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


_UpperCAmelCase            =   logging.getLogger(__name__)




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Dict     ):
		'''simple docstring'''




		if os.path.exists(__lowercase     ):
				if os.path.exists(os.path.join(__lowercase						,'config.json'     )     ) and os.path.isfile(
				    os.path.join(__lowercase						,'config.json'     )     ):
						os.remove(os.path.join(__lowercase						,'config.json'     )     )
				if os.path.exists(os.path.join(__lowercase						,'pytorch_model.bin'     )     ) and os.path.isfile(
				    os.path.join(__lowercase						,'pytorch_model.bin'     )     ):
						os.remove(os.path.join(__lowercase						,'pytorch_model.bin'     )     )
		else:
				os.makedirs(__lowercase     )
		model.save_pretrained(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			Union[str, Any]=False     ):
		'''simple docstring'''




		A_		:    List[str]         =				2
		if unlogit:
				A_		:    List[Any]         =				torch.pow(__lowercase						,__lowercase     )
		A_		:    Dict         =				p * torch.log(__lowercase     )
		A_		:    Any         =				0
		return -plogp.sum(dim=-1     )




def     UpperCamelCase						(	__lowercase						:			str     ):
		'''simple docstring'''




		logger.info('lv, h >\t' + '\t'.join(f'''{x + 1}''' for x in range(len(__lowercase     )     )     )     )
		for row in range(len(__lowercase     )     ):
				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     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			Optional[Any]						,__lowercase						:			Tuple						,__lowercase						:			Union[str, Any]=True						,__lowercase						:			List[str]=True						,__lowercase						:			Union[str, Any]=None						,__lowercase						:			int=False     ):
		'''simple docstring'''




		A_		:    Optional[int]         =				model.config.num_hidden_layers, model.config.num_attention_heads
		A_		:    List[str]         =				torch.zeros(__lowercase						,__lowercase     ).to(args.device     )
		A_		:    Union[str, Any]         =				torch.zeros(__lowercase						,__lowercase     ).to(args.device     )

		if head_mask is None:
				A_		:    str         =				torch.ones(__lowercase						,__lowercase     ).to(args.device     )

		head_mask.requires_grad_(requires_grad=__lowercase     )
		# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
		if actually_pruned:
				A_		:    Dict         =				None

		A_		:    Optional[int]         =				0.0
		A_		:    List[str]         =				0.0
		for step, inputs in enumerate(tqdm(__lowercase						,desc='Iteration'						,disable=args.local_rank not in [-1, 0]     )     ):
				A_		:    List[str]         =				tuple(t.to(args.device     ) for t in inputs     )
				(A_      )		:    Dict         =				inputs

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				A_		:    Dict         =				model(__lowercase						,labels=__lowercase						,head_mask=__lowercase     )
				#  (loss), lm_logits, presents, (all hidden_states), (attentions)
				A_		:    int         =				(
				    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(__lowercase     ):
								A_		:    Tuple         =				entropy(attn.detach()						,__lowercase     )
								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(__lowercase     ).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_		:    int         =				2
				A_		:    Dict         =				torch.pow(torch.pow(__lowercase						,__lowercase     ).sum(-1     )						,1 / exponent     )
				head_importance /= norm_by_layer.unsqueeze(-1     ) + 1e-2_0

		if not args.dont_normalize_global_importance:
				A_		:    Optional[int]         =				(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

		# Print matrices
		if compute_entropy:
				logger.info('Attention entropies'     )
				print_ad_tensor(__lowercase     )
		if compute_importance:
				logger.info('Head importance scores'     )
				print_ad_tensor(__lowercase     )
		logger.info('Head ranked by importance scores'     )
		A_		:    List[str]         =				torch.zeros(head_importance.numel()						,dtype=torch.long						,device=args.device     )
		A_		:    List[str]         =				torch.arange(
		    head_importance.numel()						,device=args.device     )
		A_		:    Union[str, Any]         =				head_ranks.view_as(__lowercase     )
		print_ad_tensor(__lowercase     )
		return attn_entropy, head_importance, total_loss




def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Tuple						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		A_		:    Dict         =				compute_heads_importance(__lowercase						,__lowercase						,__lowercase						,compute_entropy=__lowercase     )
		A_		:    Union[str, Any]         =				1 / loss  # instead of downsteam score use the LM loss
		logger.info('Pruning: original score: %f, threshold: %f'						,__lowercase						,original_score * args.masking_threshold     )

		A_		:    Tuple         =				torch.ones_like(__lowercase     )
		A_		:    str         =				max(1						,int(new_head_mask.numel() * args.masking_amount     )     )

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

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

				# mask heads
				A_		:    Any         =				current_heads_to_mask[:num_to_mask]
				logger.info('Heads to mask: %s'						,str(current_heads_to_mask.tolist()     )     )
				A_		:    Union[str, Any]         =				new_head_mask.view(-1     )
				A_		:    Tuple         =				0.0
				A_		:    Dict         =				new_head_mask.view_as(__lowercase     )
				A_		:    List[str]         =				new_head_mask.clone().detach()
				print_ad_tensor(__lowercase     )

				# Compute metric and head importance again
				A_		:    int         =				compute_heads_importance(
				    __lowercase						,__lowercase						,__lowercase						,compute_entropy=__lowercase						,head_mask=__lowercase     )
				A_		:    List[str]         =				1 / loss
				logger.info(
				    'Masking: current score: %f, remaining heads %d (%.1f percents)'						,__lowercase						,new_head_mask.sum()						,new_head_mask.sum() / new_head_mask.numel() * 1_00						,)

		logger.info('Final head mask'     )
		print_ad_tensor(__lowercase     )
		np.save(os.path.join(args.output_dir						,'head_mask.npy'     )						,head_mask.detach().cpu().numpy()     )

		return head_mask




def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			int						,__lowercase						:			Optional[Any]						,__lowercase						:			Tuple     ):
		'''simple docstring'''




		A_		:    Dict         =				datetime.now()
		A_		:    Tuple         =				compute_heads_importance(
		    __lowercase						,__lowercase						,__lowercase						,compute_entropy=__lowercase						,compute_importance=__lowercase						,head_mask=__lowercase     )
		A_		:    int         =				1 / loss
		A_		:    List[str]         =				datetime.now() - before_time

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

		for k, v in heads_to_prune.items():
				if isinstance(__lowercase						,__lowercase     ):
						A_		:    List[str]         =				[
						    v,
						]

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

		A_		:    Optional[int]         =				datetime.now()
		A_		:    int         =				compute_heads_importance(
		    __lowercase						,__lowercase						,__lowercase						,compute_entropy=__lowercase						,compute_importance=__lowercase						,head_mask=__lowercase						,actually_pruned=__lowercase						,)

		A_		:    Dict         =				1 / loss
		A_		:    str         =				datetime.now() - before_time

		logger.info(
		    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'						,__lowercase						,__lowercase						,pruned_num_params / original_num_params * 1_00						,)
		logger.info('Pruning: score with masking: %f score with pruning: %f'						,__lowercase						,__lowercase     )
		logger.info('Pruning: speed ratio (original timing / new timing): %f percents'						,original_time / new_time * 1_00     )
		save_model(__lowercase						,args.output_dir     )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Dict         =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--data_dir'						,default=__lowercase						,type=__lowercase						,required=__lowercase						,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=__lowercase						,type=__lowercase						,required=__lowercase						,help='Path to pretrained model or model identifier from huggingface.co/models'						,)
		parser.add_argument(
		    '--output_dir'						,default=__lowercase						,type=__lowercase						,required=__lowercase						,help='The output directory where the model predictions and checkpoints will be written.'						,)

		# Other parameters
		parser.add_argument(
		    '--config_name'						,default=''						,type=__lowercase						,help='Pretrained config name or path if not the same as model_name_or_path'						,)
		parser.add_argument(
		    '--tokenizer_name'						,default=''						,type=__lowercase						,help='Pretrained tokenizer name or path if not the same as model_name_or_path'						,)
		parser.add_argument(
		    '--cache_dir'						,default=__lowercase						,type=__lowercase						,help='Where do you want to store the pre-trained models downloaded from s3'						,)
		parser.add_argument(
		    '--data_subset'						,type=__lowercase						,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=__lowercase						,help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'						,)
		parser.add_argument(
		    '--masking_amount'						,default=0.1						,type=__lowercase						,help='Amount to heads to masking at each masking step.'     )
		parser.add_argument('--metric_name'						,default='acc'						,type=__lowercase						,help='Metric to use for head masking.'     )

		parser.add_argument(
		    '--max_seq_length'						,default=1_28						,type=__lowercase						,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=__lowercase						,help='Batch size.'     )

		parser.add_argument('--seed'						,type=__lowercase						,default=42     )
		parser.add_argument('--local_rank'						,type=__lowercase						,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=__lowercase						,default=''						,help='Can be used for distant debugging.'     )
		parser.add_argument('--server_port'						,type=__lowercase						,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=__lowercase     )
				ptvsd.wait_for_attach()

		# Setup devices and distributed training
		if args.local_rank == -1 or args.no_cuda:
				A_		:    Optional[Any]         =				torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'     )
				A_		:    List[str]         =				0 if args.no_cuda else torch.cuda.device_count()
		else:
				torch.cuda.set_device(args.local_rank     )
				A_		:    List[str]         =				torch.device('cuda'						,args.local_rank     )
				A_		:    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     )     )     )

		A_		:    Union[str, Any]         =				GPTaLMHeadModel.from_pretrained(args.model_name_or_path     )

		# Distributed and parallel training
		model.to(args.device     )
		if args.local_rank != -1:
				A_		:    Optional[Any]         =				nn.parallel.DistributedDataParallel(
				    __lowercase						,device_ids=[args.local_rank]						,output_device=args.local_rank						,find_unused_parameters=__lowercase     )
		elif args.n_gpu > 1:
				A_		:    List[str]         =				nn.DataParallel(__lowercase     )

		# Print/save training arguments
		os.makedirs(args.output_dir						,exist_ok=__lowercase     )
		torch.save(__lowercase						,os.path.join(args.output_dir						,'run_args.bin'     )     )
		logger.info('Training/evaluation parameters %s'						,__lowercase     )

		# Prepare dataset
		A_		:    Tuple         =				np.concatenate(
		    [
		        np.loadtxt(args.data_dir						,dtype=np.intaa     ),
		    ]     )
		A_		:    Tuple         =				(torch.from_numpy(__lowercase     ),)
		A_		:    Any         =				TensorDataset(*__lowercase     )
		A_		:    Optional[Any]         =				RandomSampler(__lowercase     )
		A_		:    int         =				DataLoader(__lowercase						,sampler=__lowercase						,batch_size=args.batch_size     )

		# Compute head entropy and importance score
		compute_heads_importance(__lowercase						,__lowercase						,__lowercase     )

		# 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_		:    Optional[int]         =				mask_heads(__lowercase						,__lowercase						,__lowercase     )
				prune_heads(__lowercase						,__lowercase						,__lowercase						,__lowercase     )


if __name__ == "__main__":
	main()







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_UpperCAmelCase            =		logging.get_logger(__name__)


_UpperCAmelCase            =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

_UpperCAmelCase            =		{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				(
		    list(range(ord('!'     )						,ord('~'     ) + 1     )     ) + list(range(ord('¡'     )						,ord('¬'     ) + 1     )     ) + list(range(ord('®'     )						,ord('ÿ'     ) + 1     )     )
		)
		A_		:    Any         =				bs[:]
		A_		:    Optional[Any]         =				0
		for b in range(2**8     ):
				if b not in bs:
						bs.append(__lowercase     )
						cs.append(2**8 + n     )
						n += 1
		A_		:    str         =				[chr(__lowercase     ) for n in cs]
		return dict(zip(__lowercase						,__lowercase     )     )




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    List[str]         =				set()
		A_		:    List[Any]         =				word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)     )
				A_		:    Union[str, Any]         =				char
		return pairs





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				def __init__(						self		,     lowercase		,     lowercase		,     lowercase="replace"		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=False		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else bos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else eos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else sep_token
						A_		:    Tuple         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else cls_token
						A_		:    List[str]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else unk_token
						A_		:    Optional[int]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						A_		:    int         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

						super().__init__(
						    errors=lowercase		,     bos_token=lowercase		,     eos_token=lowercase		,     unk_token=lowercase		,     sep_token=lowercase		,     cls_token=lowercase		,     pad_token=lowercase		,     mask_token=lowercase		,     add_prefix_space=lowercase		,     **lowercase		,     )

						with open(lowercase		,     encoding='utf-8' ) as vocab_handle:
								A_		:    int         =				json.load(lowercase )
						A_		:    Union[str, Any]         =				{v: k for k, v in self.encoder.items()}
						A_		:    Union[str, Any]         =				errors  # how to handle errors in decoding
						A_		:    Tuple         =				bytes_to_unicode()
						A_		:    str         =				{v: k for k, v in self.byte_encoder.items()}
						with open(lowercase		,     encoding='utf-8' ) as merges_handle:
								A_		:    str         =				merges_handle.read().split('\n' )[1:-1]
						A_		:    Optional[Any]         =				[tuple(merge.split() ) for merge in bpe_merges]
						A_		:    Any         =				dict(zip(lowercase		,     range(len(lowercase ) ) ) )
						A_		:    str         =				{}
						A_		:    List[Any]         =				add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						A_		:    Optional[Any]         =				re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return len(self.encoder )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return dict(self.encoder		,     **self.added_tokens_encoder )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.cache:
								return self.cache[token]
						A_		:    List[Any]         =				tuple(lowercase )
						A_		:    List[Any]         =				get_pairs(lowercase )

						if not pairs:
								return token

						while True:
								A_		:    List[str]         =				min(lowercase		,     key=lambda lowercase : self.bpe_ranks.get(lowercase		,     float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								A_       ,      A_		:    Optional[Any]         =				bigram
								A_		:    List[Any]         =				[]
								A_		:    Tuple         =				0
								while i < len(lowercase ):
										try:
												A_		:    Optional[Any]         =				word.index(lowercase		,     lowercase )
										except ValueError:
												new_word.extend(word[i:] )
												break
										else:
												new_word.extend(word[i:j] )
												A_		:    int         =				j

										if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								A_		:    Union[str, Any]         =				tuple(lowercase )
								A_		:    Tuple         =				new_word
								if len(lowercase ) == 1:
										break
								else:
										A_		:    Optional[int]         =				get_pairs(lowercase )
						A_		:    Optional[Any]         =				' '.join(lowercase )
						A_		:    str         =				word
						return word
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						for token in re.findall(self.pat		,     lowercase ):
								A_		:    Optional[Any]         =				''.join(
								    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase ).split(' ' ) )
						return bpe_tokens
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.encoder.get(lowercase		,     self.encoder.get(self.unk_token ) )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.decoder.get(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				''.join(lowercase )
						A_		:    List[Any]         =				bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'		,     errors=self.errors )
						return text
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if not os.path.isdir(lowercase ):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
								return
						A_		:    Optional[int]         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						A_		:    Dict         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

						with open(lowercase		,     'w'		,     encoding='utf-8' ) as f:
								f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=lowercase		,     ensure_ascii=lowercase ) + '\n' )

						A_		:    Dict         =				0
						with open(lowercase		,     'w'		,     encoding='utf-8' ) as writer:
								writer.write('#version: 0.2\n' )
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda lowercase : kv[1] ):
										if index != token_index:
												logger.warning(
												    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
												    ' Please check that the tokenizer is not corrupted!' )
												A_		:    int         =				token_index
										writer.write(' '.join(lowercase ) + '\n' )
										index += 1

						return vocab_file, merge_file
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A_		:    Tuple         =				[self.cls_token_id]
						A_		:    Any         =				[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase		,     token_ids_a=lowercase		,     already_has_special_tokens=lowercase )

						if token_ids_a is None:
								return [1] + ([0] * len(lowercase )) + [1]
						return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[self.sep_token_id]
						A_		:    List[Any]         =				[self.cls_token_id]

						if token_ids_a is None:
								return len(cls + token_ids_a + sep ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=False		,     **lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				kwargs.pop('add_prefix_space'		,     self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(lowercase ) > 0 and not text[0].isspace()):
								A_		:    int         =				' ' + text
						return (text, kwargs)






