code: 
  
  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        __SCREAMING_SNAKE_CASE    (      a__						:      int					)				->							List[Any]:
  	__A   : Optional[Any]								= p * torch.log(a__					)
  	__A   : Any								= 0
  	return -plogp.sum(dim=-1					)
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any]					)				->							str:
  	logger.info("""lv, h >\t""" + """\t""".join(f"""{x + 1}""" for x in range(len(a__					)					)					)					)
  	for row in range(len(a__					)					):
  		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        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      List[str] ,a__						:      Optional[Any] ,a__						:      Any=True ,a__						:      List[str]=True ,a__						:      List[str]=None ,a__						:      Any=False					)				->							str:
  	__A   : Dict								= model.config.num_hidden_layers, model.config.num_attention_heads
  	__A   : Tuple								= torch.zeros(a__ ,a__					).to(args.device					)
  	__A   : Union[str, Any]								= torch.zeros(a__ ,a__					).to(args.device					)
  
  	if head_mask is None:
  		__A   : List[Any]								= torch.ones(a__ ,a__					).to(args.device					)
  
  	head_mask.requires_grad_(requires_grad=a__					)
  	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  	if actually_pruned:
  		__A   : List[Any]								= None
  
  	__A   : Optional[Any]								= None
  	__A   : List[Any]								= None
  	__A   : Any								= 0.0
  
  	for step, inputs in enumerate(tqdm(a__ ,desc="""Iteration""" ,disable=args.local_rank not in [-1, 0]					)					):
  		for k, v in inputs.items():
  			__A   : 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)
  		__A   : Optional[int]								= model(**a__ ,head_mask=a__					)
  		__A   : 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(a__					):
  				__A   : 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:
  			__A   : Optional[int]								= logits.detach().cpu().numpy()
  			__A   : List[str]								= inputs['''labels'''].detach().cpu().numpy()
  		else:
  			__A   : List[Any]								= np.append(a__ ,logits.detach().cpu().numpy() ,axis=0					)
  			__A   : List[str]								= np.append(a__ ,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:
  		__A   : List[str]								= 2
  		__A   : Tuple								= torch.pow(torch.pow(a__ ,a__					).sum(-1					) ,1 / exponent					)
  		head_importance /= norm_by_layer.unsqueeze(-1					) + 1E-2_0
  
  	if not args.dont_normalize_global_importance:
  		__A   : 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(a__					)
  	logger.info("""Head importance scores"""					)
  	print_ad_tensor(a__					)
  	logger.info("""Head ranked by importance scores"""					)
  	__A   : Union[str, Any]								= torch.zeros(head_importance.numel() ,dtype=torch.long ,device=args.device					)
  	__A   : Tuple								= torch.arange(
  	    head_importance.numel() ,device=args.device					)
  	__A   : List[Any]								= head_ranks.view_as(a__					)
  	print_ad_tensor(a__					)
  
  	return attn_entropy, head_importance, preds, labels
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[Any] ,a__						:      Dict					)				->							List[Any]:
  	__A   : Optional[Any]								= compute_heads_importance(a__ ,a__ ,a__ ,compute_entropy=a__					)
  	__A   : int								= np.argmax(a__ ,axis=1					) if args.output_mode == '''classification''' else np.squeeze(a__					)
  	__A   : Optional[Any]								= glue_compute_metrics(args.task_name ,a__ ,a__					)[args.metric_name]
  	logger.info("""Pruning: original score: %f, threshold: %f""" ,a__ ,original_score * args.masking_threshold					)
  
  	__A   : List[str]								= torch.ones_like(a__					)
  	__A   : Optional[int]								= max(1 ,int(new_head_mask.numel() * args.masking_amount					)					)
  
  	__A   : int								= original_score
  	while current_score >= original_score * args.masking_threshold:
  		__A   : int								= new_head_mask.clone()  # save current head mask
  		# heads from least important to most - keep only not-masked heads
  		__A   : Any								= float("""Inf"""					)
  		__A   : str								= head_importance.view(-1					).sort()[1]
  
  		if len(a__					) <= num_to_mask:
  			break
  
  		# mask heads
  		__A   : int								= current_heads_to_mask[:num_to_mask]
  		logger.info("""Heads to mask: %s""" ,str(current_heads_to_mask.tolist()					)					)
  		__A   : Any								= new_head_mask.view(-1					)
  		__A   : List[Any]								= 0.0
  		__A   : int								= new_head_mask.view_as(a__					)
  		__A   : Optional[int]								= new_head_mask.clone().detach()
  		print_ad_tensor(a__					)
  
  		# Compute metric and head importance again
  		__A   : Optional[int]								= compute_heads_importance(
  		    a__ ,a__ ,a__ ,compute_entropy=a__ ,head_mask=a__					)
  		__A   : int								= np.argmax(a__ ,axis=1					) if args.output_mode == '''classification''' else np.squeeze(a__					)
  		__A   : Any								= glue_compute_metrics(args.task_name ,a__ ,a__					)[args.metric_name]
  		logger.info(
  		    """Masking: current score: %f, remaining heads %d (%.1f percents)""" ,a__ ,new_head_mask.sum() ,new_head_mask.sum() / new_head_mask.numel() * 100 ,)
  
  	logger.info("""Final head mask"""					)
  	print_ad_tensor(a__					)
  	np.save(os.path.join(args.output_dir ,"""head_mask.npy"""					) ,head_mask.detach().cpu().numpy()					)
  
  	return head_mask
  def        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      List[str] ,a__						:      Dict ,a__						:      Optional[Any]					)				->							Any:
  	__A   : Tuple								= datetime.now()
  	__A   : Dict								= compute_heads_importance(
  	    a__ ,a__ ,a__ ,compute_entropy=a__ ,compute_importance=a__ ,head_mask=a__					)
  	__A   : Dict								= np.argmax(a__ ,axis=1					) if args.output_mode == '''classification''' else np.squeeze(a__					)
  	__A   : Any								= glue_compute_metrics(args.task_name ,a__ ,a__					)[args.metric_name]
  	__A   : Tuple								= datetime.now() - before_time
  
  	__A   : str								= sum(p.numel() for p in model.parameters()					)
  	__A   : List[str]								= {
  	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(a__					)					)
  	}
  
  	assert sum(len(a__					) for h in heads_to_prune.values()					) == (1 - head_mask.long()).sum().item()
  	model.prune_heads(a__					)
  	__A   : str								= sum(p.numel() for p in model.parameters()					)
  
  	__A   : Optional[int]								= datetime.now()
  	__A   : int								= compute_heads_importance(
  	    a__ ,a__ ,a__ ,compute_entropy=a__ ,compute_importance=a__ ,head_mask=a__ ,actually_pruned=a__ ,)
  	__A   : str								= np.argmax(a__ ,axis=1					) if args.output_mode == '''classification''' else np.squeeze(a__					)
  	__A   : int								= glue_compute_metrics(args.task_name ,a__ ,a__					)[args.metric_name]
  	__A   : Union[str, Any]								= datetime.now() - before_time
  
  	logger.info(
  	    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)""" ,a__ ,a__ ,pruned_num_params / original_num_params * 100 ,)
  	logger.info("""Pruning: score with masking: %f score with pruning: %f""" ,a__ ,a__					)
  	logger.info("""Pruning: speed ratio (new timing / original timing): %f percents""" ,original_time / new_time * 100					)
  def        __SCREAMING_SNAKE_CASE    (      )				->							Union[str, Any]:
  	__A   : List[Any]								= argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    """--data_dir""" ,default=a__ ,type=a__ ,required=a__ ,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=a__ ,type=a__ ,required=a__ ,help="""Path to pretrained model or model identifier from huggingface.co/models""" ,)
  	parser.add_argument(
  	    """--task_name""" ,default=a__ ,type=a__ ,required=a__ ,help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()					) ,)
  	parser.add_argument(
  	    """--output_dir""" ,default=a__ ,type=a__ ,required=a__ ,help="""The output directory where the model predictions and checkpoints will be written.""" ,)
  
  	# Other parameters
  	parser.add_argument(
  	    """--config_name""" ,default="""""" ,type=a__ ,help="""Pretrained config name or path if not the same as model_name_or_path""" ,)
  	parser.add_argument(
  	    """--tokenizer_name""" ,default="""""" ,type=a__ ,help="""Pretrained tokenizer name or path if not the same as model_name_or_path""" ,)
  	parser.add_argument(
  	    """--cache_dir""" ,default=a__ ,type=a__ ,help="""Where do you want to store the pre-trained models downloaded from huggingface.co""" ,)
  	parser.add_argument(
  	    """--data_subset""" ,type=a__ ,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=a__ ,help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value).""" ,)
  	parser.add_argument(
  	    """--masking_amount""" ,default=0.1 ,type=a__ ,help="""Amount to heads to masking at each masking step."""					)
  	parser.add_argument("""--metric_name""" ,default="""acc""" ,type=a__ ,help="""Metric to use for head masking."""					)
  
  	parser.add_argument(
  	    """--max_seq_length""" ,default=128 ,type=a__ ,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=a__ ,help="""Batch size."""					)
  
  	parser.add_argument("""--seed""" ,type=a__ ,default=42					)
  	parser.add_argument("""--local_rank""" ,type=a__ ,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=a__ ,default="""""" ,help="""Can be used for distant debugging."""					)
  	parser.add_argument("""--server_port""" ,type=a__ ,default="""""" ,help="""Can be used for distant debugging."""					)
  	__A   : 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=a__					)
  		ptvsd.wait_for_attach()
  
  	# Setup devices and distributed training
  	if args.local_rank == -1 or args.no_cuda:
  		__A   : List[str]								= torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
  		__A   : str								= 0 if args.no_cuda else torch.cuda.device_count()
  	else:
  		torch.cuda.set_device(args.local_rank					)
  		__A   : Dict								= torch.device("""cuda""" ,args.local_rank					)
  		__A   : 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
  	__A   : Union[str, Any]								= args.task_name.lower()
  	if args.task_name not in glue_processors:
  		raise ValueError("""Task not found: %s""" % (args.task_name)					)
  	__A   : str								= glue_processors[args.task_name]()
  	__A   : List[str]								= glue_output_modes[args.task_name]
  	__A   : Any								= processor.get_labels()
  	__A   : Any								= len(a__					)
  
  	# Load pretrained model and tokenizer
  	#
  	# Distributed training:
  	# The .from_pretrained methods guarantee that only one local process can concurrently
  	# download model & vocab.
  
  	__A   : Tuple								= AutoConfig.from_pretrained(
  	    args.config_name if args.config_name else args.model_name_or_path ,num_labels=a__ ,finetuning_task=args.task_name ,output_attentions=a__ ,cache_dir=args.cache_dir ,)
  	__A   : Optional[Any]								= AutoTokenizer.from_pretrained(
  	    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path ,cache_dir=args.cache_dir ,)
  	__A   : Tuple								= AutoModelForSequenceClassification.from_pretrained(
  	    args.model_name_or_path ,from_tf=bool(""".ckpt""" in args.model_name_or_path					) ,config=a__ ,cache_dir=args.cache_dir ,)
  
  	# Distributed and parallel training
  	model.to(args.device					)
  	if args.local_rank != -1:
  		__A   : Dict								= nn.parallel.DistributedDataParallel(
  		    a__ ,device_ids=[args.local_rank] ,output_device=args.local_rank ,find_unused_parameters=a__					)
  	elif args.n_gpu > 1:
  		__A   : List[str]								= nn.DataParallel(a__					)
  
  	# Print/save training arguments
  	os.makedirs(args.output_dir ,exist_ok=a__					)
  	torch.save(a__ ,os.path.join(args.output_dir ,"""run_args.bin"""					)					)
  	logger.info("""Training/evaluation parameters %s""" ,a__					)
  
  	# Prepare dataset for the GLUE task
  	__A   : Tuple								= GlueDataset(a__ ,tokenizer=a__ ,mode="""dev"""					)
  	if args.data_subset > 0:
  		__A   : Union[str, Any]								= Subset(a__ ,list(range(min(args.data_subset ,len(a__					)					)					)					)					)
  	__A   : Optional[int]								= SequentialSampler(a__					) if args.local_rank == -1 else DistributedSampler(a__					)
  	__A   : Tuple								= DataLoader(
  	    a__ ,sampler=a__ ,batch_size=args.batch_size ,collate_fn=a__					)
  
  	# Compute head entropy and importance score
  	compute_heads_importance(a__ ,a__ ,a__					)
  
  	# 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:
  		__A   : int								= mask_heads(a__ ,a__ ,a__					)
  		prune_heads(a__ ,a__ ,a__ ,a__					)
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
  
style_context: 
  
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  
  
  
  
  
  
  class     lowerCamelCase_			(  tf.keras.layers.Layer  ):
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    List				,			__A	:    bool				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    str = "longest"				,			__A	:    bool = True				,			__A	:    int = 512				,			__A	:    int = None				,			__A	:    bool = True				,			__A	:    bool = True				,			__A	:    bool = True				,			**__A	:    Any				,			):
  		super().__init__()
  		if use_fast_bert_tokenizer:
  			__A   : Optional[int]								= FastBertTokenizer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case_nfd_strip_accents=__A				,			**__A       )
  		else:
  			__A   : Optional[Any]								= tf.lookup.StaticVocabularyTable(
  			    tf.lookup.KeyValueTensorInitializer(
  			        keys=__A				,			key_dtype=tf.string				,			values=tf.range(tf.size(__A				,			out_type=tf.intaa       )				,			dtype=tf.intaa       )				,			value_dtype=tf.intaa				,			)				,			num_oov_buckets=1				,			)
  			__A   : Any								= BertTokenizerLayer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case=__A				,			**__A       )
  
  		__A   : Union[str, Any]								= vocab_list
  		__A   : List[str]								= do_lower_case
  		__A   : List[Any]								= cls_token_id or vocab_list.index("""[CLS]"""       )
  		__A   : Tuple								= sep_token_id or vocab_list.index("""[SEP]"""       )
  		__A   : str								= pad_token_id or vocab_list.index("""[PAD]"""       )
  		__A   : Optional[int]								= ShrinkLongestTrimmer(max_length - 3				,			axis=1       )  # Allow room for special tokens
  		__A   : Union[str, Any]								= max_length
  		__A   : List[Any]								= padding
  		__A   : Optional[int]								= truncation
  		__A   : Tuple								= pad_to_multiple_of
  		__A   : Optional[Any]								= return_token_type_ids
  		__A   : int								= return_attention_mask
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[Any]				,			__A	:    "PreTrainedTokenizerBase"				,			**__A	:    List[str]       ):  # noqa: F821
  		__A   : Any								= kwargs.pop("""do_lower_case"""				,			__A       )
  		__A   : Dict								= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  		__A   : List[str]								= kwargs.pop("""cls_token_id"""				,			__A       )
  		__A   : str								= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  		__A   : str								= kwargs.pop("""sep_token_id"""				,			__A       )
  		__A   : Dict								= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  		__A   : Tuple								= kwargs.pop("""pad_token_id"""				,			__A       )
  		__A   : List[Any]								= tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  		__A   : Union[str, Any]								= tokenizer.get_vocab()
  		__A   : Any								= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,			key=lambda __A       : x[1]       )
  		__A   : int								= [entry[0] for entry in vocab]
  		return cls(
  		    vocab_list=__A				,			do_lower_case=__A				,			cls_token_id=__A				,			sep_token_id=__A				,			pad_token_id=__A				,			**__A				,			)
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[str]				,			__A	:    Union[str, os.PathLike]				,			*__A	:    Tuple				,			**__A	:    Optional[int]       ):
  		try:
  			__A   : int								= BertTokenizer.from_pretrained(__A				,			*__A				,			**__A       )
  		except:  # noqa: E722
  			from .tokenization_bert_fast import BertTokenizerFast
  
  			__A   : Optional[int]								= BertTokenizerFast.from_pretrained(__A				,			*__A				,			**__A       )
  		return cls.from_tokenizer(__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Optional[Any]       ):
  		if self.do_lower_case:
  			__A   : Tuple								= case_fold_utfa(__A       )
  		__A   : Optional[int]								= self.tf_tokenizer.tokenize(__A       )
  		return tokens.merge_dims(1				,			-1       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Optional[Any]				,			__A	:    Dict=None				,			__A	:    List[Any]=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=None				,			__A	:    Union[str, Any]=None				,			__A	:    int=None				,			__A	:    Any=None				,			):
  		if padding is None:
  			__A   : List[Any]								= self.padding
  		if padding not in ("longest", "max_length"):
  			raise ValueError("""Padding must be either 'longest' or 'max_length'!"""       )
  		if max_length is not None and text_pair is not None:
  			# Because we have to instantiate a Trimmer to do it properly
  			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""       )
  		if max_length is None:
  			__A   : Any								= self.max_length
  		if truncation is None:
  			__A   : int								= self.truncation
  		if pad_to_multiple_of is None:
  			__A   : List[str]								= self.pad_to_multiple_of
  		if return_token_type_ids is None:
  			__A   : int								= self.return_token_type_ids
  		if return_attention_mask is None:
  			__A   : Any								= self.return_attention_mask
  		if not isinstance(__A				,			tf.Tensor       ):
  			__A   : str								= tf.convert_to_tensor(__A       )
  		if text_pair is not None and not isinstance(__A				,			tf.Tensor       ):
  			__A   : Optional[Any]								= tf.convert_to_tensor(__A       )
  		if text_pair is not None:
  			if text.shape.rank > 1:
  				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""       )
  			if text_pair.shape.rank > 1:
  				raise ValueError("""text_pair should not be multidimensional!"""       )
  		if text.shape.rank == 2:
  			__A						,							__A   : Optional[int]								= text[:, 0], text[:, 1]
  		__A   : List[Any]								= self.unpaired_tokenize(__A       )
  		if text_pair is None:  # Unpaired text
  			if truncation:
  				__A   : Union[str, Any]								= text[:, : max_length - 2]  # Allow room for special tokens
  			__A						,							__A   : List[Any]								= combine_segments(
  			    (text,)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		else:  # Paired text
  			__A   : Tuple								= self.unpaired_tokenize(__A       )
  			if truncation:
  				__A						,							__A   : Optional[int]								= self.paired_trimmer.trim([text, text_pair]       )
  			__A						,							__A   : int								= combine_segments(
  			    (text, text_pair)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		if padding == "longest":
  			__A   : str								= input_ids.bounding_shape(axis=1       )
  			if pad_to_multiple_of is not None:
  				# No ceiling division in tensorflow, so we negate floordiv instead
  				__A   : Optional[Any]								= pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,			__A       ))
  		else:
  			__A   : Dict								= max_length
  
  		__A						,							__A   : str								= pad_model_inputs(__A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  		__A   : List[str]								= {"""input_ids""": input_ids}
  		if return_attention_mask:
  			__A   : Any								= attention_mask
  		if return_token_type_ids:
  			__A						,							__A   : Tuple								= pad_model_inputs(
  			    __A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  			__A   : List[str]								= token_type_ids
  		return output
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return {
  		    "vocab_list": self.vocab_list,
  		    "do_lower_case": self.do_lower_case,
  		    "cls_token_id": self.cls_token_id,
  		    "sep_token_id": self.sep_token_id,
  		    "pad_token_id": self.pad_token_id,
  		}
  
  
  
  
  
  