import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowerCAmelCase__      		=					16
lowerCAmelCase__      		=					32






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__ = 1_6      ):
       lowerCAmelCase__	      =   AutoTokenizer.from_pretrained('bert-base-cased'      )
       lowerCAmelCase__	      =   load_dataset('glue'      ,    'mrpc'      )

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

       def collate_fn(lowerCAmelCase__      ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowerCAmelCase__	      =   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":
                     lowerCAmelCase__	      =   1_6
              elif accelerator.mixed_precision != "no":
                     lowerCAmelCase__	      =   8
              else:
                     lowerCAmelCase__	      =   None

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

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

       return train_dataloader, eval_dataloader


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

    lowerCAmelCase__      		=					mocked_dataloaders  # noqa: F811






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):





       if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,    lowercase__      ) == "1":
              lowerCAmelCase__	      =   2
       # Initialize accelerator
       lowerCAmelCase__	      =   Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision      )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCAmelCase__	      =   config['lr']
       lowerCAmelCase__	      =   int(config['num_epochs']      )
       lowerCAmelCase__	      =   int(config['seed']      )
       lowerCAmelCase__	      =   int(config['batch_size']      )

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps      ,    'isdigit'      ):
              if args.checkpointing_steps == "epoch":
                     lowerCAmelCase__	      =   args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     lowerCAmelCase__	      =   int(args.checkpointing_steps      )
              else:
                     raise ValueError(
                         F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
       else:
              lowerCAmelCase__	      =   None

       set_seed(lowercase__      )

       lowerCAmelCase__      ,   lowerCAmelCase__	      =   get_dataloaders(lowercase__      ,    lowercase__      )
       lowerCAmelCase__	      =   evaluate.load('glue'      ,    'mrpc'      )

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowerCAmelCase__	      =   AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,    return_dict=lowercase__      )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       lowerCAmelCase__	      =   model.to(accelerator.device      )

       # Instantiate optimizer
       lowerCAmelCase__	      =   AdamW(params=model.parameters()      ,    lr=lowercase__      )

       # Instantiate scheduler
       lowerCAmelCase__	      =   get_linear_schedule_with_warmup(
           optimizer=lowercase__      ,    num_warmup_steps=1_0_0      ,    num_training_steps=(len(lowercase__      ) * num_epochs) // gradient_accumulation_steps      ,    )

       # Prepare everything
       # 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__      ,   lowerCAmelCase__	      =   accelerator.prepare(
           lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      )

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       lowerCAmelCase__	      =   0
       # We also need to keep track of the stating epoch so files are named properly
       lowerCAmelCase__	      =   0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       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__	      =   os.path.basename(args.resume_from_checkpoint      )
              else:
                     # Get the most recent checkpoint
                     lowerCAmelCase__	      =   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime      )
                     lowerCAmelCase__	      =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              lowerCAmelCase__	      =   os.path.splitext(lowercase__      )[0]

              if "epoch" in training_difference:
                     lowerCAmelCase__	      =   int(training_difference.replace('epoch_'      ,    ''      )      ) + 1
                     lowerCAmelCase__	      =   None
              else:
                     lowerCAmelCase__	      =   int(training_difference.replace('step_'      ,    ''      )      )
                     lowerCAmelCase__	      =   resume_step // len(lowercase__      )
                     resume_step -= starting_epoch * len(lowercase__      )

    # Now we train the model
       for epoch in range(lowercase__      ,    lowercase__      ):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     lowerCAmelCase__	      =   accelerator.skip_first_batches(lowercase__      ,    lowercase__      )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     lowerCAmelCase__	      =   train_dataloader
              for step, batch in enumerate(lowercase__      ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device      )
                     lowerCAmelCase__	      =   model(**lowercase__      )
                     lowerCAmelCase__	      =   outputs.loss
                     lowerCAmelCase__	      =   loss / gradient_accumulation_steps
                     accelerator.backward(lowercase__      )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(lowercase__      ,    lowercase__      ):
                            lowerCAmelCase__	      =   F"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          lowerCAmelCase__	      =   os.path.join(args.output_dir      ,    lowercase__      )
                                   accelerator.save_state(lowercase__      )

              model.eval()
              for step, batch in enumerate(lowercase__      ):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device      )
                     with torch.no_grad():
                            lowerCAmelCase__	      =   model(**lowercase__      )
                     lowerCAmelCase__	      =   outputs.logits.argmax(dim=-1      )
                     lowerCAmelCase__      ,   lowerCAmelCase__	      =   accelerator.gather_for_metrics((predictions, batch['labels'])      )
                     metric.add_batch(
                         predictions=lowercase__      ,    references=lowercase__      ,    )

              lowerCAmelCase__	      =   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""      ,    lowercase__      )

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     lowerCAmelCase__	      =   F"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            lowerCAmelCase__	      =   os.path.join(args.output_dir      ,    lowercase__      )
                     accelerator.save_state(lowercase__      )






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   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(
           '--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(
           '--resume_from_checkpoint'      ,    type=lowercase__      ,    default=lowercase__      ,    help='If the training should continue from a checkpoint folder.'      ,    )
       lowerCAmelCase__	      =   parser.parse_args()
       lowerCAmelCase__	      =   {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       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

