


'''simple docstring'''



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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
__UpperCAmelCase      :Optional[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-classification/requirements.txt")

__UpperCAmelCase      :List[str]			    =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__UpperCAmelCase      :str			    =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






     with open(_lowercase      , '''rb'''  ) as f:
          __UpperCAmelCase     :			Tuple  							=      Image.open(_lowercase  )
          return im.convert('''RGB'''  )



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(
         default=_a     ,  metadata={
             "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
         }     ,  )
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(
         default=_a     ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(default=_a     ,  metadata={"help": "A folder containing the training data."}	)
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(default=_a     ,  metadata={"help": "A folder containing the validation data."}	)
     SCREAMING_SNAKE_CASE	:			Optional[float]							       =       field(
         default=0.15     ,  metadata={"help": "Percent to split off of train for validation."}	)
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       field(
         default=_a     ,  metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }     ,  )
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       field(
         default=_a     ,  metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }     ,  )

     def 			lowerCamelCase__			(    self					:		str      )						->							Union[str, Any]:
          if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
               raise ValueError(
                   '''You must specify either a dataset name from the hub or a train and/or validation directory.'''      )



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       field(
         default="google/vit-base-patch16-224-in21k"     ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     ,  )
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(
         default=_a     ,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_a	)}     ,  )
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(
         default=_a     ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       field(
         default=_a     ,  metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}	)
     SCREAMING_SNAKE_CASE	:			str							       =       field(
         default="main"     ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}     ,  )
     SCREAMING_SNAKE_CASE	:			str							       =       field(default=_a     ,  metadata={"help": "Name or path of preprocessor config."}	)
     SCREAMING_SNAKE_CASE	:			bool							       =       field(
         default=_a     ,  metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }     ,  )
     SCREAMING_SNAKE_CASE	:			bool							       =       field(
         default=_a     ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}     ,  )




def      _a     (						_lowercase :     List[Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			List[Any]  							=      torch.stack([example['''pixel_values'''] for example in examples]  )
     __UpperCAmelCase     :			Any  							=      torch.tensor([example['''labels'''] for example in examples]  )
     return {"pixel_values": pixel_values, "labels": labels}




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      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.
          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Optional[Any]  							=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
     else:
          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      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_image_classification'''      , _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()

     __UpperCAmelCase     :			str  							=      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.
     __UpperCAmelCase     :			Optional[int]  							=      None
     if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
          __UpperCAmelCase     :			List[str]  							=      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.'''  )

    # Set seed before initializing model.
     set_seed(training_args.seed  )

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          __UpperCAmelCase     :			Dict  							=      load_dataset(
              data_args.dataset_name      , data_args.dataset_config_name      , cache_dir=model_args.cache_dir      , task='''image-classification'''      , use_auth_token=True if model_args.use_auth_token else None      , )
     else:
          __UpperCAmelCase     :			Tuple  							=      {}
          if data_args.train_dir is not None:
               __UpperCAmelCase     :			List[str]  							=      os.path.join(data_args.train_dir      , '''**'''  )
          if data_args.validation_dir is not None:
               __UpperCAmelCase     :			Tuple  							=      os.path.join(data_args.validation_dir      , '''**'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      load_dataset(
              '''imagefolder'''      , data_files=_lowercase      , cache_dir=model_args.cache_dir      , task='''image-classification'''      , )

     # If we don't have a validation split, split off a percentage of train as validation.
     __UpperCAmelCase     :			str  							=      None if '''validation''' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split      , _lowercase  ) and data_args.train_val_split > 0.0:
          __UpperCAmelCase     :			Tuple  							=      dataset['''train'''].train_test_split(data_args.train_val_split  )
          __UpperCAmelCase     :			Union[str, Any]  							=      split['''train''']
          __UpperCAmelCase     :			Any  							=      split['''test''']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     __UpperCAmelCase     :			Union[str, Any]  							=      dataset['''train'''].features['''labels'''].names
     __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      {}, {}
     for i, label in enumerate(_lowercase  ):
          __UpperCAmelCase     :			int  							=      str(_lowercase  )
          __UpperCAmelCase     :			List[Any]  							=      label

     # Load the accuracy metric from the datasets package
     __UpperCAmelCase     :			List[str]  							=      evaluate.load('''accuracy'''  )

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(_lowercase :     Any  ):
          return metric.compute(predictions=np.argmax(p.predictions      , axis=1  )      , references=p.label_ids  )

     __UpperCAmelCase     :			Optional[int]  							=      AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path      , num_labels=len(_lowercase  )      , labelaid=_lowercase      , idalabel=_lowercase      , finetuning_task='''image-classification'''      , cache_dir=model_args.cache_dir      , revision=model_args.model_revision      , use_auth_token=True if model_args.use_auth_token else None      , )
     __UpperCAmelCase     :			List[Any]  							=      AutoModelForImageClassification.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      , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      , )
     __UpperCAmelCase     :			Dict  							=      AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path      , cache_dir=model_args.cache_dir      , revision=model_args.model_revision      , use_auth_token=True if model_args.use_auth_token else None      , )

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          __UpperCAmelCase     :			List[Any]  							=      image_processor.size['''shortest_edge''']
     else:
          __UpperCAmelCase     :			Dict  							=      (image_processor.size['''height'''], image_processor.size['''width'''])
     __UpperCAmelCase     :			int  							=      Normalize(mean=image_processor.image_mean      , std=image_processor.image_std  )
     __UpperCAmelCase     :			List[Any]  							=      Compose(
         [
             RandomResizedCrop(_lowercase  ),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]  )
     __UpperCAmelCase     :			Optional[int]  							=      Compose(
         [
             Resize(_lowercase  ),
             CenterCrop(_lowercase  ),
             ToTensor(),
             normalize,
         ]  )

     def train_transforms(_lowercase :     str  ):
          __UpperCAmelCase     :			Optional[Any]  							=      [
              _train_transforms(pil_img.convert('''RGB'''  )  ) for pil_img in example_batch['''image''']
          ]
          return example_batch

     def val_transforms(_lowercase :     List[Any]  ):
          __UpperCAmelCase     :			Tuple  							=      [_val_transforms(pil_img.convert('''RGB'''  )  ) for pil_img in example_batch['''image''']]
          return example_batch

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

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

     # Initalize our trainer
     __UpperCAmelCase     :			List[str]  							=      Trainer(
         model=_lowercase      , args=_lowercase      , train_dataset=dataset['''train'''] if training_args.do_train else None      , eval_dataset=dataset['''validation'''] if training_args.do_eval else None      , compute_metrics=_lowercase      , tokenizer=_lowercase      , data_collator=_lowercase      , )

     # Training
     if training_args.do_train:
          __UpperCAmelCase     :			Union[str, Any]  							=      None
          if training_args.resume_from_checkpoint is not None:
               __UpperCAmelCase     :			Any  							=      training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               __UpperCAmelCase     :			int  							=      last_checkpoint
          __UpperCAmelCase     :			List[Any]  							=      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:
          __UpperCAmelCase     :			Tuple  							=      trainer.evaluate()
          trainer.log_metrics('''eval'''      , _lowercase  )
          trainer.save_metrics('''eval'''      , _lowercase  )

     # Write model card and (optionally) push to hub
     __UpperCAmelCase     :			Optional[int]  							=      {
         '''finetuned_from''': model_args.model_name_or_path,
         '''tasks''': '''image-classification''',
         '''dataset''': data_args.dataset_name,
         '''tags''': ['''image-classification''', '''vision'''],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_lowercase  )
     else:
          trainer.create_model_card(**_lowercase  )


if __name__ == "__main__":
      main()



'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

__UpperCAmelCase      :str			    =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase      :Optional[int]			    =       {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase      :Union[str, Any]			    =       {
    "bert-base-uncased": 5_1_2,
    "bert-large-uncased": 5_1_2,
    "bert-base-cased": 5_1_2,
    "bert-large-cased": 5_1_2,
    "bert-base-multilingual-uncased": 5_1_2,
    "bert-base-multilingual-cased": 5_1_2,
    "bert-base-chinese": 5_1_2,
    "bert-base-german-cased": 5_1_2,
    "bert-large-uncased-whole-word-masking": 5_1_2,
    "bert-large-cased-whole-word-masking": 5_1_2,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-base-cased-finetuned-mrpc": 5_1_2,
    "bert-base-german-dbmdz-cased": 5_1_2,
    "bert-base-german-dbmdz-uncased": 5_1_2,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_1_2,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_1_2,
    "wietsedv/bert-base-dutch-cased": 5_1_2,
}

__UpperCAmelCase      :str			    =       {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       BertTokenizer

     def __init__(    self					:		Optional[int]							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		str=None							,			snake_case					:		Any=True							,			snake_case					:		Tuple="[UNK]"							,			snake_case					:		int="[SEP]"							,			snake_case					:		Optional[Any]="[PAD]"							,			snake_case					:		int="[CLS]"							,			snake_case					:		Optional[Any]="[MASK]"							,			snake_case					:		Union[str, Any]=True							,			snake_case					:		List[Any]=None							,			**snake_case					:		int							,			)						->							str:
          super().__init__(
              snake_case							,			tokenizer_file=snake_case							,			do_lower_case=snake_case							,			unk_token=snake_case							,			sep_token=snake_case							,			pad_token=snake_case							,			cls_token=snake_case							,			mask_token=snake_case							,			tokenize_chinese_chars=snake_case							,			strip_accents=snake_case							,			**snake_case							,			)

          __UpperCAmelCase     :			List[str]  							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              normalizer_state.get('''lowercase'''							,			snake_case      ) != do_lower_case
              or normalizer_state.get('''strip_accents'''							,			snake_case      ) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''							,			snake_case      ) != tokenize_chinese_chars
          ):
               __UpperCAmelCase     :			List[Any]  							=      getattr(snake_case							,			normalizer_state.pop('''type'''      )      )
               __UpperCAmelCase     :			List[Any]  							=      do_lower_case
               __UpperCAmelCase     :			List[Any]  							=      strip_accents
               __UpperCAmelCase     :			str  							=      tokenize_chinese_chars
               __UpperCAmelCase     :			List[str]  							=      normalizer_class(**snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      do_lower_case

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[str]=None      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

          return output

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			Tuple  							=      [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 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Optional[str] = None      )						->							Tuple[str]:
          __UpperCAmelCase     :			Optional[int]  							=      self._tokenizer.model.save(snake_case							,			name=snake_case      )
          return tuple(snake_case      )