






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase      =				1_6
__UpperCAmelCase      =				3_2





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

 return int(x / 2**2_0			)
class 	a_:



     """simple docstring"""



     def __enter__(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		psutil.Process()

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		threading.Thread(target=self.peak_monitor_func)
      SCREAMING_SNAKE_CASE				    =		True
      peak_monitor_thread.start()
      return self





     def     __UpperCamelCase						(		self					:	Any)							->	List[str]:

      """simple docstring"""
      return self.process.memory_info().rss





     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		-1

      while True:
       SCREAMING_SNAKE_CASE				    =		max(self.cpu_mem_used()  ,  self.cpu_peak)

       # can't sleep or will not catch the peak right (this comment is here on purpose)
       # time.sleep(0.001) # 1msec

       if not self.peak_monitoring:
        break





     def __exit__(		self					:	int  ,  *lowerCAmelCase__					:	Tuple)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False

      gc.collect()
      torch.cuda.empty_cache()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.max_memory_allocated()
      SCREAMING_SNAKE_CASE				    =		bamb(self.end - self.begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.peak - self.begin)

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_end - self.cpu_begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_peak - self.cpu_begin)
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase      =				mocked_dataloaders  # noqa: F811





def 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

 if os.environ.get('TESTING_MOCKED_DATALOADERS'	,  lowercase_			) == "1":
  SCREAMING_SNAKE_CASE				    =		2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 SCREAMING_SNAKE_CASE				    =		FullyShardedDataParallelPlugin(
     state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  )

 # Initialize accelerator
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		Accelerator(
      cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  log_with='wandb'	,  project_dir=args.logging_dir	,  fsdp_plugin=lowercase_	,  )
 else:
  SCREAMING_SNAKE_CASE				    =		Accelerator(fsdp_plugin=lowercase_			)
 accelerator.print(accelerator.distributed_type			)

 if hasattr(args.checkpointing_steps	,  'isdigit'			):
  if args.checkpointing_steps == "epoch":
   SCREAMING_SNAKE_CASE				    =		args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   SCREAMING_SNAKE_CASE				    =		int(args.checkpointing_steps			)
  else:
   raise ValueError(
       f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
 else:
  SCREAMING_SNAKE_CASE				    =		None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 SCREAMING_SNAKE_CASE				    =		config['lr']
 SCREAMING_SNAKE_CASE				    =		int(config['num_epochs']			)
 SCREAMING_SNAKE_CASE				    =		int(config['seed']			)
 SCREAMING_SNAKE_CASE				    =		int(config['batch_size']			)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		vars(lowercase_			)
  accelerator.init_trackers('fsdp_glue_no_trainer'	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(args.model_name_or_path			)
 SCREAMING_SNAKE_CASE				    =		load_dataset('glue'	,  'mrpc'			)
 SCREAMING_SNAKE_CASE				    =		evaluate.load('glue'	,  'mrpc'			)

 def tokenize_function(lowercase_			):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE				    =		tokenizer(examples['sentence1']	,  examples['sentence2']	,  truncation=lowercase_	,  max_length=lowercase_			)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  SCREAMING_SNAKE_CASE				    =		datasets.map(
      lowercase_	,  batched=lowercase_	,  remove_columns=['idx', 'sentence1', 'sentence2']	,  )

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 SCREAMING_SNAKE_CASE				    =		tokenized_datasets.rename_column('label'	,  'labels'			)

 # If the batch size is too big we use gradient accumulation
 SCREAMING_SNAKE_CASE				    =		1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  SCREAMING_SNAKE_CASE				    =		batch_size // MAX_GPU_BATCH_SIZE
  SCREAMING_SNAKE_CASE				    =		MAX_GPU_BATCH_SIZE

 def collate_fn(lowercase_			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE				    =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   SCREAMING_SNAKE_CASE				    =		1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE				    =		8
  else:
   SCREAMING_SNAKE_CASE				    =		None

  return tokenizer.pad(
      lowercase_	,  padding='longest'	,  max_length=lowercase_	,  pad_to_multiple_of=lowercase_	,  return_tensors='pt'	,  )

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE				    =		DataLoader(
     tokenized_datasets['train']	,  shuffle=lowercase_	,  collate_fn=lowercase_	,  batch_size=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		DataLoader(
     tokenized_datasets['validation']	,  shuffle=lowercase_	,  collate_fn=lowercase_	,  batch_size=lowercase_			)

 set_seed(lowercase_			)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 SCREAMING_SNAKE_CASE				    =		AutoModelForSequenceClassification.from_pretrained(
     args.model_name_or_path	,  return_dict=lowercase_	,  low_cpu_mem_usage=lowercase_			)

 # New Code #
 # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
 SCREAMING_SNAKE_CASE				    =		accelerator.prepare(lowercase_			)
 accelerator.print(lowercase_			)

 # Instantiate optimizer
 # New Code #
 # For FSDP feature, at present it doesn't support multiple parameter groups,
 # so we need to create a single parameter group for the whole model
 SCREAMING_SNAKE_CASE				    =		torch.optim.AdamW(params=model.parameters()	,  lr=lowercase_	,  weight_decay=2e-4			)

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE				    =		get_linear_schedule_with_warmup(
     optimizer=lowercase_	,  num_warmup_steps=1_0	,  num_training_steps=(len(lowercase_			) * num_epochs) // gradient_accumulation_steps	,  )

 # New Code #
 # For FSDP feature, prepare everything except the model as we have already prepared the model
 # before creating the optimizer
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		accelerator.prepare(
     lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
   accelerator.load_state(args.resume_from_checkpoint			)
   SCREAMING_SNAKE_CASE				    =		os.path.basename(args.resume_from_checkpoint			)
  else:
   # Get the most recent checkpoint
   SCREAMING_SNAKE_CASE				    =		[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
   dirs.sort(key=os.path.getctime			)
   SCREAMING_SNAKE_CASE				    =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  SCREAMING_SNAKE_CASE				    =		os.path.splitext(lowercase_			)[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace('epoch_'	,  ''			)			)
   SCREAMING_SNAKE_CASE				    =		None
  else:
   SCREAMING_SNAKE_CASE				    =		int(training_difference.replace('step_'	,  ''			)			)
   num_epochs -= resume_step // len(lowercase_			)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   SCREAMING_SNAKE_CASE				    =		(num_epochs * len(lowercase_			)) - resume_step

    # Now we train the model
 for epoch in range(lowercase_			):
  # New Code #
  # context manager to track the peak memory usage during the training epoch
  with TorchTracemalloc() as tracemalloc:
   model.train()
   if args.with_tracking:
    SCREAMING_SNAKE_CASE				    =		0
   for step, batch in enumerate(lowercase_			):
    # We need to skip steps until we reach the resumed step
    if args.resume_from_checkpoint and epoch == 0:
     if resume_step is not None and step < resume_step:
      pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    SCREAMING_SNAKE_CASE				    =		outputs.loss
    SCREAMING_SNAKE_CASE				    =		loss / gradient_accumulation_steps
    # We keep track of the loss at each epoch
    if args.with_tracking:
     total_loss += loss.detach().float()
    accelerator.backward(lowercase_			)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # accelerator.print(lr_scheduler.get_lr())

    overall_step += 1

    if isinstance(lowercase_	,  lowercase_			):
     SCREAMING_SNAKE_CASE				    =		f'''step_{overall_step}'''
     if overall_step % checkpointing_steps == 0:
      if args.output_dir is not None:
       SCREAMING_SNAKE_CASE				    =		os.path.join(args.output_dir	,  lowercase_			)
      accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the train (max): {}'.format(
          tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   model.eval()
   for step, batch in enumerate(lowercase_			):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    with torch.no_grad():
     SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    SCREAMING_SNAKE_CASE				    =		outputs.logits.argmax(dim=-1			)
    SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		accelerator.gather_for_metrics((predictions, batch['labels'])			)
    metric.add_batch(
        predictions=lowercase_	,  references=lowercase_	,  )

   SCREAMING_SNAKE_CASE				    =		metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:'''	,  lowercase_			)
   if args.with_tracking:
    accelerator.log(
        {
            'accuracy': eval_metric['accuracy'],
            'f1': eval_metric['f1'],
            'train_loss': total_loss.item() / len(lowercase_			),
        }	,  step=lowercase_	,  )

   if checkpointing_steps == "epoch":
    SCREAMING_SNAKE_CASE				    =		f'''epoch_{epoch}'''
    if args.output_dir is not None:
     SCREAMING_SNAKE_CASE				    =		os.path.join(args.output_dir	,  lowercase_			)
    accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

 if args.with_tracking:
  accelerator.end_training()





def 						A_					(						)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		argparse.ArgumentParser(description='Simple example of training script.'			)
 parser.add_argument(
     '--mixed_precision'	,  type=lowercase_	,  default=lowercase_	,  choices=['no', 'fp16', 'bf16', 'fp8']	,  help='Whether to use mixed precision. Choose'
     'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
     'and an Nvidia Ampere GPU.'	,  )
 parser.add_argument('--cpu'	,  action='store_true'	,  help='If passed, will train on the CPU.'			)
 parser.add_argument(
     '--checkpointing_steps'	,  type=lowercase_	,  default=lowercase_	,  help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,  )
 parser.add_argument(
     '--resume_from_checkpoint'	,  type=lowercase_	,  default=lowercase_	,  help='If the training should continue from a checkpoint folder.'	,  )
 parser.add_argument(
     '--with_tracking'	,  action='store_true'	,  help='Whether to load in all available experiment trackers from the environment and use them for logging.'	,  )
 parser.add_argument(
     '--output_dir'	,  type=lowercase_	,  default='.'	,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,  )
 parser.add_argument(
     '--logging_dir'	,  type=lowercase_	,  default='logs'	,  help='Location on where to store experiment tracking logs`'	,  )
 parser.add_argument(
     '--model_name_or_path'	,  type=lowercase_	,  help='Path to pretrained model or model identifier from huggingface.co/models.'	,  required=lowercase_	,  )
 SCREAMING_SNAKE_CASE				    =		parser.parse_args()
 SCREAMING_SNAKE_CASE				    =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
 training_function(lowercase_	,  lowercase_			)


if __name__ == "__main__":
      main()











'''simple docstring'''






import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A :			Optional[Any]							   =      logging.get_logger(__name__)

A :			Optional[Any]							   =      {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A :			Tuple							   =      {
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

A :			Optional[int]							   =      {'Helsinki-NLP/opus-mt-en-de': 512}
A :			Any							   =      {}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_INIT_CONFIGURATION
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     re.compile(">>.+<<"	)  # type: re.Pattern


					def __init__(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[Any]=None							,  __snake_case						:	int=None							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	Optional[int]="<unk>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Tuple="<pad>"							,  __snake_case						:	List[str]=5_12							,  __snake_case						:	Optional[Dict[str, Any]] = None							,  __snake_case						:	Union[str, Any]=False							,  **__snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
											    source_lang=__snake_case							,  target_lang=__snake_case							,  unk_token=__snake_case							,  eos_token=__snake_case							,  pad_token=__snake_case							,  model_max_length=__snake_case							,  sp_model_kwargs=self.sp_model_kwargs							,  target_vocab_file=__snake_case							,  separate_vocabs=__snake_case							,  **__snake_case							,  )
											assert Path(__snake_case							).exists(), f'''cannot find spm source {source_spm}'''

											_snake_case: List[Any]	       =		separate_vocabs
											_snake_case: List[Any]	       =		load_json(__snake_case							)
											if self.unk_token not in self.encoder:
																	raise KeyError('<unk> token must be in vocab'							)
											assert self.pad_token in self.encoder

											if separate_vocabs:
																	_snake_case: List[str]	       =		load_json(__snake_case							)
																	_snake_case: Optional[int]	       =		{v: k for k, v in self.target_encoder.items()}
																	_snake_case: Tuple	       =		[]
											else:
																	_snake_case: Any	       =		{v: k for k, v in self.encoder.items()}
																	_snake_case: list	       =		[k for k in self.encoder if k.startswith('>>'							) and k.endswith('<<'							)]

											_snake_case: List[Any]	       =		source_lang
											_snake_case: List[str]	       =		target_lang
											_snake_case: List[str]	       =		[source_spm, target_spm]

											# load SentencePiece model for pre-processing
											_snake_case: Optional[Any]	       =		load_spm(__snake_case							,  self.sp_model_kwargs							)
											_snake_case: List[str]	       =		load_spm(__snake_case							,  self.sp_model_kwargs							)
											_snake_case: str	       =		self.spm_source
											_snake_case: int	       =		self.encoder

											# Multilingual target side: default to using first supported language code.

											self._setup_normalizer()


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											try:
																	from sacremoses import MosesPunctNormalizer

																	_snake_case: List[Any]	       =		MosesPunctNormalizer(self.source_lang							).normalize
											except (ImportError, FileNotFoundError):
																	warnings.warn('Recommended: pip install sacremoses.'							)
																	_snake_case: List[Any]	       =		lambda __snake_case							: x


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											return self.punc_normalizer(__snake_case							) if x else ""


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											return self.current_encoder.get(__snake_case							,  self.current_encoder[self.unk_token]							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.language_code_re.match(__snake_case							)
											_snake_case: list	       =		[match.group(0							)] if match else []
											return code, self.language_code_re.sub(''							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[Any]	       =		self.remove_language_code(__snake_case							)
											_snake_case: Union[str, Any]	       =		self.current_spm.encode(__snake_case							,  out_type=__snake_case							)
											return code + pieces


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	int							):





											'''simple docstring'''





											return self.decoder.get(__snake_case							,  self.unk_token							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Tuple							,  **__snake_case						:	int							):





											'''simple docstring'''





											return super().batch_decode(__snake_case							,  **__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Any							,  **__snake_case						:	List[str]							):





											'''simple docstring'''





											return super().decode(__snake_case							,  **__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	List[str]							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
											_snake_case: List[Any]	       =		[]
											_snake_case: Any	       =		''
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							out_string += sp_model.decode_pieces(__snake_case							) + token + " "
																							_snake_case: Any	       =		[]
																	else:
																							current_sub_tokens.append(__snake_case							)
											out_string += sp_model.decode_pieces(__snake_case							)
											return out_string.strip()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]=None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.spm_source
											_snake_case: List[str]	       =		self.encoder


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: int	       =		self.spm_target
											if self.separate_vocabs:
																	_snake_case: Union[str, Any]	       =		self.target_encoder


					@property
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											return len(self.encoder							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __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
											_snake_case: str	       =		[]

											if self.separate_vocabs:
																	_snake_case: Optional[int]	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']							,  )
																	_snake_case: Optional[int]	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']							,  )
																	save_json(self.encoder							,  __snake_case							)
																	save_json(self.target_encoder							,  __snake_case							)
																	saved_files.append(__snake_case							)
																	saved_files.append(__snake_case							)
											else:
																	_snake_case: int	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']							)
																	save_json(self.encoder							,  __snake_case							)
																	saved_files.append(__snake_case							)

											for spm_save_filename, spm_orig_path, spm_model in zip(
											    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]							,  self.spm_files							,  [self.spm_source, self.spm_target]							,  ):
																	_snake_case: Any	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + spm_save_filename							)
																	if os.path.abspath(__snake_case							) != os.path.abspath(__snake_case							) and os.path.isfile(__snake_case							):
																							copyfile(__snake_case							,  __snake_case							)
																							saved_files.append(__snake_case							)
																	elif not os.path.isfile(__snake_case							):
																							with open(__snake_case							,  'wb'							) as fi:
																													_snake_case: Any	       =		spm_model.serialized_model_proto()
																													fi.write(__snake_case							)
																							saved_files.append(__snake_case							)

											return tuple(__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											return self.get_src_vocab()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											return dict(self.target_encoder							,  **self.added_tokens_decoder							)


					def __getstate__(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: str	       =		self.__dict__.copy()
											state.update(
											    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}							)
											return state


					def __setstate__(       self						:	str							,  __snake_case						:	Dict							):





											'''simple docstring'''





											_snake_case: str	       =		d

											# for backward compatibility
											if not hasattr(self							,  'sp_model_kwargs'							):
																	_snake_case: List[Any]	       =		{}

											_snake_case       ,    _snake_case: Dict	       =		(load_spm(__snake_case							,  self.sp_model_kwargs							) for f in self.spm_files)
											_snake_case: str	       =		self.spm_source
											self._setup_normalizer()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  *__snake_case						:	Tuple							,  **__snake_case						:	Tuple							):





											'''simple docstring'''





											return 1


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Dict	       =		set(self.all_special_ids							)  # call it once instead of inside list comp
											all_special_ids.remove(self.unk_token_id							)  # <unk> is only sometimes special
											return [1 if x in all_special_ids else 0 for x in seq]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List							,  __snake_case						:	Optional[List] = None							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											if already_has_special_tokens:
																	return self._special_token_mask(__snake_case							)
											elif token_ids_a is None:
																	return self._special_token_mask(__snake_case							) + [1]
											else:
																	return self._special_token_mask(token_ids_a + token_ids_a							) + [1]





def    lowercase_   (		lowercase__					,	lowercase__    ) ->sentencepiece.SentencePieceProcessor:
						_snake_case: Union[str, Any]	       =		sentencepiece.SentencePieceProcessor(**lowercase__    )
						spm.Load(lowercase__    )
						return spm





def    lowercase_   (		lowercase__					,	lowercase__    ) ->None:
						with open(lowercase__					,	'w'    ) as f:
												json.dump(lowercase__					,	lowercase__					,	indent=2    )





def    lowercase_   (		lowercase__    ) ->Union[Dict, List]:
						with open(lowercase__					,	'r'    ) as f:
												return json.load(lowercase__    )





