code: 
  '''simple docstring'''
  
  import argparse
  import logging
  import os
  from datetime import datetime
  
  import numpy as np
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, SequentialSampler, Subset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      GlueDataset,
      default_data_collator,
      glue_compute_metrics,
      glue_output_modes,
      glue_processors,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.getLogger(__name__)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      List[Any]:
  		_UpperCamelCase       : Optional[Any]    =      p * torch.log(UpperCamelCase	)
  		_UpperCamelCase       : Any    =      0
  		return -plogp.sum(dim=-1	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      str:
  		logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(UpperCamelCase	)	)	)	)
  		for row in range(len(UpperCamelCase	)	):
  				if tensor.dtype != torch.long:
  						logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:.5f}''' for x in tensor[row].cpu().data	)	)
  				else:
  						logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:d}''' for x in tensor[row].cpu().data	)	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase=True			,UpperCamelCase=True			,UpperCamelCase=None			,UpperCamelCase=False	) ->      str:
  		_UpperCamelCase       : Dict    =      model.config.num_hidden_layers, model.config.num_attention_heads
  		_UpperCamelCase       : Tuple    =      torch.zeros(UpperCamelCase			,UpperCamelCase	).to(args.device	)
  		_UpperCamelCase       : Union[str, Any]    =      torch.zeros(UpperCamelCase			,UpperCamelCase	).to(args.device	)
  
  		if head_mask is None:
  				_UpperCamelCase       : List[Any]    =      torch.ones(UpperCamelCase			,UpperCamelCase	).to(args.device	)
  
  		head_mask.requires_grad_(requires_grad=UpperCamelCase	)
  		# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  		if actually_pruned:
  				_UpperCamelCase       : List[Any]    =      None
  
  		_UpperCamelCase       : Optional[Any]    =      None
  		_UpperCamelCase       : List[Any]    =      None
  		_UpperCamelCase       : Any    =      0.0
  
  		for step, inputs in enumerate(tqdm(UpperCamelCase			,desc='''Iteration'''			,disable=args.local_rank not in [-1, 0]	)	):
  				for k, v in inputs.items():
  						_UpperCamelCase       : Union[str, Any]    =      v.to(args.device	)
  
  				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
  				_UpperCamelCase       : Optional[int]    =      model(**UpperCamelCase			,head_mask=UpperCamelCase	)
  				_UpperCamelCase       : Any    =      (
  				    outputs[0],
  				    outputs[1],
  				    outputs[-1],
  				)  # Loss and logits are the first, attention the last
  				loss.backward()  # Backpropagate to populate the gradients in the head mask
  
  				if compute_entropy:
  						for layer, attn in enumerate(UpperCamelCase	):
  								_UpperCamelCase       : Any    =      entropy(attn.detach()	) * inputs['''attention_mask'''].float().unsqueeze(1	)
  								attn_entropy[layer] += masked_entropy.sum(-1	).sum(0	).detach()
  
  				if compute_importance:
  						head_importance += head_mask.grad.abs().detach()
  
  				# Also store our logits/labels if we want to compute metrics afterwards
  				if preds is None:
  						_UpperCamelCase       : Optional[int]    =      logits.detach().cpu().numpy()
  						_UpperCamelCase       : List[str]    =      inputs['''labels'''].detach().cpu().numpy()
  				else:
  						_UpperCamelCase       : List[Any]    =      np.append(UpperCamelCase			,logits.detach().cpu().numpy()			,axis=0	)
  						_UpperCamelCase       : List[str]    =      np.append(UpperCamelCase			,inputs['''labels'''].detach().cpu().numpy()			,axis=0	)
  
  				tot_tokens += inputs["attention_mask"].float().detach().sum().data
  
  		# Normalize
  		attn_entropy /= tot_tokens
  		head_importance /= tot_tokens
  		# Layerwise importance normalization
  		if not args.dont_normalize_importance_by_layer:
  				_UpperCamelCase       : List[str]    =      2
  				_UpperCamelCase       : Tuple    =      torch.pow(torch.pow(UpperCamelCase			,UpperCamelCase	).sum(-1	)			,1 / exponent	)
  				head_importance /= norm_by_layer.unsqueeze(-1	) + 1e-20
  
  		if not args.dont_normalize_global_importance:
  				_UpperCamelCase       : Optional[Any]    =      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
  
  		# Print/save matrices
  		np.save(os.path.join(args.output_dir			,'''attn_entropy.npy'''	)			,attn_entropy.detach().cpu().numpy()	)
  		np.save(os.path.join(args.output_dir			,'''head_importance.npy'''	)			,head_importance.detach().cpu().numpy()	)
  
  		logger.info('''Attention entropies'''	)
  		print_ad_tensor(UpperCamelCase	)
  		logger.info('''Head importance scores'''	)
  		print_ad_tensor(UpperCamelCase	)
  		logger.info('''Head ranked by importance scores'''	)
  		_UpperCamelCase       : Union[str, Any]    =      torch.zeros(head_importance.numel()			,dtype=torch.long			,device=args.device	)
  		_UpperCamelCase       : Tuple    =      torch.arange(
  		    head_importance.numel()			,device=args.device	)
  		_UpperCamelCase       : List[Any]    =      head_ranks.view_as(UpperCamelCase	)
  		print_ad_tensor(UpperCamelCase	)
  
  		return attn_entropy, head_importance, preds, labels
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      List[Any]:
  		_UpperCamelCase       : Optional[Any]    =      compute_heads_importance(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,compute_entropy=UpperCamelCase	)
  		_UpperCamelCase       : int    =      np.argmax(UpperCamelCase			,axis=1	) if args.output_mode == '''classification''' else np.squeeze(UpperCamelCase	)
  		_UpperCamelCase       : Optional[Any]    =      glue_compute_metrics(args.task_name			,UpperCamelCase			,UpperCamelCase	)[args.metric_name]
  		logger.info('''Pruning: original score: %f, threshold: %f'''			,UpperCamelCase			,original_score * args.masking_threshold	)
  
  		_UpperCamelCase       : List[str]    =      torch.ones_like(UpperCamelCase	)
  		_UpperCamelCase       : Optional[int]    =      max(1			,int(new_head_mask.numel() * args.masking_amount	)	)
  
  		_UpperCamelCase       : int    =      original_score
  		while current_score >= original_score * args.masking_threshold:
  				_UpperCamelCase       : int    =      new_head_mask.clone()  # save current head mask
  				# heads from least important to most - keep only not-masked heads
  				_UpperCamelCase       : Any    =      float('''Inf'''	)
  				_UpperCamelCase       : str    =      head_importance.view(-1	).sort()[1]
  
  				if len(UpperCamelCase	) <= num_to_mask:
  						break
  
  				# mask heads
  				_UpperCamelCase       : int    =      current_heads_to_mask[:num_to_mask]
  				logger.info('''Heads to mask: %s'''			,str(current_heads_to_mask.tolist()	)	)
  				_UpperCamelCase       : Any    =      new_head_mask.view(-1	)
  				_UpperCamelCase       : List[Any]    =      0.0
  				_UpperCamelCase       : int    =      new_head_mask.view_as(UpperCamelCase	)
  				_UpperCamelCase       : Optional[int]    =      new_head_mask.clone().detach()
  				print_ad_tensor(UpperCamelCase	)
  
  				# Compute metric and head importance again
  				_UpperCamelCase       : Optional[int]    =      compute_heads_importance(
  				    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,compute_entropy=UpperCamelCase			,head_mask=UpperCamelCase	)
  				_UpperCamelCase       : int    =      np.argmax(UpperCamelCase			,axis=1	) if args.output_mode == '''classification''' else np.squeeze(UpperCamelCase	)
  				_UpperCamelCase       : Any    =      glue_compute_metrics(args.task_name			,UpperCamelCase			,UpperCamelCase	)[args.metric_name]
  				logger.info(
  				    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''			,UpperCamelCase			,new_head_mask.sum()			,new_head_mask.sum() / new_head_mask.numel() * 1_00			,)
  
  		logger.info('''Final head mask'''	)
  		print_ad_tensor(UpperCamelCase	)
  		np.save(os.path.join(args.output_dir			,'''head_mask.npy'''	)			,head_mask.detach().cpu().numpy()	)
  
  		return head_mask
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Any:
  		_UpperCamelCase       : Tuple    =      datetime.now()
  		_UpperCamelCase       : Dict    =      compute_heads_importance(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,compute_entropy=UpperCamelCase			,compute_importance=UpperCamelCase			,head_mask=UpperCamelCase	)
  		_UpperCamelCase       : Dict    =      np.argmax(UpperCamelCase			,axis=1	) if args.output_mode == '''classification''' else np.squeeze(UpperCamelCase	)
  		_UpperCamelCase       : Any    =      glue_compute_metrics(args.task_name			,UpperCamelCase			,UpperCamelCase	)[args.metric_name]
  		_UpperCamelCase       : Tuple    =      datetime.now() - before_time
  
  		_UpperCamelCase       : str    =      sum(p.numel() for p in model.parameters()	)
  		_UpperCamelCase       : List[str]    =      {
  		    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(UpperCamelCase	)	)
  		}
  
  		assert sum(len(UpperCamelCase	) for h in heads_to_prune.values()	) == (1 - head_mask.long()).sum().item()
  		model.prune_heads(UpperCamelCase	)
  		_UpperCamelCase       : str    =      sum(p.numel() for p in model.parameters()	)
  
  		_UpperCamelCase       : Optional[int]    =      datetime.now()
  		_UpperCamelCase       : int    =      compute_heads_importance(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,compute_entropy=UpperCamelCase			,compute_importance=UpperCamelCase			,head_mask=UpperCamelCase			,actually_pruned=UpperCamelCase			,)
  		_UpperCamelCase       : str    =      np.argmax(UpperCamelCase			,axis=1	) if args.output_mode == '''classification''' else np.squeeze(UpperCamelCase	)
  		_UpperCamelCase       : int    =      glue_compute_metrics(args.task_name			,UpperCamelCase			,UpperCamelCase	)[args.metric_name]
  		_UpperCamelCase       : Union[str, Any]    =      datetime.now() - before_time
  
  		logger.info(
  		    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''			,UpperCamelCase			,UpperCamelCase			,pruned_num_params / original_num_params * 1_00			,)
  		logger.info('''Pruning: score with masking: %f score with pruning: %f'''			,UpperCamelCase			,UpperCamelCase	)
  		logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''			,original_time / new_time * 1_00	)
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Union[str, Any]:
  		_UpperCamelCase       : List[Any]    =      argparse.ArgumentParser()
  		# Required parameters
  		parser.add_argument(
  		    '''--data_dir'''			,default=UpperCamelCase			,type=UpperCamelCase			,required=UpperCamelCase			,help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''			,)
  		parser.add_argument(
  		    '''--model_name_or_path'''			,default=UpperCamelCase			,type=UpperCamelCase			,required=UpperCamelCase			,help='''Path to pretrained model or model identifier from huggingface.co/models'''			,)
  		parser.add_argument(
  		    '''--task_name'''			,default=UpperCamelCase			,type=UpperCamelCase			,required=UpperCamelCase			,help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()	)			,)
  		parser.add_argument(
  		    '''--output_dir'''			,default=UpperCamelCase			,type=UpperCamelCase			,required=UpperCamelCase			,help='''The output directory where the model predictions and checkpoints will be written.'''			,)
  
  		# Other parameters
  		parser.add_argument(
  		    '''--config_name'''			,default=''''''			,type=UpperCamelCase			,help='''Pretrained config name or path if not the same as model_name_or_path'''			,)
  		parser.add_argument(
  		    '''--tokenizer_name'''			,default=''''''			,type=UpperCamelCase			,help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''			,)
  		parser.add_argument(
  		    '''--cache_dir'''			,default=UpperCamelCase			,type=UpperCamelCase			,help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''			,)
  		parser.add_argument(
  		    '''--data_subset'''			,type=UpperCamelCase			,default=-1			,help='''If > 0: limit the data to a subset of data_subset instances.'''	)
  		parser.add_argument(
  		    '''--overwrite_output_dir'''			,action='''store_true'''			,help='''Whether to overwrite data in output directory'''	)
  		parser.add_argument(
  		    '''--overwrite_cache'''			,action='''store_true'''			,help='''Overwrite the cached training and evaluation sets'''	)
  
  		parser.add_argument(
  		    '''--dont_normalize_importance_by_layer'''			,action='''store_true'''			,help='''Don\'t normalize importance score by layers'''	)
  		parser.add_argument(
  		    '''--dont_normalize_global_importance'''			,action='''store_true'''			,help='''Don\'t normalize all importance scores between 0 and 1'''			,)
  
  		parser.add_argument(
  		    '''--try_masking'''			,action='''store_true'''			,help='''Whether to try to mask head until a threshold of accuracy.'''	)
  		parser.add_argument(
  		    '''--masking_threshold'''			,default=0.9			,type=UpperCamelCase			,help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''			,)
  		parser.add_argument(
  		    '''--masking_amount'''			,default=0.1			,type=UpperCamelCase			,help='''Amount to heads to masking at each masking step.'''	)
  		parser.add_argument('''--metric_name'''			,default='''acc'''			,type=UpperCamelCase			,help='''Metric to use for head masking.'''	)
  
  		parser.add_argument(
  		    '''--max_seq_length'''			,default=1_28			,type=UpperCamelCase			,help=(
  		        '''The maximum total input sequence length after WordPiece tokenization. \n'''
  		        '''Sequences longer than this will be truncated, sequences shorter padded.'''
  		    )			,)
  		parser.add_argument('''--batch_size'''			,default=1			,type=UpperCamelCase			,help='''Batch size.'''	)
  
  		parser.add_argument('''--seed'''			,type=UpperCamelCase			,default=42	)
  		parser.add_argument('''--local_rank'''			,type=UpperCamelCase			,default=-1			,help='''local_rank for distributed training on gpus'''	)
  		parser.add_argument('''--no_cuda'''			,action='''store_true'''			,help='''Whether not to use CUDA when available'''	)
  		parser.add_argument('''--server_ip'''			,type=UpperCamelCase			,default=''''''			,help='''Can be used for distant debugging.'''	)
  		parser.add_argument('''--server_port'''			,type=UpperCamelCase			,default=''''''			,help='''Can be used for distant debugging.'''	)
  		_UpperCamelCase       : Tuple    =      parser.parse_args()
  
  		if args.server_ip and args.server_port:
  				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
  				import ptvsd
  
  				print('''Waiting for debugger attach'''	)
  				ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,redirect_output=UpperCamelCase	)
  				ptvsd.wait_for_attach()
  
  		# Setup devices and distributed training
  		if args.local_rank == -1 or args.no_cuda:
  				_UpperCamelCase       : List[str]    =      torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''	)
  				_UpperCamelCase       : str    =      0 if args.no_cuda else torch.cuda.device_count()
  		else:
  				torch.cuda.set_device(args.local_rank	)
  				_UpperCamelCase       : Dict    =      torch.device('''cuda'''			,args.local_rank	)
  				_UpperCamelCase       : List[Any]    =      1
  				torch.distributed.init_process_group(backend='''nccl'''	)  # Initializes the distributed backend
  
  		# Setup logging
  		logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN	)
  		logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device			,args.n_gpu			,bool(args.local_rank != -1	)	)	)
  		# Set the verbosity to info of the Transformers logger (on main process only):
  		if is_main_process(args.local_rank	):
  				transformers.utils.logging.set_verbosity_info()
  				transformers.utils.logging.enable_default_handler()
  				transformers.utils.logging.enable_explicit_format()
  
  		# Set seeds
  		set_seed(args.seed	)
  
  		# Prepare GLUE task
  		_UpperCamelCase       : Union[str, Any]    =      args.task_name.lower()
  		if args.task_name not in glue_processors:
  				raise ValueError('''Task not found: %s''' % (args.task_name)	)
  		_UpperCamelCase       : str    =      glue_processors[args.task_name]()
  		_UpperCamelCase       : List[str]    =      glue_output_modes[args.task_name]
  		_UpperCamelCase       : Any    =      processor.get_labels()
  		_UpperCamelCase       : Any    =      len(UpperCamelCase	)
  
  		# Load pretrained model and tokenizer
  		#
  		# Distributed training:
  		# The .from_pretrained methods guarantee that only one local process can concurrently
  		# download model & vocab.
  
  		_UpperCamelCase       : Tuple    =      AutoConfig.from_pretrained(
  		    args.config_name if args.config_name else args.model_name_or_path			,num_labels=UpperCamelCase			,finetuning_task=args.task_name			,output_attentions=UpperCamelCase			,cache_dir=args.cache_dir			,)
  		_UpperCamelCase       : Optional[Any]    =      AutoTokenizer.from_pretrained(
  		    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path			,cache_dir=args.cache_dir			,)
  		_UpperCamelCase       : Tuple    =      AutoModelForSequenceClassification.from_pretrained(
  		    args.model_name_or_path			,from_tf=bool('''.ckpt''' in args.model_name_or_path	)			,config=UpperCamelCase			,cache_dir=args.cache_dir			,)
  
  		# Distributed and parallel training
  		model.to(args.device	)
  		if args.local_rank != -1:
  				_UpperCamelCase       : Dict    =      nn.parallel.DistributedDataParallel(
  				    UpperCamelCase			,device_ids=[args.local_rank]			,output_device=args.local_rank			,find_unused_parameters=UpperCamelCase	)
  		elif args.n_gpu > 1:
  				_UpperCamelCase       : List[str]    =      nn.DataParallel(UpperCamelCase	)
  
  		# Print/save training arguments
  		os.makedirs(args.output_dir			,exist_ok=UpperCamelCase	)
  		torch.save(UpperCamelCase			,os.path.join(args.output_dir			,'''run_args.bin'''	)	)
  		logger.info('''Training/evaluation parameters %s'''			,UpperCamelCase	)
  
  		# Prepare dataset for the GLUE task
  		_UpperCamelCase       : Tuple    =      GlueDataset(UpperCamelCase			,tokenizer=UpperCamelCase			,mode='''dev'''	)
  		if args.data_subset > 0:
  				_UpperCamelCase       : Union[str, Any]    =      Subset(UpperCamelCase			,list(range(min(args.data_subset			,len(UpperCamelCase	)	)	)	)	)
  		_UpperCamelCase       : Optional[int]    =      SequentialSampler(UpperCamelCase	) if args.local_rank == -1 else DistributedSampler(UpperCamelCase	)
  		_UpperCamelCase       : Tuple    =      DataLoader(
  		    UpperCamelCase			,sampler=UpperCamelCase			,batch_size=args.batch_size			,collate_fn=UpperCamelCase	)
  
  		# Compute head entropy and importance score
  		compute_heads_importance(UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)
  
  		# Try head masking (set heads to zero until the score goes under a threshole)
  		# and head pruning (remove masked heads and see the effect on the network)
  		if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
  				_UpperCamelCase       : int    =      mask_heads(UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)
  				prune_heads(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   List[str]          =    ('_front', '_back', '_len')
  
  		@dataclass
  		class 			UpperCAmelCase				:
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  				A__  :   Any          =    None
  				A__  :   Deque._Node | None          =    None
  				A__  :   Deque._Node | None          =    None
  
  
  
  
  
  
  
  		class 			UpperCAmelCase				:
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  				A__  :   Any          =    ('_cur',)
  				def __init__(    self  ,					_snake_case     )				->    None:
  						_UpperCamelCase       : Any    =      cur
  				def __iter__(    self     )				->    Deque._Iterator:
  						return self
  
  
  
  
  				def _lowercase  (    self     )				->    Any:
  						if self._cur is None:
  								# finished iterating
  								raise StopIteration
  						_UpperCamelCase       : List[Any]    =      self._cur.val
  						_UpperCamelCase       : List[Any]    =      self._cur.next_node
  
  						return val
  
  
  
  
  
  
  
  		def __init__(    self  ,					_snake_case = None     )				->    None:
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : int    =      0
  
  				if iterable is not None:
  						# append every value to the deque
  						for val in iterable:
  								self.append(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				_UpperCamelCase       : List[str]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
  				if self.is_empty():
  						# front = back
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : Tuple    =      1
  				else:
  						# connect nodes
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : int    =      self._back
  						_UpperCamelCase       : List[Any]    =      node  # assign new back to the new node
  
  						self._len += 1
  
  						# make sure there were no errors
  						assert not self.is_empty(), "Error on appending value."
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				_UpperCamelCase       : Optional[Any]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
  				if self.is_empty():
  						# front = back
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : Optional[int]    =      1
  				else:
  						# connect nodes
  						_UpperCamelCase       : Tuple    =      self._front
  						_UpperCamelCase       : Optional[int]    =      node
  						_UpperCamelCase       : List[str]    =      node  # assign new front to the new node
  
  						self._len += 1
  
  						# make sure there were no errors
  						assert not self.is_empty(), "Error on appending value."
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				for val in iterable:
  						self.append(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				for val in iterable:
  						self.appendleft(_snake_case     )
  		def _lowercase  (    self     )				->    Any:
  				assert not self.is_empty(), "Deque is empty."
  
  				_UpperCamelCase       : int    =      self._back
  				_UpperCamelCase       : List[Any]    =      self._back.prev_node  # set new back
  				# drop the last node - python will deallocate memory automatically
  				_UpperCamelCase       : Union[str, Any]    =      None
  
  				self._len -= 1
  
  				return topop.val
  		def _lowercase  (    self     )				->    Any:
  				assert not self.is_empty(), "Deque is empty."
  
  				_UpperCamelCase       : List[Any]    =      self._front
  				_UpperCamelCase       : str    =      self._front.next_node  # set new front and drop the first node
  				_UpperCamelCase       : Union[str, Any]    =      None
  
  				self._len -= 1
  
  				return topop.val
  		def _lowercase  (    self     )				->    bool:
  				return self._front is None
  		def __len__(    self     )				->    int:
  				return self._len
  		def __eq__(    self  ,					_snake_case     )				->    bool:
  
  				if not isinstance(_snake_case  ,					_snake_case     ):
  						return NotImplemented
  
  				_UpperCamelCase       : Optional[int]    =      self._front
  				_UpperCamelCase       : List[Any]    =      other._front
  
  				# if the length of the dequeues are not the same, they are not equal
  				if len(self     ) != len(_snake_case     ):
  						return False
  
  				while me is not None and oth is not None:
  						# compare every value
  						if me.val != oth.val:
  								return False
  						_UpperCamelCase       : str    =      me.next_node
  						_UpperCamelCase       : List[str]    =      oth.next_node
  
  				return True
  		def __iter__(    self     )				->    Deque._Iterator:
  				return Deque._Iterator(self._front     )
  
  
  
  
  		def __repr__(    self     )				->    str:
  				_UpperCamelCase       : str    =      []
  				_UpperCamelCase       : Tuple    =      self._front
  				while aux is not None:
  						# append the values in a list to display
  						values_list.append(aux.val     )
  						_UpperCamelCase       : List[str]    =      aux.next_node
  
  				return F'''[{', '.join(repr(_snake_case     ) for val in values_list     )}]'''
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  