__name__)







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=False	)       ->			Tuple:
					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
										    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
										    ' instructions.'	)
										raise

					if not is_sharded:
										lowerCamelCase_  				=      os.path.abspath(__UpperCamelCase	)
										logger.info(f'''Loading PyTorch weights from {pt_path}'''	)

										lowerCamelCase_  				=      torch.load(__UpperCamelCase    ,map_location='cpu'	)
										logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters.'''	)

										lowerCamelCase_  				=      convert_pytorch_state_dict_to_flax(__UpperCamelCase    ,__UpperCamelCase	)
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										lowerCamelCase_  				=      convert_pytorch_sharded_state_dict_to_flax(__UpperCamelCase    ,__UpperCamelCase	)
					return flax_state_dict







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,)       ->			(Tuple[str], np.ndarray):

					def is_key_or_prefix_key_in_dict(__UpperCamelCase	) -> bool:
										return len(set(__UpperCamelCase	) & {key, (model_prefix,) + key}	) > 0

					# layer norm
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('scale',)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__UpperCamelCase	):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('mean',)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__UpperCamelCase	):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('var',)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__UpperCamelCase	):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('embedding',)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__UpperCamelCase	):
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__UpperCamelCase	):
										lowerCamelCase_  				=      pt_tensor.transpose(2    ,3    ,1    ,0	)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__UpperCamelCase	):
										lowerCamelCase_  				=      pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('weight',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					lowerCamelCase_  				=      pt_tuple_key[:-1] + ('bias',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
					lowerCamelCase_  				=      None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										lowerCamelCase_  				=      pt_tuple_key[-2] + '_g'
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										lowerCamelCase_  				=      pt_tuple_key[-2] + '_v'
					if name is not None:
										lowerCamelCase_  				=      pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			Optional[Any]:
					# convert pytorch tensor to numpy
					lowerCamelCase_  				=      {k: v.numpy() for k, v in pt_state_dict.items()}

					lowerCamelCase_  				=      flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										lowerCamelCase_  				=      flax_model.params['params']
					else:
										lowerCamelCase_  				=      flax_model.params
					lowerCamelCase_  				=      flatten_dict(__UpperCamelCase	)

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										lowerCamelCase_  				=      flatten_dict(flax_model.params['batch_stats']	)
										random_flax_state_dict.update(__UpperCamelCase	)

					lowerCamelCase_  				=      {}

					lowerCamelCase_  				=      (model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('.'	)[0] for k in pt_state_dict.keys()}
					)
					lowerCamelCase_  				=      (model_prefix in flax_model_params) and (
					    model_prefix not in {k.split('.'	)[0] for k in pt_state_dict.keys()}
					)

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										lowerCamelCase_  				=      tuple(pt_key.split('.'	)	)

										# remove base model prefix if necessary
										lowerCamelCase_  				=      pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															lowerCamelCase_  				=      pt_tuple_key[1:]

										# Correctly rename weight parameters
										lowerCamelCase_				,lowerCamelCase_  				=      rename_key_and_reshape_tensor(
										    __UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)

										# add model prefix if necessary
										lowerCamelCase_  				=      (model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															lowerCamelCase_  				=      (model_prefix,) + flax_key

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																				    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)

        # add batch stats if the model contains batchnorm layers
										if "batch_stats" in flax_model.params:
															if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																				lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(__UpperCamelCase    ,__UpperCamelCase	)
																				continue

															# also add unexpected weight so that warning is thrown
															lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)

										else:
															# also add unexpected weight so that warning is thrown
															lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)

					return unflatten_dict(__UpperCamelCase	)







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			Dict:
					import torch

					# Load the index
					lowerCamelCase_  				=      {}
					for shard_file in shard_filenames:
										# load using msgpack utils
										lowerCamelCase_  				=      torch.load(__UpperCamelCase	)
										lowerCamelCase_  				=      {k: v.numpy() for k, v in pt_state_dict.items()}

										lowerCamelCase_  				=      flax_model.base_model_prefix

										# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
										if "batch_stats" in flax_model.params:
															lowerCamelCase_  				=      flax_model.params['params']

															lowerCamelCase_  				=      flatten_dict(__UpperCamelCase	)
															random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']	)	)
										else:
															lowerCamelCase_  				=      flax_model.params
															lowerCamelCase_  				=      flatten_dict(__UpperCamelCase	)

										lowerCamelCase_  				=      (model_prefix not in flax_model_params) and (
										    model_prefix in {k.split('.'	)[0] for k in pt_state_dict.keys()}
										)
										lowerCamelCase_  				=      (model_prefix in flax_model_params) and (
										    model_prefix not in {k.split('.'	)[0] for k in pt_state_dict.keys()}
										)
										# Need to change some parameters name to match Flax names
										for pt_key, pt_tensor in pt_state_dict.items():
															lowerCamelCase_  				=      tuple(pt_key.split('.'	)	)

															# remove base model prefix if necessary
															lowerCamelCase_  				=      pt_tuple_key[0] == model_prefix
															if load_model_with_head_into_base_model and has_base_model_prefix:
																				lowerCamelCase_  				=      pt_tuple_key[1:]

															# Correctly rename weight parameters
															lowerCamelCase_				,lowerCamelCase_  				=      rename_key_and_reshape_tensor(
															    __UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)
															# add model prefix if necessary
															lowerCamelCase_  				=      (model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				lowerCamelCase_  				=      (model_prefix,) + flax_key

															if flax_key in random_flax_state_dict:
																				if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																									raise ValueError(
																									    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																									    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)

            # add batch stats if the model contains batchnorm layers
															if "batch_stats" in flax_model.params:
																				if "mean" in flax_key[-1]:
																									lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)
																									continue
																				if "var" in flax_key[-1]:
																									lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(__UpperCamelCase    ,__UpperCamelCase	)
																									continue

																				# also add unexpected weight so that warning is thrown
																				lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)

															else:
																				# also add unexpected weight so that warning is thrown
																				lowerCamelCase_  				=      jnp.asarray(__UpperCamelCase	)
					return unflatten_dict(__UpperCamelCase	)







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			List[Any]:
					lowerCamelCase_  				=      os.path.abspath(__UpperCamelCase	)
					logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''	)

					# import correct flax class
					lowerCamelCase_  				=      getattr(__UpperCamelCase    ,'Flax' + model.__class__.__name__	)

					# load flax weight dict
					with open(__UpperCamelCase    ,'rb'	) as state_f:
										try:
															lowerCamelCase_  				=      from_bytes(__UpperCamelCase    ,state_f.read()	)
										except UnpicklingError:
															raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''	)

					return load_flax_weights_in_pytorch_model(__UpperCamelCase    ,__UpperCamelCase	)







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			List[str]:

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
										    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
										    ' instructions.'	)
										raise

					# check if we have bf16 weights
					lowerCamelCase_  				=      flatten_dict(jax.tree_util.tree_map(lambda __UpperCamelCase	: x.dtype == jnp.bfloataa    ,__UpperCamelCase	)	).values()
					if any(__UpperCamelCase	):
										# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
										    'before loading those in PyTorch model.'	)
										lowerCamelCase_  				=      jax.tree_util.tree_map(
										    lambda __UpperCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params    ,__UpperCamelCase	)

					lowerCamelCase_  				=      flatten_dict(__UpperCamelCase	)
					lowerCamelCase_  				=      pt_model.state_dict()

					lowerCamelCase_  				=      (pt_model.base_model_prefix in flax_state) and (
					    pt_model.base_model_prefix not in {k.split('.'	)[0] for k in pt_model_dict.keys()}
					)
					lowerCamelCase_  				=      (pt_model.base_model_prefix not in flax_state) and (
					    pt_model.base_model_prefix in {k.split('.'	)[0] for k in pt_model_dict.keys()}
					)

					# keep track of unexpected & missing keys
					lowerCamelCase_  				=      []
					lowerCamelCase_  				=      set(pt_model_dict.keys()	)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										lowerCamelCase_  				=      flax_key_tuple[0] == pt_model.base_model_prefix
										lowerCamelCase_  				=      '.'.join((pt_model.base_model_prefix,) + flax_key_tuple	) in pt_model_dict

										# adapt flax_key to prepare for loading from/to base model only
										if load_model_with_head_into_base_model and has_base_model_prefix:
															lowerCamelCase_  				=      flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															lowerCamelCase_  				=      (pt_model.base_model_prefix,) + flax_key_tuple

										# rename flax weights to PyTorch format
										if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__UpperCamelCase	) not in pt_model_dict:
															# conv layer
															lowerCamelCase_  				=      flax_key_tuple[:-1] + ('weight',)
															lowerCamelCase_  				=      jnp.transpose(__UpperCamelCase    ,(3, 2, 0, 1)	)
										elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase	) not in pt_model_dict:
															# linear layer
															lowerCamelCase_  				=      flax_key_tuple[:-1] + ('weight',)
															lowerCamelCase_  				=      flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															lowerCamelCase_  				=      flax_key_tuple[:-1] + ('weight',)

										# adding batch stats from flax batch norm to pt
										elif "mean" in flax_key_tuple[-1]:
															lowerCamelCase_  				=      flax_key_tuple[:-1] + ('running_mean',)
										elif "var" in flax_key_tuple[-1]:
															lowerCamelCase_  				=      flax_key_tuple[:-1] + ('running_var',)

										if "batch_stats" in flax_state:
															lowerCamelCase_  				=      '.'.join(flax_key_tuple[1:]	)  # Remove the params/batch_stats header
										else:
															lowerCamelCase_  				=      '.'.join(__UpperCamelCase	)

										# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
										lowerCamelCase_  				=      {}
										# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
										for key in pt_model_dict:
															lowerCamelCase_  				=      key.split('.'	)
															lowerCamelCase_  				=      None
															if key_components[-3::2] == ["parametrizations", "original0"]:
																				lowerCamelCase_  				=      key_components[-2] + '_g'
															elif key_components[-3::2] == ["parametrizations", "original1"]:
																				lowerCamelCase_  				=      key_components[-2] + '_v'
															if name is not None:
																				lowerCamelCase_  				=      key_components[:-3] + [name]
																				lowerCamelCase_  				=      '.'.join(__UpperCamelCase	)
																				lowerCamelCase_  				=      key

										if flax_key in special_pt_names:
															lowerCamelCase_  				=      special_pt_names[flax_key]

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																				    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)
															else:
																				# add weight to pytorch dict
																				lowerCamelCase_  				=      np.asarray(__UpperCamelCase	) if not isinstance(__UpperCamelCase    ,np.ndarray	) else flax_tensor
																				lowerCamelCase_  				=      torch.from_numpy(__UpperCamelCase	)
																				# remove from missing keys
																				missing_keys.remove(__UpperCamelCase	)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__UpperCamelCase	)

					pt_model.load_state_dict(__UpperCamelCase	)

					# re-transform missing_keys to list
					lowerCamelCase_  				=      list(__UpperCamelCase	)

					if len(__UpperCamelCase	) > 0:
										logger.warning(
										    'Some weights of the Flax model were not used when initializing the PyTorch model'
										    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
										    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
										    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
										    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
										    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
										    ' FlaxBertForSequenceClassification model).'	)
					else:
										logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''	)
					if len(__UpperCamelCase	) > 0:
										logger.warning(
										    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
										    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
										    ' use it for predictions and inference.'	)
					else:
										logger.warning(
										    f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
										    'If your task is similar to the task the model of the checkpoint was trained on, '
										    f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''	)

					return pt_model




'''simple docstring'''

from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= RobertaConfig
      UpperCAmelCase_   :   Tuple   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	RobertaEmbeddings(lowerCamelCase_				)
         self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= RobertaConfig
      UpperCAmelCase_   :   Optional[Any]   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	config.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	config.num_hidden_layers

         UpperCAmelCase__				:					str 					=	DeeRobertaModel(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Dropout(config.hidden_dropout_prob				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(config.hidden_size       ,self.config.num_labels				)




      @add_start_docstrings_to_model_forward(lowerCamelCase_				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=-1       ,lowerCamelCase_=False       ,)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_layers
         try:
            UpperCAmelCase__				:					Union[str, Any] 					=	self.roberta(
                lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,position_ids=lowerCamelCase_       ,head_mask=lowerCamelCase_       ,inputs_embeds=lowerCamelCase_       ,)

            UpperCAmelCase__				:					List[Any] 					=	outputs[1]

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

         if not self.training:
            UpperCAmelCase__				:					Dict 					=	entropy(lowerCamelCase_				)
            UpperCAmelCase__				:					List[Any] 					=	[]
            UpperCAmelCase__				:					str 					=	[]
         if labels is not None:
            if self.num_labels == 1:
               #  We are doing regression
               UpperCAmelCase__				:					str 					=	MSELoss()
               UpperCAmelCase__				:					Union[str, Any] 					=	loss_fct(logits.view(-1				)       ,labels.view(-1				)				)
            else:
               UpperCAmelCase__				:					Any 					=	CrossEntropyLoss()
               UpperCAmelCase__				:					Optional[Any] 					=	loss_fct(logits.view(-1       ,self.num_labels				)       ,labels.view(-1				)				)

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

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

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




