






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def   lowercase__   (  __snake_case					:							Dict						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		fname.split(os.path.sep						)[-1]
					return re.search(R'^(.*)_\d+\.jpg$'  , __snake_case						).groups()[0]



class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase=None       ,	_UpperCamelCase=None     )			->							int:
							UpperCAmelCase_					:   Any   =		file_names
							UpperCAmelCase_					:   Optional[Any]   =		image_transform
							UpperCAmelCase_					:   int   =		label_to_id



		def __len__(							self     )			->							Optional[Any]:
							return len(self.file_names     )



		def __getitem__(							self       ,	_UpperCamelCase     )			->							Optional[Any]:
							UpperCAmelCase_					:   Dict   =		self.file_names[idx]
							UpperCAmelCase_					:   Optional[Any]   =		PIL.Image.open(_UpperCamelCase     )
							UpperCAmelCase_					:   List[str]   =		raw_image.convert('RGB'     )
							if self.image_transform is not None:
												UpperCAmelCase_					:   Optional[Any]   =		self.image_transform(_UpperCamelCase     )
							UpperCAmelCase_					:   Tuple   =		extract_label(_UpperCamelCase     )
							if self.label_to_id is not None:
												UpperCAmelCase_					:   Tuple   =		self.label_to_id[label]
							return {"image": image, "label": label}





def   lowercase__   (  __snake_case					:							int  , __snake_case					:							List[Any]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Tuple   =		Accelerator(cpu=args.cpu  , mixed_precision=args.mixed_precision						)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase_					:   Dict   =		config['lr']
					UpperCAmelCase_					:   Optional[int]   =		int(config['num_epochs']						)
					UpperCAmelCase_					:   List[str]   =		int(config['seed']						)
					UpperCAmelCase_					:   List[Any]   =		int(config['batch_size']						)
					UpperCAmelCase_					:   Tuple   =		config['image_size']
					if not isinstance(__snake_case  , (list, tuple)						):
										UpperCAmelCase_					:   Optional[Any]   =		(image_size, image_size)

					# Grab all the image filenames
					UpperCAmelCase_					:   Optional[Any]   =		[os.path.join(args.data_dir  , __snake_case						) for fname in os.listdir(args.data_dir						) if fname.endswith('.jpg'						)]

					# Build the label correspondences
					UpperCAmelCase_					:   int   =		[extract_label(__snake_case						) for fname in file_names]
					UpperCAmelCase_					:   List[str]   =		list(set(__snake_case						)						)
					id_to_label.sort()
					UpperCAmelCase_					:   str   =		{lbl: i for i, lbl in enumerate(__snake_case						)}

					# Set the seed before splitting the data.
					np.random.seed(__snake_case						)
					torch.manual_seed(__snake_case						)
					torch.cuda.manual_seed_all(__snake_case						)

					# Split our filenames between train and validation
					UpperCAmelCase_					:   Optional[Any]   =		np.random.permutation(len(__snake_case						)						)
					UpperCAmelCase_					:   List[str]   =		int(0.8 * len(__snake_case						)						)
					UpperCAmelCase_					:   List[Any]   =		random_perm[:cut]
					UpperCAmelCase_					:   List[Any]   =		random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					UpperCAmelCase_					:   Tuple   =		Compose([RandomResizedCrop(__snake_case  , scale=(0.5, 1.0)						), ToTensor()]						)
					UpperCAmelCase_					:   str   =		PetsDataset(
					    [file_names[i] for i in train_split]  , image_transform=__snake_case  , label_to_id=__snake_case						)

					# For evaluation, we use a deterministic Resize
					UpperCAmelCase_					:   Union[str, Any]   =		Compose([Resize(__snake_case						), ToTensor()]						)
					UpperCAmelCase_					:   Tuple   =		PetsDataset([file_names[i] for i in eval_split]  , image_transform=__snake_case  , label_to_id=__snake_case						)

					# Instantiate dataloaders.
					UpperCAmelCase_					:   Any   =		DataLoader(__snake_case  , shuffle=__snake_case  , batch_size=__snake_case  , num_workers=4						)
					UpperCAmelCase_					:   Union[str, Any]   =		DataLoader(__snake_case  , shuffle=__snake_case  , batch_size=__snake_case  , num_workers=4						)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCAmelCase_					:   Optional[int]   =		create_model('resnet50d'  , pretrained=__snake_case  , num_classes=len(__snake_case						)						)

					# 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).
					UpperCAmelCase_					:   str   =		model.to(accelerator.device						)

					# Freezing the base model
					for param in model.parameters():
										UpperCAmelCase_					:   List[Any]   =		False
					for param in model.get_classifier().parameters():
										UpperCAmelCase_					:   str   =		True

					# We normalize the batches of images to be a bit faster.
					UpperCAmelCase_					:   Union[str, Any]   =		torch.tensor(model.default_cfg['mean']						)[None, :, None, None].to(accelerator.device						)
					UpperCAmelCase_					:   Dict   =		torch.tensor(model.default_cfg['std']						)[None, :, None, None].to(accelerator.device						)

					# Instantiate optimizer
					UpperCAmelCase_					:   Optional[Any]   =		torch.optim.Adam(params=model.parameters()  , lr=lr / 25						)

					# Instantiate learning rate scheduler
					UpperCAmelCase_					:   Union[str, Any]   =		OneCycleLR(optimizer=__snake_case  , max_lr=__snake_case  , epochs=__snake_case  , steps_per_epoch=len(__snake_case						)						)

					# 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.
					UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_					:   List[str]   =		accelerator.prepare(
					    __snake_case  , __snake_case  , __snake_case  , __snake_case  , __snake_case						)

					# Now we train the model
					for epoch in range(__snake_case						):
										model.train()
										for step, batch in enumerate(__snake_case						):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															UpperCAmelCase_					:   Optional[int]   =		{k: v.to(accelerator.device						) for k, v in batch.items()}
															UpperCAmelCase_					:   List[Any]   =		(batch['image'] - mean) / std
															UpperCAmelCase_					:   Union[str, Any]   =		model(__snake_case						)
															UpperCAmelCase_					:   Tuple   =		torch.nn.functional.cross_entropy(__snake_case  , batch['label']						)
															accelerator.backward(__snake_case						)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

										model.eval()
										UpperCAmelCase_					:   List[str]   =		0
										UpperCAmelCase_					:   List[Any]   =		0
										for _, batch in enumerate(__snake_case						):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															UpperCAmelCase_					:   List[Any]   =		{k: v.to(accelerator.device						) for k, v in batch.items()}
															UpperCAmelCase_					:   Tuple   =		(batch['image'] - mean) / std
															with torch.no_grad():
																				UpperCAmelCase_					:   Dict   =		model(__snake_case						)
															UpperCAmelCase_					:   List[str]   =		outputs.argmax(dim=-1						)
															UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[int]   =		accelerator.gather_for_metrics((predictions, batch['label'])						)
															UpperCAmelCase_					:   Any   =		predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										UpperCAmelCase_					:   str   =		accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}: {100 * eval_metric:.2f}"						)




def   lowercase__   (  ):


					'''simple docstring'''


					UpperCAmelCase_					:   List[str]   =		argparse.ArgumentParser(description='Simple example of training script.'						)
					parser.add_argument('--data_dir'  , required=__snake_case  , help='The data folder on disk.'						)
					parser.add_argument(
					    '--mixed_precision'  , type=__snake_case  , default=__snake_case  , 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(
					    '--checkpointing_steps'  , type=__snake_case  , default=__snake_case  , help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'  , )
					parser.add_argument('--cpu'  , action='store_true'  , help='If passed, will train on the CPU.'						)
					UpperCAmelCase_					:   Any   =		parser.parse_args()
					UpperCAmelCase_					:   str   =		{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
					training_function(__snake_case  , __snake_case						)


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

__UpperCAmelCase										=		logging.get_logger(__name__)

__UpperCAmelCase										=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

__UpperCAmelCase										=		{
    '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'
        ),
    },
}

__UpperCAmelCase										=		{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__UpperCAmelCase										=		{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}




def   lowercase__   (  ):


					'''simple docstring'''


					UpperCAmelCase_					:   int   =		(
					    list(range(ord('!'						)  , ord('~'						) + 1						)						) + list(range(ord('¡'						)  , ord('¬'						) + 1						)						) + list(range(ord('®'						)  , ord('ÿ'						) + 1						)						)
					)
					UpperCAmelCase_					:   List[Any]   =		bs[:]
					UpperCAmelCase_					:   List[Any]   =		0
					for b in range(2**8						):
										if b not in bs:
															bs.append(__snake_case						)
															cs.append(2**8 + n						)
															n += 1
					UpperCAmelCase_					:   Optional[Any]   =		[chr(__snake_case						) for n in cs]
					return dict(zip(__snake_case  , __snake_case						)						)




def   lowercase__   (  __snake_case					:							Optional[int]						):


					'''simple docstring'''


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






class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						VOCAB_FILES_NAMES
		_snake_case					:					Any 		=						PRETRAINED_VOCAB_FILES_MAP
		_snake_case					:					Union[str, Any] 		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case					:					Tuple 		=						['''input_ids''', '''attention_mask''', '''token_type_ids''']



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase="replace"       ,	_UpperCamelCase="[CLS]"       ,	_UpperCamelCase="[SEP]"       ,	_UpperCamelCase="[SEP]"       ,	_UpperCamelCase="[CLS]"       ,	_UpperCamelCase="[UNK]"       ,	_UpperCamelCase="[PAD]"       ,	_UpperCamelCase="[MASK]"       ,	_UpperCamelCase=False       ,	_UpperCamelCase=False       ,	**_UpperCamelCase       ,	)			->							Any:
							UpperCAmelCase_					:   List[str]   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else bos_token
							UpperCAmelCase_					:   List[str]   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else eos_token
							UpperCAmelCase_					:   Dict   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else sep_token
							UpperCAmelCase_					:   Optional[int]   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else cls_token
							UpperCAmelCase_					:   Any   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else unk_token
							UpperCAmelCase_					:   Any   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_					:   Optional[Any]   =		AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase     ) if isinstance(_UpperCamelCase       ,	_UpperCamelCase     ) else mask_token

							super().__init__(
							    errors=_UpperCamelCase       ,	bos_token=_UpperCamelCase       ,	eos_token=_UpperCamelCase       ,	unk_token=_UpperCamelCase       ,	sep_token=_UpperCamelCase       ,	cls_token=_UpperCamelCase       ,	pad_token=_UpperCamelCase       ,	mask_token=_UpperCamelCase       ,	add_prefix_space=_UpperCamelCase       ,	add_bos_token=_UpperCamelCase       ,	**_UpperCamelCase       ,	)
							UpperCAmelCase_					:   List[str]   =		add_bos_token

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

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							UpperCAmelCase_					:   Optional[Any]   =		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       __UpperCAmelCase    (							self     )			->							Any:
							return len(self.encoder     )



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							return dict(self.encoder       ,	**self.added_tokens_encoder     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							if token in self.cache:
												return self.cache[token]
							UpperCAmelCase_					:   Any   =		tuple(_UpperCamelCase     )
							UpperCAmelCase_					:   Tuple   =		get_pairs(_UpperCamelCase     )

							if not pairs:
												return token

							while True:
												UpperCAmelCase_					:   Dict   =		min(_UpperCamelCase       ,	key=lambda _UpperCamelCase     : self.bpe_ranks.get(_UpperCamelCase       ,	float('inf'     )     )     )
												if bigram not in self.bpe_ranks:
																	break
												UpperCAmelCase_ ,						UpperCAmelCase_					:   Tuple   =		bigram
												UpperCAmelCase_					:   Union[str, Any]   =		[]
												UpperCAmelCase_					:   Dict   =		0
												while i < len(_UpperCamelCase     ):
																	try:
																						UpperCAmelCase_					:   Tuple   =		word.index(_UpperCamelCase       ,	_UpperCamelCase     )
																	except ValueError:
																						new_word.extend(word[i:]     )
																						break
																	else:
																						new_word.extend(word[i:j]     )
																						UpperCAmelCase_					:   List[str]   =		j

																	if word[i] == first and i < len(_UpperCamelCase     ) - 1 and word[i + 1] == second:
																						new_word.append(first + second     )
																						i += 2
																	else:
																						new_word.append(word[i]     )
																						i += 1
												UpperCAmelCase_					:   Union[str, Any]   =		tuple(_UpperCamelCase     )
												UpperCAmelCase_					:   Optional[int]   =		new_word
												if len(_UpperCamelCase     ) == 1:
																	break
												else:
																	UpperCAmelCase_					:   Optional[Any]   =		get_pairs(_UpperCamelCase     )
							UpperCAmelCase_					:   str   =		' '.join(_UpperCamelCase     )
							UpperCAmelCase_					:   Optional[int]   =		word
							return word



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							List[int]:
							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_					:   Optional[Any]   =		[self.cls_token_id]
							UpperCAmelCase_					:   Union[str, Any]   =		[self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep



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

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



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							List[int]:
							UpperCAmelCase_					:   Optional[Any]   =		[self.sep_token_id]
							UpperCAmelCase_					:   Union[str, Any]   =		[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       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[str]:
							UpperCAmelCase_					:   Optional[Any]   =		[]
							for token in re.findall(self.pat       ,	_UpperCamelCase     ):
												UpperCAmelCase_					:   int   =		''.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(_UpperCamelCase     ).split(' '     )     )
							return bpe_tokens



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							Optional[Any]:
							return self.encoder.get(_UpperCamelCase       ,	self.encoder.get(self.unk_token     )     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							int:
							return self.decoder.get(_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							Optional[int]:
							UpperCAmelCase_					:   Tuple   =		''.join(_UpperCamelCase     )
							UpperCAmelCase_					:   Any   =		bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'       ,	errors=self.errors     )
							return text



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							Tuple[str]:
							if not os.path.isdir(_UpperCamelCase     ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
												return
							UpperCAmelCase_					:   Dict   =		os.path.join(
							    _UpperCamelCase       ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[Any]   =		os.path.join(
							    _UpperCamelCase       ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

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

							UpperCAmelCase_					:   List[Any]   =		0
							with open(_UpperCamelCase       ,	'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 _UpperCamelCase     : 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!'     )
																						UpperCAmelCase_					:   Tuple   =		token_index
																	writer.write(' '.join(_UpperCamelCase     ) + '\n'     )
																	index += 1

							return vocab_file, merge_file



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase=False       ,	**_UpperCamelCase     )			->							Optional[int]:
							UpperCAmelCase_					:   str   =		kwargs.pop('add_prefix_space'       ,	self.add_prefix_space     )
							if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase     ) > 0 and not text[0].isspace()):
												UpperCAmelCase_					:   Optional[Any]   =		' ' + text
							return (text, kwargs)



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[int]:
							UpperCAmelCase_					:   Union[str, Any]   =		[]
							for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(_UpperCamelCase       ,	add_special_tokens=_UpperCamelCase     ) + [self.eos_token_id]     )
							if len(_UpperCamelCase     ) > self.model_max_length:
												UpperCAmelCase_					:   Union[str, Any]   =		input_ids[-self.model_max_length :]
							return input_ids






