



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__snake_case     :int			       =logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

__snake_case     :Optional[int]			       =list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__snake_case     :List[Any]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     int								=     field(
				    default='cifar10' ,				metadata={'help': 'Name of a dataset from the datasets package'}       )
				A_   :     Optional[Any]								=     field(
				    default=lowercase_ ,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
				A_   :     Tuple								=     field(
				    default=lowercase_ ,				metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'} ,				)
				A_   :     Optional[int]								=     field(default=lowercase_ ,				metadata={'help': 'A folder containing the training data.'}       )
				A_   :     str								=     field(default=lowercase_ ,				metadata={'help': 'A folder containing the validation data.'}       )
				A_   :     List[Any]								=     field(
				    default=0.15 ,				metadata={'help': 'Percent to split off of train for validation.'}       )
				A_   :     int								=     field(default=3_2 ,				metadata={'help': 'The size of the square patches to use for masking.'}       )
				A_   :     List[str]								=     field(
				    default=0.6 ,				metadata={'help': 'Percentage of patches to mask.'} ,				)
				A_   :     Any								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of training examples to this '
				            'value if set.'
				        )
				    } ,				)
				A_   :     Optional[int]								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
				            'value if set.'
				        )
				    } ,				)


				def __UpperCamelCase						(							self      :  str       )		->     Dict:
											A      						=    {}
											if self.train_dir is not None:
																		A      						=    self.train_dir
											if self.validation_dir is not None:
																		A      						=    self.validation_dir
											A      						=    data_files if data_files else None





@dataclass
class        lowerCAmelCase__					:
				A_   :     Tuple								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
				            'checkpoint identifier on the hub. '
				            'Don\'t set if you want to train a model from scratch.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=lowercase_ ,				metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowercase_       )} ,				)
				A_   :     Optional[Any]								=     field(
				    default=lowercase_ ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
				A_   :     Tuple								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'Override some existing default config settings when a model is trained from scratch. Example: '
				            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
				        )
				    } ,				)
				A_   :     Dict								=     field(
				    default=lowercase_ ,				metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'} ,				)
				A_   :     List[Any]								=     field(
				    default='main' ,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,				)
				A_   :     int								=     field(default=lowercase_ ,				metadata={'help': 'Name or path of preprocessor config.'}       )
				A_   :     List[str]								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
				            'with private models).'
				        )
				    } ,				)
				A_   :     Dict								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
				        )
				    } ,				)
				A_   :     Union[str, Any]								=     field(
				    default=lowercase_ ,				metadata={
				        'help': (
				            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
				        )
				    } ,				)
				A_   :     Union[str, Any]								=     field(
				    default=lowercase_ ,				metadata={'help': 'Stride to use for the encoder.'} ,				)





class        lowerCAmelCase__					:


				def __init__(							self      :  Union[str, Any]						, __UpperCamelCase      :  Optional[int]=192						, __UpperCamelCase      :  Any=32						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  List[Any]=0.6       )		->     Tuple:
											A      						=    input_size
											A      						=    mask_patch_size
											A      						=    model_patch_size
											A      						=    mask_ratio

											if self.input_size % self.mask_patch_size != 0:
																		raise ValueError('Input size must be divisible by mask patch size'       )
											if self.mask_patch_size % self.model_patch_size != 0:
																		raise ValueError('Mask patch size must be divisible by model patch size'       )

											A      						=    self.input_size // self.mask_patch_size
											A      						=    self.mask_patch_size // self.model_patch_size

											A      						=    self.rand_size**2
											A      						=    int(np.ceil(self.token_count * self.mask_ratio       )       )


				def __call__(							self      :  Optional[Any]       )		->     Optional[int]:
											A      						=    np.random.permutation(self.token_count       )[: self.mask_count]
											A      						=    np.zeros(self.token_count						, dtype=UpperCamelCase_       )
											A      						=    1

											A      						=    mask.reshape((self.rand_size, self.rand_size)       )
											A      						=    mask.repeat(self.scale						, axis=0       ).repeat(self.scale						, axis=1       )

											return torch.tensor(mask.flatten()       )






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    torch.stack([example['pixel_values'] for example in examples]				)
							A      						=    torch.stack([example['mask'] for example in examples]				)
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def  lowerCamelCase_	(      )       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
							if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														A						,   A						,   A      						=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
							else:
														A						,   A						,   A      						=    parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry('run_mim'			,	__lowercase			,	__lowercase				)

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,	datefmt='%m/%d/%Y %H:%M:%S'			,	handlers=[logging.StreamHandler(sys.stdout				)]			,	)

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							A      						=    training_args.get_process_log_level()
							logger.setLevel(__lowercase				)
							transformers.utils.logging.set_verbosity(__lowercase				)
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + F'''distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'''				)
							logger.info(F'''Training/evaluation parameters {training_args}'''				)

							# Detecting last checkpoint.
							A      						=    None
							if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
														A      						=    get_last_checkpoint(training_args.output_dir				)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
																					raise ValueError(
																					    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    'Use --overwrite_output_dir to overcome.'				)
														elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																					logger.info(
																					    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Initialize our dataset.
							A      						=    load_dataset(
							    data_args.dataset_name			,	data_args.dataset_config_name			,	data_files=data_args.data_files			,	cache_dir=model_args.cache_dir			,	use_auth_token=True if model_args.use_auth_token else None			,	)

							# If we don't have a validation split, split off a percentage of train as validation.
							A      						=    None if 'validation' in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split			,	__lowercase				) and data_args.train_val_split > 0.0:
														A      						=    ds['train'].train_test_split(data_args.train_val_split				)
														A      						=    split['train']
														A      						=    split['test']

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							A      						=    {
							    'cache_dir': model_args.cache_dir,
							    'revision': model_args.model_revision,
							    'use_auth_token': True if model_args.use_auth_token else None,
							}
							if model_args.config_name_or_path:
														A      						=    AutoConfig.from_pretrained(model_args.config_name_or_path			,	**__lowercase				)
							elif model_args.model_name_or_path:
														A      						=    AutoConfig.from_pretrained(model_args.model_name_or_path			,	**__lowercase				)
							else:
														A      						=    CONFIG_MAPPING[model_args.model_type]()
														logger.warning('You are instantiating a new config instance from scratch.'				)
														if model_args.config_overrides is not None:
																					logger.info(F'''Overriding config: {model_args.config_overrides}'''				)
																					config.update_from_string(model_args.config_overrides				)
																					logger.info(F'''New config: {config}'''				)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
							if hasattr(__lowercase			,	'decoder_type'				):
														A      						=    'simmim'

							# adapt config
							A      						=    model_args.image_size if model_args.image_size is not None else config.image_size
							A      						=    model_args.patch_size if model_args.patch_size is not None else config.patch_size
							A      						=    (
							    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
							)

							config.update(
							    {
							        'image_size': model_args.image_size,
							        'patch_size': model_args.patch_size,
							        'encoder_stride': model_args.encoder_stride,
							    }				)

							# create image processor
							if model_args.image_processor_name:
														A      						=    AutoImageProcessor.from_pretrained(model_args.image_processor_name			,	**__lowercase				)
							elif model_args.model_name_or_path:
														A      						=    AutoImageProcessor.from_pretrained(model_args.model_name_or_path			,	**__lowercase				)
							else:
														A      						=    {
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														A      						=    IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														A      						=    AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path			,	from_tf=bool('.ckpt' in model_args.model_name_or_path				)			,	config=__lowercase			,	cache_dir=model_args.cache_dir			,	revision=model_args.model_revision			,	use_auth_token=True if model_args.use_auth_token else None			,	)
							else:
														logger.info('Training new model from scratch'				)
														A      						=    AutoModelForMaskedImageModeling.from_config(__lowercase				)

							if training_args.do_train:
														A      						=    ds['train'].column_names
							else:
														A      						=    ds['validation'].column_names

							if data_args.image_column_name is not None:
														A      						=    data_args.image_column_name
							elif "image" in column_names:
														A      						=    'image'
							elif "img" in column_names:
														A      						=    'img'
							else:
														A      						=    column_names[0]

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							A      						=    Compose(
							    [
							        Lambda(lambda lowerCAmelCase__				: img.convert('RGB'				) if img.mode != "RGB" else img				),
							        RandomResizedCrop(model_args.image_size			,	scale=(0.67, 1.0)			,	ratio=(3.0 / 4.0, 4.0 / 3.0)				),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean			,	std=image_processor.image_std				),
							    ]				)

							# create mask generator
							A      						=    MaskGenerator(
							    input_size=model_args.image_size			,	mask_patch_size=data_args.mask_patch_size			,	model_patch_size=model_args.patch_size			,	mask_ratio=data_args.mask_ratio			,	)

							def preprocess_images(lowerCAmelCase__	:	Optional[Any]				):
														A      						=    [transforms(__lowercase				) for image in examples[image_column_name]]
														A      						=    [mask_generator() for i in range(len(examples[image_column_name]				)				)]

														return examples

							if training_args.do_train:
														if "train" not in ds:
																					raise ValueError('--do_train requires a train dataset'				)
														if data_args.max_train_samples is not None:
																					A      						=    ds['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
														# Set the training transforms
														ds["train"].set_transform(__lowercase				)

							if training_args.do_eval:
														if "validation" not in ds:
																					raise ValueError('--do_eval requires a validation dataset'				)
														if data_args.max_eval_samples is not None:
																					A      						=    (
																					    ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
																					)
														# Set the validation transforms
														ds["validation"].set_transform(__lowercase				)

							# Initialize our trainer
							A      						=    Trainer(
							    model=__lowercase			,	args=__lowercase			,	train_dataset=ds['train'] if training_args.do_train else None			,	eval_dataset=ds['validation'] if training_args.do_eval else None			,	tokenizer=__lowercase			,	data_collator=__lowercase			,	)

							# Training
							if training_args.do_train:
														A      						=    None
														if training_args.resume_from_checkpoint is not None:
																					A      						=    training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					A      						=    last_checkpoint
														A      						=    trainer.train(resume_from_checkpoint=__lowercase				)
														trainer.save_model()
														trainer.log_metrics('train'			,	train_result.metrics				)
														trainer.save_metrics('train'			,	train_result.metrics				)
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														A      						=    trainer.evaluate()
														trainer.log_metrics('eval'			,	__lowercase				)
														trainer.save_metrics('eval'			,	__lowercase				)

							# Write model card and (optionally) push to hub
							A      						=    {
							    'finetuned_from': model_args.model_name_or_path,
							    'tasks': 'masked-image-modeling',
							    'dataset': data_args.dataset_name,
							    'tags': ['masked-image-modeling'],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**__lowercase				)
							else:
														trainer.create_model_card(**__lowercase				)


if __name__ == "__main__":
				main()







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							List[str]						= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a   :							Dict						= {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__a   :							List[str]						= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE     =					RobertaTokenizer




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


         """simple docstring"""



         super().__init__(
             UpperCamelCase_			,						UpperCamelCase_			,						tokenizer_file=UpperCamelCase_			,						errors=UpperCamelCase_			,						bos_token=UpperCamelCase_			,						eos_token=UpperCamelCase_			,						sep_token=UpperCamelCase_			,						cls_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						add_prefix_space=UpperCamelCase_			,						trim_offsets=UpperCamelCase_			,						**UpperCamelCase_			,						)

         __A       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
             __A       =						getattr(UpperCamelCase_			,						pre_tok_state.pop("""type"""					)					)
             __A       =						add_prefix_space
             __A       =						pre_tok_class(**UpperCamelCase_					)

         __A       =						add_prefix_space

         __A       =						"""post_processor"""
         __A       =						getattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)
         if tokenizer_component_instance:
             __A       =						json.loads(tokenizer_component_instance.__getstate__()					)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __A       =						tuple(state["""sep"""]					)
             if "cls" in state:
                 __A       =						tuple(state["""cls"""]					)

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

             if changes_to_apply:
                 __A       =						getattr(UpperCamelCase_			,						state.pop("""type"""					)					)
                 __A       =						component_class(**UpperCamelCase_					)
                 setattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         if self._mask_token is None:
             if self.verbose:
                 logger.error("""Using mask_token, but it is not set yet."""					)
             return None
         return str(self._mask_token					)




     @mask_token.setter
     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Tuple			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Optional[int]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      int=None					):


         """simple docstring"""



         __A       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = 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 + sep + token_ids_a + sep					) * [0]




