
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConditionalDetrImageProcessor







class     lowercase		( unittest.TestCase		):




							def __init__(    self       ,A__       ,A__=7       ,A__=3       ,A__=3_0       ,A__=4_0_0       ,A__=True       ,A__=None       ,A__=True       ,A__=[0.5, 0.5, 0.5]       ,A__=[0.5, 0.5, 0.5]       ,A__=True       ,A__=1 / 2_5_5       ,A__=True       ,):
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowercase						     =						size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						num_channels
												lowercase						     =						min_resolution
												lowercase						     =						max_resolution
												lowercase						     =						do_resize
												lowercase						     =						size
												lowercase						     =						do_normalize
												lowercase						     =						image_mean
												lowercase						     =						image_std
												lowercase						     =						do_rescale
												lowercase						     =						rescale_factor
												lowercase						     =						do_pad




							def 			A__				(    self):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}





							def 			A__				(    self       ,A__       ,A__=False):
												if not batched:
																	lowercase						     =						image_inputs[0]
																	if isinstance(A__       ,Image.Image):
																						lowercase     ,						lowercase						     =						image.size
																	else:
																						lowercase     ,						lowercase						     =						image.shape[1], image.shape[2]
																	if w < h:
																						lowercase						     =						int(self.size['''shortest_edge'''] * h / w)
																						lowercase						     =						self.size['''shortest_edge''']
																	elif w > h:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						int(self.size['''shortest_edge'''] * w / h)
																	else:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						self.size['''shortest_edge''']

												else:
																	lowercase						     =						[]
																	for image in image_inputs:
																						lowercase     ,						lowercase						     =						self.get_expected_values([image])
																						expected_values.append((expected_height, expected_width))
																	lowercase						     =						max(A__       ,key=lambda A__: item[0])[0]
																	lowercase						     =						max(A__       ,key=lambda A__: item[1])[1]

												return expected_height, expected_width







@require_torch
@require_vision
class     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Dict	 =ConditionalDetrImageProcessor if is_vision_available() else None




							def 			A__				(    self):
												lowercase						     =						ConditionalDetrImageProcessingTester(self)




							@property
							def 			A__				(    self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def 			A__				(    self):
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__       ,'''image_mean'''))
												self.assertTrue(hasattr(A__       ,'''image_std'''))
												self.assertTrue(hasattr(A__       ,'''do_normalize'''))
												self.assertTrue(hasattr(A__       ,'''do_resize'''))
												self.assertTrue(hasattr(A__       ,'''size'''))




							def 			A__				(    self):
												lowercase						     =						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3})
												self.assertEqual(image_processor.do_pad       ,A__)

												lowercase						     =						self.image_processing_class.from_dict(
												    self.image_processor_dict       ,size=4_2       ,max_size=8_4       ,pad_and_return_pixel_mask=A__)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4})
												self.assertEqual(image_processor.do_pad       ,A__)




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,Image.Image)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,numpify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,np.ndarray)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,torchify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,torch.Tensor)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							@slow
							def 			A__				(    self):
												# prepare image and target
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''image_id''': 3_9_7_6_9, '''annotations''': target}

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))





							@slow
							def 			A__				(    self):
												# prepare image, target and masks_path
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

												lowercase						     =						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor(format='''coco_panoptic''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,masks_path=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify masks
												lowercase						     =						8_2_2_8_7_3
												self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,A__)
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))






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 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						torch.exp(lowerCAmelCase__		)
					lowercase						     =						torch.sum(lowerCAmelCase__			,      dim=1		)  # sum of exp(x_i)
					lowercase						     =						torch.sum(x * exp_x			,      dim=1		)  # sum of x_i * exp(x_i)
					return torch.log(lowerCAmelCase__		) - B / A







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						config.output_attentions
												lowercase						     =						config.output_hidden_states
												lowercase						     =						nn.ModuleList([BertLayer(A__) for _ in range(config.num_hidden_layers)])
												lowercase						     =						nn.ModuleList([BertHighway(A__) for _ in range(config.num_hidden_layers)])

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




							def 			A__				(    self       ,A__):
												if (type(A__) is float) or (type(A__) is int):
																	for i in range(len(self.early_exit_entropy)):
																						lowercase						     =						x
												else:
																	lowercase						     =						x




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





							def 			A__				(    self       ,A__       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												lowercase						     =						()
												lowercase						     =						()
												lowercase						     =						()
												for i, layer_module in enumerate(self.layer):
																	if self.output_hidden_states:
																						lowercase						     =						all_hidden_states + (hidden_states,)

																	lowercase						     =						layer_module(
																	    A__       ,A__       ,head_mask[i]       ,A__       ,A__)
																	lowercase						     =						layer_outputs[0]

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

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

																	lowercase						     =						self.highway[i](A__)
																	# logits, pooled_output

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

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

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

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

												lowercase						     =						outputs + (all_highway_exits,)
												return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__):
												super().__init__(A__)
												lowercase						     =						config

												lowercase						     =						BertEmbeddings(A__)
												lowercase						     =						DeeBertEncoder(A__)
												lowercase						     =						BertPooler(A__)

												self.init_weights()




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




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




							def 			A__				(    self       ,A__):
												lowercase						     =						value




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





							@add_start_docstrings_to_model_forward(A__)
							def 			A__				(    self       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												if input_ids is not None and inputs_embeds is not None:
																	raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''')
												elif input_ids is not None:
																	lowercase						     =						input_ids.size()
												elif inputs_embeds is not None:
																	lowercase						     =						inputs_embeds.size()[:-1]
												else:
																	raise ValueError('''You have to specify either input_ids or inputs_embeds''')

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

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

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

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

												lowercase						     =						encoder_extended_attention_mask.to(
												    dtype=next(self.parameters()).dtype)  # fp16 compatibility
												lowercase						     =						(1.0 - encoder_extended_attention_mask) * -10000.0

												# 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]
												lowercase						     =						self.get_head_mask(A__       ,self.config.num_hidden_layers)

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

												lowercase						     =						(
												    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     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__):
												lowercase						     =						message
												lowercase						     =						exit_layer  # start from 1!







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						BertPooler(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,config.num_labels)





							def 			A__				(    self       ,A__):
												# Pooler
												lowercase						     =						encoder_outputs[0]
												lowercase						     =						self.pooler(A__)
												# "return" pooler_output

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

												# Dropout and classification
												lowercase						     =						bmodel_output[1]

												lowercase						     =						self.dropout(A__)
												lowercase						     =						self.classifier(A__)

												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. '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__):
												super().__init__(A__)
												lowercase						     =						config.num_labels
												lowercase						     =						config.num_hidden_layers

												lowercase						     =						DeeBertModel(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,self.config.num_labels)

												self.init_weights()





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

																	lowercase						     =						outputs[1]

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

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

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

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




