
"""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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


lowerCamelCase__   					=		logging.getLogger(__name__)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase		):
	# save results
	if os.path.exists(_UpperCamelCase		):
		if os.path.exists(os.path.join(_UpperCamelCase       ,  'config.json'		)		) and os.path.isfile(
		    os.path.join(_UpperCamelCase       ,  'config.json'		)		):
			os.remove(os.path.join(_UpperCamelCase       ,  'config.json'		)		)
		if os.path.exists(os.path.join(_UpperCamelCase       ,  'pytorch_model.bin'		)		) and os.path.isfile(
		    os.path.join(_UpperCamelCase       ,  'pytorch_model.bin'		)		):
			os.remove(os.path.join(_UpperCamelCase       ,  'pytorch_model.bin'		)		)
	else:
		os.makedirs(_UpperCamelCase		)
	model.save_pretrained(_UpperCamelCase		)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase=False		):
	__lowerCAmelCase			:						Optional[Any]									=      2
	if unlogit:
		__lowerCAmelCase			:						str									=      torch.pow(_UpperCamelCase       ,  _UpperCamelCase		)
	__lowerCAmelCase			:						Tuple									=      p * torch.log(_UpperCamelCase		)
	__lowerCAmelCase			:						Any									=      0
	return -plogp.sum(dim=-1		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	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     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False		):
	__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      model.config.num_hidden_layers, model.config.num_attention_heads
	__lowerCAmelCase			:						List[Any]									=      torch.zeros(_UpperCamelCase       ,  _UpperCamelCase		).to(args.device		)
	__lowerCAmelCase			:						Any									=      torch.zeros(_UpperCamelCase       ,  _UpperCamelCase		).to(args.device		)

	if head_mask is None:
		__lowerCAmelCase			:						int									=      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:
		__lowerCAmelCase			:						int									=      None

	__lowerCAmelCase			:						Tuple									=      0.0
	__lowerCAmelCase			:						Tuple									=      0.0
	for step, inputs in enumerate(tqdm(_UpperCamelCase       ,  desc='Iteration'       ,  disable=args.local_rank not in [-1, 0]		)		):
		__lowerCAmelCase			:						List[str]									=      tuple(t.to(args.device		) for t in inputs		)
		((__lowerCAmelCase)			,   )			:						Tuple									=      inputs

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		__lowerCAmelCase			:						Union[str, Any]									=      model(_UpperCamelCase       ,  labels=_UpperCamelCase       ,  head_mask=_UpperCamelCase		)
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Optional[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
		total_loss += loss.detach().cpu().numpy()
		if compute_entropy:
			for layer, attn in enumerate(_UpperCamelCase		):
				__lowerCAmelCase			:						int									=      entropy(attn.detach()       ,  _UpperCamelCase		)
				attn_entropy[layer] += masked_entropy.sum(-1		).sum(0		).sum(0		).detach()

		if compute_importance:
			head_importance += head_mask.grad.abs().detach()
		tot_tokens += torch.ones_like(_UpperCamelCase		).float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		__lowerCAmelCase			:						List[str]									=      2
		__lowerCAmelCase			:						Tuple									=      torch.pow(torch.pow(_UpperCamelCase       ,  _UpperCamelCase		).sum(-1		)       ,  1 / exponent		)
		head_importance /= norm_by_layer.unsqueeze(-1		) + 1e-2_0

	if not args.dont_normalize_global_importance:
		__lowerCAmelCase			:						Dict									=      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

	# Print matrices
	if compute_entropy:
		logger.info('Attention entropies'		)
		print_ad_tensor(_UpperCamelCase		)
	if compute_importance:
		logger.info('Head importance scores'		)
		print_ad_tensor(_UpperCamelCase		)
	logger.info('Head ranked by importance scores'		)
	__lowerCAmelCase			:						int									=      torch.zeros(head_importance.numel()       ,  dtype=torch.long       ,  device=args.device		)
	__lowerCAmelCase			:						Dict									=      torch.arange(
	    head_importance.numel()       ,  device=args.device		)
	__lowerCAmelCase			:						Any									=      head_ranks.view_as(_UpperCamelCase		)
	print_ad_tensor(_UpperCamelCase		)
	return attn_entropy, head_importance, total_loss
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						int									=      compute_heads_importance(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  compute_entropy=_UpperCamelCase		)
	__lowerCAmelCase			:						List[str]									=      1 / loss  # instead of downsteam score use the LM loss
	logger.info('Pruning: original score: %f, threshold: %f'       ,  _UpperCamelCase       ,  original_score * args.masking_threshold		)

	__lowerCAmelCase			:						Optional[int]									=      torch.ones_like(_UpperCamelCase		)
	__lowerCAmelCase			:						Any									=      max(1       ,  int(new_head_mask.numel() * args.masking_amount		)		)

	__lowerCAmelCase			:						Tuple									=      original_score
	while current_score >= original_score * args.masking_threshold:
		__lowerCAmelCase			:						Union[str, Any]									=      new_head_mask.clone().detach()  # save current head mask
		# heads from least important to most - keep only not-masked heads
		__lowerCAmelCase			:						Optional[Any]									=      float('Inf'		)
		__lowerCAmelCase			:						Tuple									=      head_importance.view(-1		).sort()[1]

		if len(_UpperCamelCase		) <= num_to_mask:
			print('BREAK BY num_to_mask'		)
			break

		# mask heads
		__lowerCAmelCase			:						List[str]									=      current_heads_to_mask[:num_to_mask]
		logger.info('Heads to mask: %s'       ,  str(current_heads_to_mask.tolist()		)		)
		__lowerCAmelCase			:						Optional[int]									=      new_head_mask.view(-1		)
		__lowerCAmelCase			:						Optional[int]									=      0.0
		__lowerCAmelCase			:						Optional[Any]									=      new_head_mask.view_as(_UpperCamelCase		)
		__lowerCAmelCase			:						Any									=      new_head_mask.clone().detach()
		print_ad_tensor(_UpperCamelCase		)

		# Compute metric and head importance again
		__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      compute_heads_importance(
		    _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  compute_entropy=_UpperCamelCase       ,  head_mask=_UpperCamelCase		)
		__lowerCAmelCase			:						Tuple									=      1 / loss
		logger.info(
		    'Masking: current score: %f, remaining heads %d (%.1f percents)'       ,  _UpperCamelCase       ,  new_head_mask.sum()       ,  new_head_mask.sum() / new_head_mask.numel() * 100       ,  )

	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     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						Union[str, Any]									=      datetime.now()
	__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      compute_heads_importance(
	    _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  compute_entropy=_UpperCamelCase       ,  compute_importance=_UpperCamelCase       ,  head_mask=_UpperCamelCase		)
	__lowerCAmelCase			:						Union[str, Any]									=      1 / loss
	__lowerCAmelCase			:						Dict									=      datetime.now() - before_time

	__lowerCAmelCase			:						str									=      sum(p.numel() for p in model.parameters()		)
	__lowerCAmelCase			:						Tuple									=      {
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_UpperCamelCase		)		)
	}

	for k, v in heads_to_prune.items():
		if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
			__lowerCAmelCase			:						Tuple									=      [
			    v,
			]

	assert sum(len(_UpperCamelCase		) for h in heads_to_prune.values()		) == (1 - head_mask.long()).sum().item()
	model.prune_heads(_UpperCamelCase		)
	__lowerCAmelCase			:						Optional[int]									=      sum(p.numel() for p in model.parameters()		)

	__lowerCAmelCase			:						str									=      datetime.now()
	__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      compute_heads_importance(
	    _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  compute_entropy=_UpperCamelCase       ,  compute_importance=_UpperCamelCase       ,  head_mask=_UpperCamelCase       ,  actually_pruned=_UpperCamelCase       ,  )

	__lowerCAmelCase			:						Optional[Any]									=      1 / loss
	__lowerCAmelCase			:						Optional[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 * 100       ,  )
	logger.info('Pruning: score with masking: %f score with pruning: %f'       ,  _UpperCamelCase       ,  _UpperCamelCase		)
	logger.info('Pruning: speed ratio (original timing / new timing): %f percents'       ,  original_time / new_time * 100		)
	save_model(_UpperCamelCase       ,  args.output_dir		)
def     __lowerCAmelCase				():
	__lowerCAmelCase			:						List[str]									=      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(
	    '--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 s3'       ,  )
	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=128       ,  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.'		)
	__lowerCAmelCase			:						List[str]									=      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:
		__lowerCAmelCase			:						List[Any]									=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'		)
		__lowerCAmelCase			:						Optional[Any]									=      0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank		)
		__lowerCAmelCase			:						Dict									=      torch.device('cuda'       ,  args.local_rank		)
		__lowerCAmelCase			:						Union[str, 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		)		)		)

	__lowerCAmelCase			:						Any									=      GPTaLMHeadModel.from_pretrained(args.model_name_or_path		)

	# Distributed and parallel training
	model.to(args.device		)
	if args.local_rank != -1:
		__lowerCAmelCase			:						int									=      nn.parallel.DistributedDataParallel(
		    _UpperCamelCase       ,  device_ids=[args.local_rank]       ,  output_device=args.local_rank       ,  find_unused_parameters=_UpperCamelCase		)
	elif args.n_gpu > 1:
		__lowerCAmelCase			:						Tuple									=      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
	__lowerCAmelCase			:						List[str]									=      np.concatenate(
	    [
	        np.loadtxt(args.data_dir       ,  dtype=np.intaa		),
	    ]		)
	__lowerCAmelCase			:						List[Any]									=      (torch.from_numpy(_UpperCamelCase		),)
	__lowerCAmelCase			:						Optional[int]									=      TensorDataset(*_UpperCamelCase		)
	__lowerCAmelCase			:						Tuple									=      RandomSampler(_UpperCamelCase		)
	__lowerCAmelCase			:						Optional[Any]									=      DataLoader(_UpperCamelCase       ,  sampler=_UpperCamelCase       ,  batch_size=args.batch_size		)

	# 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:
		__lowerCAmelCase			:						List[str]									=      mask_heads(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		)
		prune_heads(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		)


if __name__ == "__main__":
							main()

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """vocab.txt"""}

__snake_case								=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__snake_case								=  {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					with open(UpperCamelCase_	,	'r'		) as f:
										SCREAMING_SNAKE_CASE__													= f.read().splitlines()
										return [l.strip() for l in lines]




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Tuple				       =VOCAB_FILES_NAMES
	A__    :   Union[str, Any]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Any				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Any				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple="<unk>"       ,   UpperCAmelCase_							:   Optional[Any]="<cls>"       ,   UpperCAmelCase_							:   List[Any]="<pad>"       ,   UpperCAmelCase_							:   Optional[int]="<mask>"       ,   UpperCAmelCase_							:   Optional[int]="<eos>"       ,   **UpperCAmelCase_							:   Optional[int]       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= load_vocab_file(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= dict(enumerate(self.all_tokens							)							)
						SCREAMING_SNAKE_CASE__													= {tok: ind for ind, tok in enumerate(self.all_tokens							)}
						SCREAMING_SNAKE_CASE__													= unk_token
						SCREAMING_SNAKE_CASE__													= cls_token
						SCREAMING_SNAKE_CASE__													= pad_token
						SCREAMING_SNAKE_CASE__													= mask_token
						SCREAMING_SNAKE_CASE__													= eos_token
						SCREAMING_SNAKE_CASE__													= self.all_tokens
						self._create_trie(self.unique_no_split_tokens							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   **UpperCAmelCase_							:   List[Any]							):
						return text.split()
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Optional[Any]=False							):
						return len(self._id_to_token							)
	def      A_				(			self							:   Union[str, Any]							):
						return {token: i for i, token in enumerate(self.all_tokens							)}
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.eos_token_id]  # No sep token in ESM vocabulary
						if token_ids_a is None:
											if self.eos_token_id is None:
																return cls + token_ids_a
											else:
																return cls + token_ids_a + sep
						elif self.eos_token_id is None:
											raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'							)
						return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   List       ,   UpperCAmelCase_							:   Optional[List] = None       ,   UpperCAmelCase_							:   bool = False							):
						if already_has_special_tokens:
											if token_ids_a is not None:
																raise ValueError(
																    'You should not supply a second sequence if the provided sequence of '
																    'ids is already formatted with special tokens for the model.'							)

											return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
						SCREAMING_SNAKE_CASE__													= [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						if token_ids_a is not None:
											mask += [0] * len(UpperCAmelCase_							) + [1]
						return mask
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= os.path.join(UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'							)
						with open(UpperCAmelCase_       ,   'w'							) as f:
											f.write('\n'.join(self.all_tokens							)							)
						return (vocab_file,)
	@property
	def      A_				(			self							:   int							):
						return self.get_vocab_size(with_added_tokens=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[List[str], List[AddedToken]]       ,   UpperCAmelCase_							:   bool = False							):
						return super()._add_tokens(UpperCAmelCase_       ,   special_tokens=UpperCAmelCase_							)





