



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
#
########################################################################


_lowercase				:					str     					=16
_lowercase				:					Union[str, Any]     					=32




def    UpperCAmelCase					(  lowercase__						:					Tuple	):



			'''simple docstring'''





			return int(x / 2**20	)
class lowerCAmelCase_       :







	'''simple docstring'''




	def __enter__(	self       ):



				'''simple docstring'''


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

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

	def 				_A			(	self       ):



				'''simple docstring'''


				return self.process.memory_info().rss

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  -1

				while True:
							a__					=  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   ,       *lowerCamelCase       ):



				'''simple docstring'''


				a__					=  False

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

				a__					=  self.cpu_mem_used()
				a__					=  bamb(self.cpu_end - self.cpu_begin       )
				a__					=  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

	_lowercase				:					Tuple     					=mocked_dataloaders  # noqa: F811




def    UpperCAmelCase					(  lowercase__						:					Dict				,					lowercase__						:					Dict	):



			'''simple docstring'''





			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,					lowercase__	) == "1":
						a__					=  2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			a__					=  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:
						a__					=  Accelerator(
						    cpu=args.cpu				,					mixed_precision=args.mixed_precision				,					log_with="""wandb"""				,					project_dir=args.logging_dir				,					fsdp_plugin=lowercase__				,					)
			else:
						a__					=  Accelerator(fsdp_plugin=lowercase__	)
			accelerator.print(accelerator.distributed_type	)

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

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

			a__					=  AutoTokenizer.from_pretrained(args.model_name_or_path	)
			a__					=  load_dataset("""glue"""				,					"""mrpc"""	)
			a__					=  evaluate.load("""glue"""				,					"""mrpc"""	)

			def tokenize_function(lowercase__						:					str	):
						# max_length=None => use the model max length (it's actually the default)
						a__					=  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():
						a__					=  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
			a__					=  tokenized_datasets.rename_column("""label"""				,					"""labels"""	)

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

			def collate_fn(lowercase__						:					str	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						a__					=  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":
									a__					=  16
						elif accelerator.mixed_precision != "no":
									a__					=  8
						else:
									a__					=  None

						return tokenizer.pad(
						    lowercase__				,					padding="""longest"""				,					max_length=lowercase__				,					pad_to_multiple_of=lowercase__				,					return_tensors="""pt"""				,					)

			# Instantiate dataloaders.
			a__					=  DataLoader(
			    tokenized_datasets["""train"""]				,					shuffle=lowercase__				,					collate_fn=lowercase__				,					batch_size=lowercase__	)
			a__					=  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)
			a__					=  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
			a__					=  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
			a__					=  torch.optim.AdamW(params=model.parameters()				,					lr=lowercase__				,					weight_decay=2e-4	)

			# Instantiate scheduler
			a__					=  get_linear_schedule_with_warmup(
			    optimizer=lowercase__				,					num_warmup_steps=10				,					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.
			a__				,       a__				,       a__				,       a__					=  accelerator.prepare(
			    lowercase__				,					lowercase__				,					lowercase__				,					lowercase__	)

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

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace("""epoch_"""				,					""""""	)	)
									a__					=  None
						else:
									a__					=  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
									a__					=  (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:
												a__					=  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	)
												a__					=  model(**lowercase__	)
												a__					=  outputs.loss
												a__					=  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__	):
															a__					=  f'step_{overall_step}'
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					a__					=  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():
															a__					=  model(**lowercase__	)
												a__					=  outputs.logits.argmax(dim=-1	)
												a__				,       a__					=  accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
												metric.add_batch(
												    predictions=lowercase__				,					references=lowercase__				,					)

									a__					=  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":
												a__					=  f'epoch_{epoch}'
												if args.output_dir is not None:
															a__					=  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    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  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__				,					)
			a__					=  parser.parse_args()
			a__					=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	main()






import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

_lowercase				:					Any     					=logging.get_logger(__name__)

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

_lowercase				:					int     					={
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

_lowercase				:					Tuple     					={
    """microsoft/deberta-base""": 5_12,
    """microsoft/deberta-large""": 5_12,
    """microsoft/deberta-xlarge""": 5_12,
    """microsoft/deberta-base-mnli""": 5_12,
    """microsoft/deberta-large-mnli""": 5_12,
    """microsoft/deberta-xlarge-mnli""": 5_12,
}

_lowercase				:					str     					={
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			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(lowercase__	)
									cs.append(2**8 + n	)
									n += 1
			a__					=  [chr(lowercase__	) for n in cs]
			return dict(zip(lowercase__				,					lowercase__	)	)




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  set()
			a__					=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						a__					=  char
			return pairs





class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     List[str]									=		VOCAB_FILES_NAMES
	A_       :     str									=		PRETRAINED_VOCAB_FILES_MAP
	A_       :     List[str]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_       :     Optional[int]									=		['input_ids', 'attention_mask', 'token_type_ids']

	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase="replace"   ,       lowerCamelCase="[CLS]"   ,       lowerCamelCase="[SEP]"   ,       lowerCamelCase="[SEP]"   ,       lowerCamelCase="[CLS]"   ,       lowerCamelCase="[UNK]"   ,       lowerCamelCase="[PAD]"   ,       lowerCamelCase="[MASK]"   ,       lowerCamelCase=False   ,       lowerCamelCase=False   ,       **lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else bos_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else eos_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else sep_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else cls_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else unk_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else pad_token

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

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

				with open(lowerCamelCase   ,       encoding="""utf-8"""       ) as vocab_handle:
							a__					=  json.load(lowerCamelCase       )
				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(lowerCamelCase   ,       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(lowerCamelCase   ,       range(len(lowerCamelCase       )       )       )       )
				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
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 				_A			(	self       ):



				'''simple docstring'''


				return len(self.encoder       )

	def 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				if token in self.cache:
							return self.cache[token]
				a__					=  tuple(lowerCamelCase       )
				a__					=  get_pairs(lowerCamelCase       )

				if not pairs:
							return token

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

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

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				a__					=  [self.cls_token_id]
				a__					=  [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None   ,       lowerCamelCase = False       ):



				'''simple docstring'''


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

				if token_ids_a is None:
							return [1] + ([0] * len(lowerCamelCase       )) + [1]
				return [1] + ([0] * len(lowerCamelCase       )) + [1] + ([0] * len(lowerCamelCase       )) + [1]

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				a__					=  [self.sep_token_id]
				a__					=  [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       ) * [0] + len(token_ids_a + sep       ) * [1]

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				return self.decoder.get(lowerCamelCase       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


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

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

				a__					=  0
				with open(lowerCamelCase   ,       """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 lowerCamelCase       : 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(lowerCamelCase       ) + """\n"""       )
										index += 1

				return vocab_file, merge_file

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase=False   ,       **lowerCamelCase       ):



				'''simple docstring'''


				a__					=  kwargs.pop("""add_prefix_space"""   ,       self.add_prefix_space       )
				if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase       ) > 0 and not text[0].isspace()):
							a__					=  """ """ + text
				return (text, kwargs)







	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowerCamelCase   ,       add_special_tokens=lowerCamelCase       ) + [self.eos_token_id]       )
				if len(lowerCamelCase       ) > self.model_max_length:
							a__					=  input_ids[-self.model_max_length :]
				return input_ids

