"""simple docstring"""


import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase       :    List[str]         =		logging.getLogger(__name__)


def   __UpperCAmelCase     (	_snake_case		:       str       ):
   _lowercase							     =       p * torch.log(a_       )
   _lowercase							     =       0
   return -plogp.sum(dim=-1       )


def   __UpperCAmelCase     (	_snake_case		:       Tuple       ):
   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   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       Tuple,				_snake_case		:       Any,				_snake_case		:       Optional[Any]=True,				_snake_case		:       Tuple=True,				_snake_case		:       List[Any]=None,				_snake_case		:       Optional[int]=False       ):
   _lowercase     ,  _lowercase							     =       model.config.num_hidden_layers, model.config.num_attention_heads
   _lowercase							     =       torch.zeros(a_,				a_       ).to(args.device       )
   _lowercase							     =       torch.zeros(a_,				a_       ).to(args.device       )

   if head_mask is None:
      _lowercase							     =       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:
      _lowercase							     =       None

   _lowercase							     =       None
   _lowercase							     =       None
   _lowercase							     =       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():
         _lowercase							     =       v.to(args.device       )

      # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
      _lowercase							     =       model(**a_,				head_mask=a_       )
      _lowercase     ,  _lowercase     ,  _lowercase							     =       (
          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_       ):
            _lowercase							     =       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:
         _lowercase							     =       logits.detach().cpu().numpy()
         _lowercase							     =       inputs["labels"].detach().cpu().numpy()
      else:
         _lowercase							     =       np.append(a_,				logits.detach().cpu().numpy(),				axis=0       )
         _lowercase							     =       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:
      _lowercase							     =       2
      _lowercase							     =       torch.pow(torch.pow(a_,				a_       ).sum(-1       ),				1 / exponent       )
      head_importance /= norm_by_layer.unsqueeze(-1       ) + 1e-20

   if not args.dont_normalize_global_importance:
      _lowercase							     =       (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"       )
   _lowercase							     =       torch.zeros(head_importance.numel(),				dtype=torch.long,				device=args.device       )
   _lowercase							     =       torch.arange(
       head_importance.numel(),				device=args.device       )
   _lowercase							     =       head_ranks.view_as(a_       )
   print_ad_tensor(a_       )

   return attn_entropy, head_importance, preds, labels


def   __UpperCAmelCase     (	_snake_case		:       Optional[int],				_snake_case		:       Tuple,				_snake_case		:       Union[str, Any]       ):
   _lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase							     =       compute_heads_importance(a_,				a_,				a_,				compute_entropy=a_       )
   _lowercase							     =       np.argmax(a_,				axis=1       ) if args.output_mode == "classification" else np.squeeze(a_       )
   _lowercase							     =       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       )

   _lowercase							     =       torch.ones_like(a_       )
   _lowercase							     =       max(1,				int(new_head_mask.numel() * args.masking_amount       )       )

   _lowercase							     =       original_score
   while current_score >= original_score * args.masking_threshold:
      _lowercase							     =       new_head_mask.clone()  # save current head mask
      # heads from least important to most - keep only not-masked heads
      _lowercase							     =       float("Inf"       )
      _lowercase							     =       head_importance.view(-1       ).sort()[1]

      if len(a_       ) <= num_to_mask:
         break

      # mask heads
      _lowercase							     =       current_heads_to_mask[:num_to_mask]
      logger.info("Heads to mask: %s",				str(current_heads_to_mask.tolist()       )       )
      _lowercase							     =       new_head_mask.view(-1       )
      _lowercase							     =       0.0
      _lowercase							     =       new_head_mask.view_as(a_       )
      _lowercase							     =       new_head_mask.clone().detach()
      print_ad_tensor(a_       )

      # Compute metric and head importance again
      _lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase							     =       compute_heads_importance(
          a_,				a_,				a_,				compute_entropy=a_,				head_mask=a_       )
      _lowercase							     =       np.argmax(a_,				axis=1       ) if args.output_mode == "classification" else np.squeeze(a_       )
      _lowercase							     =       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() * 1_0_0,				)

   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   __UpperCAmelCase     (	_snake_case		:       Dict,				_snake_case		:       int,				_snake_case		:       Optional[int],				_snake_case		:       List[Any]       ):
   _lowercase							     =       datetime.now()
   _lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase							     =       compute_heads_importance(
       a_,				a_,				a_,				compute_entropy=a_,				compute_importance=a_,				head_mask=a_       )
   _lowercase							     =       np.argmax(a_,				axis=1       ) if args.output_mode == "classification" else np.squeeze(a_       )
   _lowercase							     =       glue_compute_metrics(args.task_name,				a_,				a_       )[args.metric_name]
   _lowercase							     =       datetime.now() - before_time

   _lowercase							     =       sum(p.numel() for p in model.parameters()       )
   _lowercase							     =       {
       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_       )
   _lowercase							     =       sum(p.numel() for p in model.parameters()       )

   _lowercase							     =       datetime.now()
   _lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase							     =       compute_heads_importance(
       a_,				a_,				a_,				compute_entropy=a_,				compute_importance=a_,				head_mask=a_,				actually_pruned=a_,				)
   _lowercase							     =       np.argmax(a_,				axis=1       ) if args.output_mode == "classification" else np.squeeze(a_       )
   _lowercase							     =       glue_compute_metrics(args.task_name,				a_,				a_       )[args.metric_name]
   _lowercase							     =       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 * 1_0_0,				)
   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 * 1_0_0       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       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=1_2_8,				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=4_2       )
   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."       )
   _lowercase							     =       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:
      _lowercase							     =       torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"       )
      _lowercase							     =       0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank       )
      _lowercase							     =       torch.device("cuda",				args.local_rank       )
      _lowercase							     =       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
   _lowercase							     =       args.task_name.lower()
   if args.task_name not in glue_processors:
      raise ValueError("Task not found: %s" % (args.task_name)       )
   _lowercase							     =       glue_processors[args.task_name]()
   _lowercase							     =       glue_output_modes[args.task_name]
   _lowercase							     =       processor.get_labels()
   _lowercase							     =       len(a_       )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   _lowercase							     =       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,				)
   _lowercase							     =       AutoTokenizer.from_pretrained(
       args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,				cache_dir=args.cache_dir,				)
   _lowercase							     =       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:
      _lowercase							     =       nn.parallel.DistributedDataParallel(
          a_,				device_ids=[args.local_rank],				output_device=args.local_rank,				find_unused_parameters=a_       )
   elif args.n_gpu > 1:
      _lowercase							     =       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
   _lowercase							     =       GlueDataset(a_,				tokenizer=a_,				mode="dev"       )
   if args.data_subset > 0:
      _lowercase							     =       Subset(a_,				list(range(min(args.data_subset,				len(a_       )       )       )       )       )
   _lowercase							     =       SequentialSampler(a_       ) if args.local_rank == -1 else DistributedSampler(a_       )
   _lowercase							     =       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:
      _lowercase							     =       mask_heads(a_,				a_,				a_       )
      prune_heads(a_,				a_,				a_,				a_       )


if __name__ == "__main__":
  main()

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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
	if is_torch_available():
		import torch
	if is_tf_available():
		import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__a							  =						logging.get_logger(__name__)

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

__a							  =						{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

__a							  =						{
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}

@lru_cache()
def    UpperCamelCase_ (	)     ->List[Any]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Union[str, Any]:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask"]







	def __init__(      self				:			Optional[int]							,			snake_case__				:			List[Any]							,			snake_case__				:			int							,			snake_case__				:			Tuple="replace"							,			snake_case__				:			List[str]="<|endoftext|>"							,			snake_case__				:			Tuple="<|endoftext|>"							,			snake_case__				:			Union[str, Any]="<|endoftext|>"							,			snake_case__				:			int=None							,			snake_case__				:			Optional[int]=False							,			snake_case__				:			Tuple=False							,			**snake_case__				:			Optional[Any]							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token
			super().__init__(
			    errors=snake_case__							,			unk_token=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			pad_token=snake_case__							,			add_prefix_space=snake_case__							,			add_bos_token=snake_case__							,			**snake_case__							,			)
			A				=add_bos_token

			with open(snake_case__							,			encoding="utf-8" ) as vocab_handle:
					A				=json.load(snake_case__ )
			A				={v: k for k, v in self.encoder.items()}
			A				=errors  # how to handle errors in decoding
			A				=bytes_to_unicode()
			A				={v: k for k, v in self.byte_encoder.items()}
			with open(snake_case__							,			encoding="utf-8" ) as merges_handle:
					A				=merges_handle.read().split("\n" )[1:-1]
			A				=[tuple(merge.split() ) for merge in bpe_merges]
			A				=dict(zip(snake_case__							,			range(len(snake_case__ ) ) ) )
			A				={}
			A				=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			return dict(self.encoder							,			**self.added_tokens_encoder )







	def        _a					(      self				:			str							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=min(snake_case__							,			key=lambda snake_case__ : self.bpe_ranks.get(snake_case__							,			float("inf" ) ) )
					if bigram not in self.bpe_ranks:
							break
					A			,    A				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=j

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Optional[Any]							,			snake_case__				:			Any							,			snake_case__				:			Optional[Any]=None ):
			"""simple docstring"""





			if self.add_bos_token:
					A				=[self.bos_token_id]
			else:
					A				=[]

			A				=bos_token_ids + token_ids_a

			if token_ids_a is None:
					return output

			return output + bos_token_ids + token_ids_a







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".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(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			str							,			snake_case__				:			Dict ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			List[str]							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





			if not os.path.isdir(snake_case__ ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
					return
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

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

			A				=0
			with open(snake_case__							,			"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 snake_case__ : 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				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			str							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[int]=False							,			**snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if is_split_into_words or add_prefix_space:
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			str							,			snake_case__				:			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,			snake_case__				:			bool = False							,			snake_case__				:			bool = None							,			snake_case__				:			Optional[List[str]] = None							,			**snake_case__				:			Union[str, Any]							,			):
			"""simple docstring"""





			A				=super()._decode(
			    token_ids=snake_case__							,			skip_special_tokens=snake_case__							,			clean_up_tokenization_spaces=snake_case__							,			**snake_case__							,			)

			if truncate_before_pattern is not None and len(snake_case__ ) > 0:
					A				=self.truncate(snake_case__							,			snake_case__ )

			return decoded_text







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any]							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			def find_re(snake_case__				:			Dict							,			snake_case__				:			Tuple							,			snake_case__				:			int ):
					A				=pattern.search(snake_case__							,			snake_case__ )
					return m.start() if m else -1

			A				=[re.compile(snake_case__							,			re.MULTILINE ) for pattern in truncate_before_pattern]

			A				=list(re.finditer("^print"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: prints[1].start()]

			A				=list(re.finditer("^def"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: defs[1].start()]

			A				=0

			A				=[
			    pos for pos in [find_re(snake_case__							,			snake_case__							,			snake_case__ ) for terminal in terminals] if pos != -1
			]

			if len(snake_case__ ) > 0:
					return completion[: min(snake_case__ )]
			else:
					return completion




