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
  
  
  __A :		Any      							=    logging.getLogger(__name__)
  def 					A_			(		snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							p * torch.log(snake_case_    )
      UpperCamelCase				:   Optional[Any]          =							0
      return -plogp.sum(dim=-1    )
  def 					A_			(		snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      logger.info("""lv, h >\t""" + """\t""".join(f'{x + 1}' for x in range(len(snake_case_    )    )    )    )
      for row in range(len(snake_case_    )    ):
          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 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]						,snake_case_     :       List[Any]=True						,snake_case_     :       int=True						,snake_case_     :       Dict=None						,snake_case_     :       List[str]=False    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase       ,							UpperCamelCase				:   Optional[Any]          =							model.config.num_hidden_layers, model.config.num_attention_heads
      UpperCamelCase				:   Any          =							torch.zeros(snake_case_						,snake_case_    ).to(args.device    )
      UpperCamelCase				:   str          =							torch.zeros(snake_case_						,snake_case_    ).to(args.device    )
  
      if head_mask is None:
          UpperCamelCase				:   List[str]          =							torch.ones(snake_case_						,snake_case_    ).to(args.device    )
  
      head_mask.requires_grad_(requires_grad=snake_case_    )
      # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
      if actually_pruned:
          UpperCamelCase				:   Dict          =							None
  
      UpperCamelCase				:   List[Any]          =							None
      UpperCamelCase				:   Any          =							None
      UpperCamelCase				:   Optional[int]          =							0.0
  
      for step, inputs in enumerate(tqdm(snake_case_						,desc="""Iteration"""						,disable=args.local_rank not in [-1, 0]    )    ):
          for k, v in inputs.items():
              UpperCamelCase				:   List[Any]          =							v.to(args.device    )
  
          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          UpperCamelCase				:   str          =							model(**snake_case_						,head_mask=snake_case_    )
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							(
              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(snake_case_    ):
                  UpperCamelCase				:   Optional[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				:   Union[str, Any]          =							logits.detach().cpu().numpy()
              UpperCamelCase				:   Optional[Any]          =							inputs["""labels"""].detach().cpu().numpy()
          else:
              UpperCamelCase				:   Optional[int]          =							np.append(snake_case_						,logits.detach().cpu().numpy()						,axis=0    )
              UpperCamelCase				:   Union[str, Any]          =							np.append(snake_case_						,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				:   Optional[Any]          =							2
          UpperCamelCase				:   Any          =							torch.pow(torch.pow(snake_case_						,snake_case_    ).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(snake_case_    )
      logger.info("""Head importance scores"""    )
      print_ad_tensor(snake_case_    )
      logger.info("""Head ranked by importance scores"""    )
      UpperCamelCase				:   Any          =							torch.zeros(head_importance.numel()						,dtype=torch.long						,device=args.device    )
      UpperCamelCase				:   List[Any]          =							torch.arange(
          head_importance.numel()						,device=args.device    )
      UpperCamelCase				:   List[str]          =							head_ranks.view_as(snake_case_    )
      print_ad_tensor(snake_case_    )
  
      return attn_entropy, head_importance, preds, labels
  def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       Dict						,snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							compute_heads_importance(snake_case_						,snake_case_						,snake_case_						,compute_entropy=snake_case_    )
      UpperCamelCase				:   str          =							np.argmax(snake_case_						,axis=1    ) if args.output_mode == """classification""" else np.squeeze(snake_case_    )
      UpperCamelCase				:   int          =							glue_compute_metrics(args.task_name						,snake_case_						,snake_case_    )[args.metric_name]
      logger.info("""Pruning: original score: %f, threshold: %f"""						,snake_case_						,original_score * args.masking_threshold    )
  
      UpperCamelCase				:   List[str]          =							torch.ones_like(snake_case_    )
      UpperCamelCase				:   List[Any]          =							max(1						,int(new_head_mask.numel() * args.masking_amount    )    )
  
      UpperCamelCase				:   Any          =							original_score
      while current_score >= original_score * args.masking_threshold:
          UpperCamelCase				:   str          =							new_head_mask.clone()  # save current head mask
          # heads from least important to most - keep only not-masked heads
          UpperCamelCase				:   Union[str, Any]          =							float("""Inf"""    )
          UpperCamelCase				:   Dict          =							head_importance.view(-1    ).sort()[1]
  
          if len(snake_case_    ) <= num_to_mask:
              break
  
          # mask heads
          UpperCamelCase				:   List[Any]          =							current_heads_to_mask[:num_to_mask]
          logger.info("""Heads to mask: %s"""						,str(current_heads_to_mask.tolist()    )    )
          UpperCamelCase				:   str          =							new_head_mask.view(-1    )
          UpperCamelCase				:   int          =							0.0
          UpperCamelCase				:   Union[str, Any]          =							new_head_mask.view_as(snake_case_    )
          UpperCamelCase				:   int          =							new_head_mask.clone().detach()
          print_ad_tensor(snake_case_    )
  
          # Compute metric and head importance again
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							compute_heads_importance(
              snake_case_						,snake_case_						,snake_case_						,compute_entropy=snake_case_						,head_mask=snake_case_    )
          UpperCamelCase				:   List[str]          =							np.argmax(snake_case_						,axis=1    ) if args.output_mode == """classification""" else np.squeeze(snake_case_    )
          UpperCamelCase				:   Optional[int]          =							glue_compute_metrics(args.task_name						,snake_case_						,snake_case_    )[args.metric_name]
          logger.info(
              """Masking: current score: %f, remaining heads %d (%.1f percents)"""						,snake_case_						,new_head_mask.sum()						,new_head_mask.sum() / new_head_mask.numel() * 1_0_0						,)
  
      logger.info("""Final head mask"""    )
      print_ad_tensor(snake_case_    )
      np.save(os.path.join(args.output_dir						,"""head_mask.npy"""    )						,head_mask.detach().cpu().numpy()    )
  
      return head_mask
  def 					A_			(		snake_case_     :       str						,snake_case_     :       Tuple						,snake_case_     :       int						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   str          =							datetime.now()
      UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							compute_heads_importance(
          snake_case_						,snake_case_						,snake_case_						,compute_entropy=snake_case_						,compute_importance=snake_case_						,head_mask=snake_case_    )
      UpperCamelCase				:   Dict          =							np.argmax(snake_case_						,axis=1    ) if args.output_mode == """classification""" else np.squeeze(snake_case_    )
      UpperCamelCase				:   int          =							glue_compute_metrics(args.task_name						,snake_case_						,snake_case_    )[args.metric_name]
      UpperCamelCase				:   Optional[Any]          =							datetime.now() - before_time
  
      UpperCamelCase				:   Optional[int]          =							sum(p.numel() for p in model.parameters()    )
      UpperCamelCase				:   Dict          =							{
          layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(snake_case_    )    )
      }
  
      assert sum(len(snake_case_    ) for h in heads_to_prune.values()    ) == (1 - head_mask.long()).sum().item()
      model.prune_heads(snake_case_    )
      UpperCamelCase				:   List[str]          =							sum(p.numel() for p in model.parameters()    )
  
      UpperCamelCase				:   Optional[int]          =							datetime.now()
      UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Union[str, Any]          =							compute_heads_importance(
          snake_case_						,snake_case_						,snake_case_						,compute_entropy=snake_case_						,compute_importance=snake_case_						,head_mask=snake_case_						,actually_pruned=snake_case_						,)
      UpperCamelCase				:   Any          =							np.argmax(snake_case_						,axis=1    ) if args.output_mode == """classification""" else np.squeeze(snake_case_    )
      UpperCamelCase				:   Union[str, Any]          =							glue_compute_metrics(args.task_name						,snake_case_						,snake_case_    )[args.metric_name]
      UpperCamelCase				:   List[Any]          =							datetime.now() - before_time
  
      logger.info(
          """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""						,snake_case_						,snake_case_						,pruned_num_params / original_num_params * 1_0_0						,)
      logger.info("""Pruning: score with masking: %f score with pruning: %f"""						,snake_case_						,snake_case_    )
      logger.info("""Pruning: speed ratio (new timing / original timing): %f percents"""						,original_time / new_time * 1_0_0    )
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--data_dir"""						,default=snake_case_						,type=snake_case_						,required=snake_case_						,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=snake_case_						,type=snake_case_						,required=snake_case_						,help="""Path to pretrained model or model identifier from huggingface.co/models"""						,)
      parser.add_argument(
          """--task_name"""						,default=snake_case_						,type=snake_case_						,required=snake_case_						,help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()    )						,)
      parser.add_argument(
          """--output_dir"""						,default=snake_case_						,type=snake_case_						,required=snake_case_						,help="""The output directory where the model predictions and checkpoints will be written."""						,)
  
      # Other parameters
      parser.add_argument(
          """--config_name"""						,default=""""""						,type=snake_case_						,help="""Pretrained config name or path if not the same as model_name_or_path"""						,)
      parser.add_argument(
          """--tokenizer_name"""						,default=""""""						,type=snake_case_						,help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""						,)
      parser.add_argument(
          """--cache_dir"""						,default=snake_case_						,type=snake_case_						,help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""						,)
      parser.add_argument(
          """--data_subset"""						,type=snake_case_						,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=snake_case_						,help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""						,)
      parser.add_argument(
          """--masking_amount"""						,default=0.1						,type=snake_case_						,help="""Amount to heads to masking at each masking step."""    )
      parser.add_argument("""--metric_name"""						,default="""acc"""						,type=snake_case_						,help="""Metric to use for head masking."""    )
  
      parser.add_argument(
          """--max_seq_length"""						,default=1_2_8						,type=snake_case_						,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=snake_case_						,help="""Batch size."""    )
  
      parser.add_argument("""--seed"""						,type=snake_case_						,default=4_2    )
      parser.add_argument("""--local_rank"""						,type=snake_case_						,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=snake_case_						,default=""""""						,help="""Can be used for distant debugging."""    )
      parser.add_argument("""--server_port"""						,type=snake_case_						,default=""""""						,help="""Can be used for distant debugging."""    )
      UpperCamelCase				:   Optional[Any]          =							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=snake_case_    )
          ptvsd.wait_for_attach()
  
      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
          UpperCamelCase				:   Dict          =							torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""    )
          UpperCamelCase				:   Any          =							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				:   Dict          =							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				:   Any          =							glue_processors[args.task_name]()
      UpperCamelCase				:   str          =							glue_output_modes[args.task_name]
      UpperCamelCase				:   Optional[int]          =							processor.get_labels()
      UpperCamelCase				:   int          =							len(snake_case_    )
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      UpperCamelCase				:   int          =							AutoConfig.from_pretrained(
          args.config_name if args.config_name else args.model_name_or_path						,num_labels=snake_case_						,finetuning_task=args.task_name						,output_attentions=snake_case_						,cache_dir=args.cache_dir						,)
      UpperCamelCase				:   Optional[int]          =							AutoTokenizer.from_pretrained(
          args.tokenizer_name if args.tokenizer_name else args.model_name_or_path						,cache_dir=args.cache_dir						,)
      UpperCamelCase				:   Optional[Any]          =							AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path						,from_tf=bool(""".ckpt""" in args.model_name_or_path    )						,config=snake_case_						,cache_dir=args.cache_dir						,)
  
      # Distributed and parallel training
      model.to(args.device    )
      if args.local_rank != -1:
          UpperCamelCase				:   List[str]          =							nn.parallel.DistributedDataParallel(
              snake_case_						,device_ids=[args.local_rank]						,output_device=args.local_rank						,find_unused_parameters=snake_case_    )
      elif args.n_gpu > 1:
          UpperCamelCase				:   Dict          =							nn.DataParallel(snake_case_    )
  
      # Print/save training arguments
      os.makedirs(args.output_dir						,exist_ok=snake_case_    )
      torch.save(snake_case_						,os.path.join(args.output_dir						,"""run_args.bin"""    )    )
      logger.info("""Training/evaluation parameters %s"""						,snake_case_    )
  
      # Prepare dataset for the GLUE task
      UpperCamelCase				:   Optional[Any]          =							GlueDataset(snake_case_						,tokenizer=snake_case_						,mode="""dev"""    )
      if args.data_subset > 0:
          UpperCamelCase				:   Optional[Any]          =							Subset(snake_case_						,list(range(min(args.data_subset						,len(snake_case_    )    )    )    )    )
      UpperCamelCase				:   Tuple          =							SequentialSampler(snake_case_    ) if args.local_rank == -1 else DistributedSampler(snake_case_    )
      UpperCamelCase				:   str          =							DataLoader(
          snake_case_						,sampler=snake_case_						,batch_size=args.batch_size						,collate_fn=snake_case_    )
  
      # Compute head entropy and importance score
      compute_heads_importance(snake_case_						,snake_case_						,snake_case_    )
  
      # 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				:   Optional[Any]          =							mask_heads(snake_case_						,snake_case_						,snake_case_    )
          prune_heads(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
  
  
  if __name__ == "__main__":
    main()
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
  else:
    __A :		Tuple      							=    None
  
  
  __A :		Optional[Any]      							=    logging.get_logger(__name__)
  
  __A :		Optional[Any]      							=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __A :		Optional[Any]      							=    {
      '''vocab_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
          ),
      },
      '''tokenizer_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
          ),
      },
  }
  
  __A :		List[Any]      							=    {
      '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
  }
  
  # fmt: off
  __A :		Dict      							=    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				VOCAB_FILES_NAMES
     lowercase       :  Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  List[Any]      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  Tuple      =				['input_ids', 'attention_mask']
     lowercase       :  Optional[int]      =				MBartaaTokenizer
  
     lowercase       :  List[int]      =				[]
     lowercase       :  List[int]      =				[]
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="<s>"    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="<mask>"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase				:   Optional[int]          =							AddedToken(SCREAMING_SNAKE_CASE_    ,      lstrip=SCREAMING_SNAKE_CASE_    ,      rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) else mask_token
  
         UpperCamelCase				:   Dict          =							kwargs.get("""additional_special_tokens"""    ,      [] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]
  
         super().__init__(
             SCREAMING_SNAKE_CASE_    ,      src_lang=SCREAMING_SNAKE_CASE_    ,      tgt_lang=SCREAMING_SNAKE_CASE_    ,      tokenizer_file=SCREAMING_SNAKE_CASE_    ,      eos_token=SCREAMING_SNAKE_CASE_    ,      sep_token=SCREAMING_SNAKE_CASE_    ,      cls_token=SCREAMING_SNAKE_CASE_    ,      unk_token=SCREAMING_SNAKE_CASE_    ,      pad_token=SCREAMING_SNAKE_CASE_    ,      mask_token=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   int          =							vocab_file
         UpperCamelCase				:   Optional[Any]          =							False if not self.vocab_file else True
  
         UpperCamelCase				:   List[Any]          =							{
             lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }
  
         UpperCamelCase				:   List[str]          =							src_lang if src_lang is not None else """en_XX"""
         UpperCamelCase				:   Union[str, Any]          =							tgt_lang
         UpperCamelCase				:   Union[str, Any]          =							self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._src_lang
  
  
  
  
  
  
  
     @src_lang.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "en_XX"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "ro_RO"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   str          =							src_lang
         UpperCamelCase				:   Optional[Any]          =							tgt_lang
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_src_lang_special_tokens(self.src_lang )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[Any]          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Optional[int]          =							[self.eos_token_id]
  
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Any          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Any          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Tuple          =							[self.eos_token_id]
  
         UpperCamelCase				:   str          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Dict          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCamelCase				:   Optional[int]          =							src_lang
         UpperCamelCase				:   Optional[int]          =							self(SCREAMING_SNAKE_CASE_    ,      add_special_tokens=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							tgt_lang_id
         return inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer.""" )
  
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   Optional[int]          =							os.path.join(
             SCREAMING_SNAKE_CASE_    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
             copyfile(self.vocab_file    ,      SCREAMING_SNAKE_CASE_ )
  
         return (out_vocab_file,)
  
  
  
  
  