


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     :					List[Any]       =16
_UpperCAmelCase     :					Dict       =32



def      lowerCAmelCase	(						lowerCAmelCase_	)->	List[Any]:
				return int(x / 2**20	)







class 			snake_case__:







						'''simple docstring'''




						def __enter__(			self							)   ->      int:
										gc.collect()
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
										lowerCAmelCase_	: List[str]      			=			torch.cuda.memory_allocated()
										lowerCAmelCase_	: Tuple      			=			psutil.Process()

										lowerCAmelCase_	: Tuple      			=			self.cpu_mem_used()
										lowerCAmelCase_	: Tuple      			=			True
										lowerCAmelCase_	: List[Any]      			=			threading.Thread(target=self.peak_monitor_func							)
										lowerCAmelCase_	: Optional[Any]      			=			True
										peak_monitor_thread.start()
										return self




						def   lowercase_	(			self							)   ->      int:
										return self.process.memory_info().rss




						def   lowercase_	(			self							)   ->      int:
										lowerCAmelCase_	: Optional[int]      			=			-1

										while True:
														lowerCAmelCase_	: int      			=			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				,  *__lowercase							)   ->      int:
										lowerCAmelCase_	: Union[str, Any]      			=			False

										gc.collect()
										torch.cuda.empty_cache()
										lowerCAmelCase_	: Dict      			=			torch.cuda.memory_allocated()
										lowerCAmelCase_	: Tuple      			=			torch.cuda.max_memory_allocated()
										lowerCAmelCase_	: Optional[Any]      			=			bamb(self.end - self.begin							)
										lowerCAmelCase_	: str      			=			bamb(self.peak - self.begin							)

										lowerCAmelCase_	: Union[str, Any]      			=			self.cpu_mem_used()
										lowerCAmelCase_	: Optional[int]      			=			bamb(self.cpu_end - self.cpu_begin							)
										lowerCAmelCase_	: Optional[Any]      			=			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     :					Union[str, Any]       =mocked_dataloaders  # noqa: F811



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Tuple:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,				lowerCAmelCase_	) == "1":
								lowerCAmelCase_	: List[Any]      			=			2

				# New Code #
				# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
				lowerCAmelCase_	: Optional[int]      			=			FullyShardedDataParallelPlugin(
				    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCAmelCase_	,				ranka_only=lowerCAmelCase_	)	,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase_	,				ranka_only=lowerCAmelCase_	)	,				)

				# Initialize accelerator
				if args.with_tracking:
								lowerCAmelCase_	: List[str]      			=			Accelerator(
								    cpu=args.cpu	,				mixed_precision=args.mixed_precision	,				log_with='''wandb'''	,				project_dir=args.logging_dir	,				fsdp_plugin=lowerCAmelCase_	,				)
				else:
								lowerCAmelCase_	: Dict      			=			Accelerator(fsdp_plugin=lowerCAmelCase_	)
				accelerator.print(accelerator.distributed_type	)

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

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

				lowerCAmelCase_	: str      			=			AutoTokenizer.from_pretrained(args.model_name_or_path	)
				lowerCAmelCase_	: List[str]      			=			load_dataset('''glue'''	,				'''mrpc'''	)
				lowerCAmelCase_	: int      			=			evaluate.load('''glue'''	,				'''mrpc'''	)

				def tokenize_function(lowerCAmelCase_	):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase_	: List[str]      			=			tokenizer(examples['''sentence1''']	,				examples['''sentence2''']	,				truncation=lowerCAmelCase_	,				max_length=lowerCAmelCase_	)
								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():
								lowerCAmelCase_	: str      			=			datasets.map(
								    lowerCAmelCase_	,				batched=lowerCAmelCase_	,				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
				lowerCAmelCase_	: Union[str, Any]      			=			tokenized_datasets.rename_column('''label'''	,				'''labels'''	)

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase_	: List[str]      			=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase_	: Any      			=			batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase_	: Union[str, Any]      			=			MAX_GPU_BATCH_SIZE

				def collate_fn(lowerCAmelCase_	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase_	: Tuple      			=			128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase_	: str      			=			16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase_	: Optional[Any]      			=			8
								else:
												lowerCAmelCase_	: Optional[Any]      			=			None

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

				# Instantiate dataloaders.
				lowerCAmelCase_	: List[str]      			=			DataLoader(
				    tokenized_datasets['''train''']	,				shuffle=lowerCAmelCase_	,				collate_fn=lowerCAmelCase_	,				batch_size=lowerCAmelCase_	)
				lowerCAmelCase_	: str      			=			DataLoader(
				    tokenized_datasets['''validation''']	,				shuffle=lowerCAmelCase_	,				collate_fn=lowerCAmelCase_	,				batch_size=lowerCAmelCase_	)

				set_seed(lowerCAmelCase_	)

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

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

				# 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
				lowerCAmelCase_	: Any      			=			torch.optim.AdamW(params=model.parameters()	,				lr=lowerCAmelCase_	,				weight_decay=2e-4	)

				# Instantiate scheduler
				lowerCAmelCase_	: int      			=			get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_	,				num_warmup_steps=10	,				num_training_steps=(len(lowerCAmelCase_	) * 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.
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: str      			=			accelerator.prepare(
				    lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)

				lowerCAmelCase_	: Any      			=			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	)
												lowerCAmelCase_	: Any      			=			os.path.basename(args.resume_from_checkpoint	)
								else:
												# Get the most recent checkpoint
												lowerCAmelCase_	: str      			=			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
												dirs.sort(key=os.path.getctime	)
												lowerCAmelCase_	: Any      			=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								lowerCAmelCase_	: Any      			=			os.path.splitext(lowerCAmelCase_	)[0]

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

    # Now we train the model
				for epoch in range(lowerCAmelCase_	):
								# New Code #
								# context manager to track the peak memory usage during the training epoch
								with TorchTracemalloc() as tracemalloc:
												model.train()
												if args.with_tracking:
																lowerCAmelCase_	: List[Any]      			=			0
												for step, batch in enumerate(lowerCAmelCase_	):
																# 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	)
																lowerCAmelCase_	: Optional[Any]      			=			model(**lowerCAmelCase_	)
																lowerCAmelCase_	: Union[str, Any]      			=			outputs.loss
																lowerCAmelCase_	: Dict      			=			loss / gradient_accumulation_steps
																# We keep track of the loss at each epoch
																if args.with_tracking:
																				total_loss += loss.detach().float()
																accelerator.backward(lowerCAmelCase_	)
																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(lowerCAmelCase_	,				lowerCAmelCase_	):
																				lowerCAmelCase_	: Union[str, Any]      			=			f"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																								if args.output_dir is not None:
																												lowerCAmelCase_	: Union[str, Any]      			=			os.path.join(args.output_dir	,				lowerCAmelCase_	)
																								accelerator.save_state(lowerCAmelCase_	)
        # 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=lowerCAmelCase_	,				)

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

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

												if checkpointing_steps == "epoch":
																lowerCAmelCase_	: int      			=			f"""epoch_{epoch}"""
																if args.output_dir is not None:
																				lowerCAmelCase_	: List[str]      			=			os.path.join(args.output_dir	,				lowerCAmelCase_	)
																accelerator.save_state(lowerCAmelCase_	)
        # 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=lowerCAmelCase_	,				)

				if args.with_tracking:
								accelerator.end_training()



def      lowerCAmelCase	(						)->	Tuple:
				lowerCAmelCase_	: Union[str, Any]      			=			argparse.ArgumentParser(description='''Simple example of training script.'''	)
				parser.add_argument(
				    '''--mixed_precision'''	,				type=lowerCAmelCase_	,				default=lowerCAmelCase_	,				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=lowerCAmelCase_	,				default=lowerCAmelCase_	,				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=lowerCAmelCase_	,				default=lowerCAmelCase_	,				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=lowerCAmelCase_	,				default='''.'''	,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,				)
				parser.add_argument(
				    '''--logging_dir'''	,				type=lowerCAmelCase_	,				default='''logs'''	,				help='''Location on where to store experiment tracking logs`'''	,				)
				parser.add_argument(
				    '''--model_name_or_path'''	,				type=lowerCAmelCase_	,				help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,				required=lowerCAmelCase_	,				)
				lowerCAmelCase_	: List[str]      			=			parser.parse_args()
				lowerCAmelCase_	: int      			=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCAmelCase_	,				lowerCAmelCase_	)


if __name__ == "__main__":
						main()



from typing import Dict, List, Optional, Tuple

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


_UpperCAmelCase     :					List[str]       =logging.get_logger(__name__)







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							["""input_ids""", """attention_mask"""]




						def __init__(			self				,  __lowercase="[PAD]"				,  __lowercase="[BOS]"				,  __lowercase="[EOS]"				,  __lowercase="[MASK]"				,  __lowercase="[CLS]"				,  __lowercase="[SEP]"				,  __lowercase=2_0_4_8				,  **__lowercase				,  )   ->      None:
										lowerCAmelCase_	: Optional[int]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else pad_token
										lowerCAmelCase_	: str      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else bos_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else eos_token
										lowerCAmelCase_	: List[str]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else mask_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else cls_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else sep_token

										super().__init__(
										    pad_token=__lowercase				,  bos_token=__lowercase				,  eos_token=__lowercase				,  mask_token=__lowercase				,  cls_token=__lowercase				,  sep_token=__lowercase				,  model_max_length=__lowercase				,  **__lowercase				,  )

										lowerCAmelCase_	: Dict      			=			2**8  # utf is 8 bits

										# define special tokens dict
										lowerCAmelCase_	: Dict[str, int]      			=			{
										    self.pad_token: 0,
										    self.bos_token: 1,
										    self.eos_token: 2,
										    self.mask_token: 3,
										    self.cls_token: 4,
										    self.sep_token: 5,
										}
										lowerCAmelCase_	: List[Any]      			=			len(self.special_tokens_encoder							)
										lowerCAmelCase_	: Dict[int, str]      			=			{v: k for k, v in self.special_tokens_encoder.items()}




						def   lowercase_	(			self							)   ->      Dict[str, int]:
										lowerCAmelCase_	: int      			=			self.special_tokens_encoder.copy()
										vocab.update(self.added_tokens_encoder							)
										for i in range(self._utf_vocab_size							):
														lowerCAmelCase_	: int      			=			chr(__lowercase							)
														lowerCAmelCase_	: Optional[Any]      			=			i + len(self.special_tokens_encoder							)
										return vocab




						@property
						def   lowercase_	(			self							)   ->      str:
										return self._utf_vocab_size + self._num_special_tokens




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None				,  __lowercase = False							)   ->      List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__lowercase				,  token_ids_a=__lowercase				,  already_has_special_tokens=__lowercase							)

										# normal case: some special tokens
										if token_ids_a is None:
														return [1] + [0] * len(__lowercase							) + [1]
										return [1] + ([0] * len(__lowercase							)) + [1] + ([0] * len(__lowercase							)) + [1]




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      List[int]:
										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




						def   lowercase_	(			self				,  __lowercase							)   ->      List[str]:
										lowerCAmelCase_	: Optional[int]      			=			[chr(__lowercase							) for i in text.encode('''utf-8'''							)]
										return tokens




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										if token in self.special_tokens_encoder:
														lowerCAmelCase_	: Optional[int]      			=			self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
														lowerCAmelCase_	: Any      			=			self.added_tokens_encoder[token]
										elif len(__lowercase							) != 1:
														lowerCAmelCase_	: Any      			=			self.unk_token_id
										else:
														lowerCAmelCase_	: List[Any]      			=			ord(__lowercase							) + self._num_special_tokens
										return token_id




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										if index in self.special_tokens_decoder:
														lowerCAmelCase_	: List[Any]      			=			self.special_tokens_decoder[index]
										elif index in self.added_tokens_decoder:
														lowerCAmelCase_	: List[str]      			=			self.added_tokens_decoder[index]
										else:
														lowerCAmelCase_	: str      			=			chr(index - self._num_special_tokens							)
										return token




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										lowerCAmelCase_	: Tuple      			=			B''''''
										for token in tokens:
														if token in self.special_tokens_decoder:
																		lowerCAmelCase_	: int      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.added_tokens_decoder:
																		lowerCAmelCase_	: Tuple      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.special_tokens_encoder:
																		lowerCAmelCase_	: Any      			=			token.encode('''utf-8'''							)
														elif token in self.added_tokens_encoder:
																		lowerCAmelCase_	: Tuple      			=			token.encode('''utf-8'''							)
														else:
																		lowerCAmelCase_	: str      			=			bytes([ord(__lowercase							)]							)
														bstring += tok_string
										lowerCAmelCase_	: str      			=			bstring.decode('''utf-8'''				,  errors='''replace'''							)
										return string




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      Tuple[str]:
										return ()