






'''simple docstring'''



import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

SCREAMING_SNAKE_CASE							=						[
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		bool  ,   UpperCAmelCase      :		str = None  ,   UpperCAmelCase      :		list = None		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=None
       lowercase       : Any						=os.path.abspath(os.path.join('''examples'''  ,   '''by_feature'''		)		)
       lowercase       : List[str]						=os.path.abspath('''examples'''		)
       for item in os.listdir(UpperCAmelCase		):
        if item not in EXCLUDE_EXAMPLES:
         lowercase       : Union[str, Any]						=os.path.join(UpperCAmelCase  ,   UpperCAmelCase		)
         if os.path.isfile(UpperCAmelCase		) and ".py" in item_path:
          with self.subTest(
              tested_script=UpperCAmelCase  ,   feature_script=UpperCAmelCase  ,   tested_section='''main()''' if parser_only else '''training_function()'''  ,   ):
           lowercase       : List[Any]						=compare_against_test(
               os.path.join(UpperCAmelCase  ,   UpperCAmelCase		)  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
           lowercase       : Optional[int]						='''\n'''.join(UpperCAmelCase		)
           if special_strings is not None:
            for string in special_strings:
             lowercase       : Optional[Any]						=diff.replace(UpperCAmelCase  ,   ''''''		)
           self.assertEqual(UpperCAmelCase  ,   ''''''		)






      def 					A__   (			self      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       self.one_complete_example('''complete_nlp_example.py'''  ,   UpperCAmelCase		)
       self.one_complete_example('''complete_nlp_example.py'''  ,   UpperCAmelCase		)






      def 					A__   (			self      :		str		)	->	Any:


       '''simple docstring'''

       lowercase       : str						=os.path.abspath(os.path.join('''examples'''  ,   '''cv_example.py'''		)		)
       lowercase       : str						=[
           ''' ''' * 16 + '''{\n\n''',
           ''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
           ''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
           ''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
           ''' ''' * 20 + '''"epoch": epoch,\n\n''',
           ''' ''' * 16 + '''},\n\n''',
           ''' ''' * 16 + '''step=epoch,\n''',
           ''' ''' * 12,
           ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
       ]
       self.one_complete_example('''complete_cv_example.py'''  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
       self.one_complete_example('''complete_cv_example.py'''  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)





@mock.patch.dict(os.environ					,							{'''TESTING_MOCKED_DATALOADERS''': '''1'''}					)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      False






      @classmethod
      def 					A__   (			cls      :		Optional[Any]		)	->	List[Any]:


       '''simple docstring'''

       super().setUpClass()
       lowercase       : List[str]						=tempfile.mkdtemp()
       lowercase       : Union[str, Any]						=os.path.join(cls._tmpdir  ,   '''default_config.yml'''		)

       write_basic_config(save_location=cls.configPath		)
       lowercase       : Any						=['''accelerate''', '''launch''', '''--config_file''', cls.configPath]






      @classmethod
      def 					A__   (			cls      :		Optional[Any]		)	->	Tuple:


       '''simple docstring'''

       super().tearDownClass()
       shutil.rmtree(cls._tmpdir		)






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Dict						=f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
       run_command(self._launch_args + testargs		)
       self.assertTrue(os.path.exists(os.path.join(self.tmpdir  ,   '''epoch_0'''		)		)		)






      def 					A__   (			self      :		int		)	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Dict						=f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
       lowercase       : str						=run_command(self._launch_args + testargs		)
       self.assertTrue(os.path.exists(os.path.join(self.tmpdir  ,   '''step_2'''		)		)		)






      def 					A__   (			self      :		Union[str, Any]		)	->	Dict:


       '''simple docstring'''

       lowercase       : Optional[int]						=f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir  ,   "epoch_0"		)}\n        '.split()
       lowercase       : Any						=run_command(self._launch_args + testargs  ,   return_stdout=UpperCAmelCase		)
       self.assertNotIn('''epoch 0:'''  ,   UpperCAmelCase		)
       self.assertIn('''epoch 1:'''  ,   UpperCAmelCase		)






      def 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : List[str]						=f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir  ,   "step_2"		)}\n        '.split()
       lowercase       : Tuple						=run_command(self._launch_args + testargs  ,   return_stdout=UpperCAmelCase		)
       if torch.cuda.is_available():
        lowercase       : List[Any]						=torch.cuda.device_count()
       else:
        lowercase       : Dict						=1
       if num_processes > 1:
        self.assertNotIn('''epoch 0:'''  ,   UpperCAmelCase		)
        self.assertIn('''epoch 1:'''  ,   UpperCAmelCase		)
       else:
        self.assertIn('''epoch 0:'''  ,   UpperCAmelCase		)
        self.assertIn('''epoch 1:'''  ,   UpperCAmelCase		)






      @slow
      def 					A__   (			self      :		Any		)	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : List[Any]						='''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
       with mock.patch.dict(os.environ  ,   {'''TESTING_MOCKED_DATALOADERS''': '''0'''}		):
        lowercase       : List[str]						=run_command(self._launch_args + testargs  ,   return_stdout=UpperCAmelCase		)
        lowercase       : List[Any]						=re.findall('''({.+})'''  ,   UpperCAmelCase		)
        lowercase       : Tuple						=[r for r in results if '''accuracy''' in r][-1]
        lowercase       : Union[str, Any]						=ast.literal_eval(UpperCAmelCase		)
        self.assertGreaterEqual(results['''accuracy''']  ,   0.7_5		)






      def 					A__   (			self      :		Any		)	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Any						=['''examples/by_feature/multi_process_metrics.py''']
       run_command(self._launch_args + testargs		)






      @require_trackers
      @mock.patch.dict(os.environ  ,   {'''WANDB_MODE''': '''offline'''}		)
      def 					A__   (			self      :		int		)	->	List[str]:


       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmpdir:
        lowercase       : Tuple						=f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
        run_command(self._launch_args + testargs		)
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase  ,   '''tracking'''		)		)		)






      def 					A__   (			self      :		List[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=['''examples/by_feature/gradient_accumulation.py''']
       run_command(self._launch_args + testargs		)






      def 					A__   (			self      :		Dict		)	->	Tuple:


       '''simple docstring'''

       lowercase       : List[str]						=['''examples/by_feature/local_sgd.py''']
       run_command(self._launch_args + testargs		)








"""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							(    __snake_case      )		->  Dict:
			__lowerCAmelCase :    Optional[int]				=			torch.exp(__snake_case      )
			__lowerCAmelCase :    int				=			torch.sum(__snake_case   ,dim=1      )  # sum of exp(x_i)
			__lowerCAmelCase :    Optional[Any]				=			torch.sum(x * exp_x   ,dim=1      )  # sum of x_i * exp(x_i)
			return torch.log(__snake_case      ) - B / A



class      A__  (    nn.Module				):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase :    Any				=			config.output_attentions
				__lowerCAmelCase :    Optional[Any]				=			config.output_hidden_states
				__lowerCAmelCase :    Tuple				=			nn.ModuleList([BertLayer(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])
				__lowerCAmelCase :    int				=			nn.ModuleList([BertHighway(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Optional[int]:

				"""simple docstring"""

				if (type(_SCREAMING_SNAKE_CASE) is float) or (type(_SCREAMING_SNAKE_CASE) is int):
							for i in range(len(self.early_exit_entropy)):
										__lowerCAmelCase :    List[Any]				=			x
				else:
							__lowerCAmelCase :    str				=			x

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			pooler.state_dict()
				for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
										param.copy_(loaded_model[name])







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			()
				__lowerCAmelCase :    Tuple				=			()
				__lowerCAmelCase :    Optional[Any]				=			()
				for i, layer_module in enumerate(self.layer):
							if self.output_hidden_states:
										__lowerCAmelCase :    Tuple				=			all_hidden_states + (hidden_states,)

							__lowerCAmelCase :    Any				=			layer_module(
							    _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     head_mask[i]			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    str				=			layer_outputs[0]

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

							__lowerCAmelCase :    Optional[int]				=			(hidden_states,)
							if self.output_hidden_states:
										__lowerCAmelCase :    Tuple				=			current_outputs + (all_hidden_states,)
							if self.output_attentions:
										__lowerCAmelCase :    List[str]				=			current_outputs + (all_attentions,)

							__lowerCAmelCase :    List[str]				=			self.highway[i](_SCREAMING_SNAKE_CASE)
							# logits, pooled_output

							if not self.training:
										__lowerCAmelCase :    Union[str, Any]				=			highway_exit[0]
										__lowerCAmelCase :    Dict				=			entropy(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase :    Dict				=			highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
										__lowerCAmelCase :    Dict				=			all_highway_exits + (highway_exit,)

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

        # Add last layer
				if self.output_hidden_states:
							__lowerCAmelCase :    Union[str, Any]				=			all_hidden_states + (hidden_states,)

				__lowerCAmelCase :    str				=			(hidden_states,)
				if self.output_hidden_states:
							__lowerCAmelCase :    Union[str, Any]				=			outputs + (all_hidden_states,)
				if self.output_attentions:
							__lowerCAmelCase :    Optional[int]				=			outputs + (all_attentions,)

				__lowerCAmelCase :    List[str]				=			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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Tuple				=			config

				__lowerCAmelCase :    str				=			BertEmbeddings(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			DeeBertEncoder(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			BertPooler(_SCREAMING_SNAKE_CASE)

				self.init_weights()

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Any:

				"""simple docstring"""

				self.encoder.init_highway_pooler(self.pooler)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			str:

				"""simple docstring"""

				return self.embeddings.word_embeddings

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			value

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

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







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=None			,     _SCREAMING_SNAKE_CASE:						int=None			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     )							->			Dict:

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

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

				if attention_mask is None:
							__lowerCAmelCase :    Union[str, Any]				=			torch.ones(_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE)
				if encoder_attention_mask is None:
							__lowerCAmelCase :    Tuple				=			torch.ones(_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE)
				if token_type_ids is None:
							__lowerCAmelCase :    Union[str, Any]				=			torch.zeros(_SCREAMING_SNAKE_CASE			,     dtype=torch.long			,     device=_SCREAMING_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 :    torch.Tensor				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_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 :    Union[str, Any]				=			encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
							__lowerCAmelCase :    Optional[Any]				=			encoder_attention_mask[:, None, None, :]

				__lowerCAmelCase :    List[str]				=			encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()).dtype)  # fp16 compatibility
				__lowerCAmelCase :    Union[str, Any]				=			(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]
				__lowerCAmelCase :    Union[str, Any]				=			self.get_head_mask(_SCREAMING_SNAKE_CASE			,     self.config.num_hidden_layers)

				__lowerCAmelCase :    Union[str, Any]				=			self.embeddings(
				    input_ids=_SCREAMING_SNAKE_CASE			,     position_ids=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     inputs_embeds=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			self.encoder(
				    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     encoder_attention_mask=_SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    List[str]				=			encoder_outputs[0]
				__lowerCAmelCase :    Union[str, Any]				=			self.pooler(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			(
				    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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			message
				__lowerCAmelCase :    Union[str, Any]				=			exit_layer  # start from 1!






class      A__  (    nn.Module				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Dict)							->			str:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase :    Optional[int]				=			BertPooler(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			nn.Dropout(config.hidden_dropout_prob)
				__lowerCAmelCase :    Optional[Any]				=			nn.Linear(config.hidden_size			,     config.num_labels)







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			encoder_outputs[0]
				__lowerCAmelCase :    Union[str, Any]				=			self.pooler(_SCREAMING_SNAKE_CASE)
				# "return" pooler_output

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

				# Dropout and classification
				__lowerCAmelCase :    Tuple				=			bmodel_output[1]

				__lowerCAmelCase :    int				=			self.dropout(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			self.classifier(_SCREAMING_SNAKE_CASE)

				return logits, pooled_output






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




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any])							->			Dict:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			config.num_labels
				__lowerCAmelCase :    Tuple				=			config.num_hidden_layers

				__lowerCAmelCase :    Optional[Any]				=			DeeBertModel(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			nn.Dropout(config.hidden_dropout_prob)
				__lowerCAmelCase :    Dict				=			nn.Linear(config.hidden_size			,     self.config.num_labels)

				self.init_weights()







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						Any=None			,     _SCREAMING_SNAKE_CASE:						Dict=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Dict=None			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=-1			,     _SCREAMING_SNAKE_CASE:						str=False			,     )							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.num_layers
				try:
							__lowerCAmelCase :    Optional[int]				=			self.bert(
							    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     position_ids=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     inputs_embeds=_SCREAMING_SNAKE_CASE			,     )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							__lowerCAmelCase :    Tuple				=			outputs[1]

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

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

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

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