


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
_A		:				List[Any]									=       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''')

_A		:				Tuple									=       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_A		:				Optional[Any]									=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def     __lowerCAmelCase						(							snake_case	:    str )       ->							Tuple:
   with open(snake_case						,      """rb""" ) as f:
      __lowerCamelCase:				List[Any]     =	Image.open(snake_case )
      return im.convert("""RGB""" )



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





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



def     __lowerCAmelCase						(							snake_case	:    List[Any] )       ->							Any:
   __lowerCamelCase:				List[str]     =	torch.stack([example["""pixel_values"""] for example in examples] )
   __lowerCamelCase:				Optional[Any]     =	torch.tensor([example["""labels"""] for example in examples] )
   return {"pixel_values": pixel_values, "labels": labels}



def     __lowerCAmelCase						(							)       ->							Any:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   __lowerCamelCase:				Dict     =	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.
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Any     =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				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"""						,      snake_case						,      snake_case )

   # 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()

   __lowerCamelCase:				Optional[Any]     =	training_args.get_process_log_level()
   logger.setLevel(snake_case )
   transformers.utils.logging.set_verbosity(snake_case )
   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.
   __lowerCamelCase:				List[str]     =	None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      __lowerCamelCase:				List[Any]     =	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:
      __lowerCamelCase:				Any     =	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:
      __lowerCamelCase:				List[str]     =	{}
      if data_args.train_dir is not None:
         __lowerCamelCase:				List[str]     =	os.path.join(data_args.train_dir						,      """**""" )
      if data_args.validation_dir is not None:
         __lowerCamelCase:				Union[str, Any]     =	os.path.join(data_args.validation_dir						,      """**""" )
      __lowerCamelCase:				Optional[Any]     =	load_dataset(
          """imagefolder"""						,      data_files=snake_case						,      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.
   __lowerCamelCase:				Any     =	None if """validation""" in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split						,      snake_case ) and data_args.train_val_split > 0.0:
      __lowerCamelCase:				Optional[Any]     =	dataset["""train"""].train_test_split(data_args.train_val_split )
      __lowerCamelCase:				Optional[int]     =	split["""train"""]
      __lowerCamelCase:				Any     =	split["""test"""]

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

   # Load the accuracy metric from the datasets package
   __lowerCamelCase:				Any     =	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(snake_case	:    Tuple ):
      return metric.compute(predictions=np.argmax(p.predictions						,      axis=1 )						,      references=p.label_ids )

   __lowerCamelCase:				Any     =	AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path						,      num_labels=len(snake_case )						,      labelaid=snake_case						,      idalabel=snake_case						,      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						,      )
   __lowerCamelCase:				List[Any]     =	AutoModelForImageClassification.from_pretrained(
       model_args.model_name_or_path						,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )						,      config=snake_case						,      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						,      )
   __lowerCamelCase:				int     =	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:
      __lowerCamelCase:				Dict     =	image_processor.size["""shortest_edge"""]
   else:
      __lowerCamelCase:				List[Any]     =	(image_processor.size["""height"""], image_processor.size["""width"""])
   __lowerCamelCase:				Tuple     =	Normalize(mean=image_processor.image_mean						,      std=image_processor.image_std )
   __lowerCamelCase:				List[Any]     =	Compose(
       [
           RandomResizedCrop(snake_case ),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ] )
   __lowerCamelCase:				Union[str, Any]     =	Compose(
       [
           Resize(snake_case ),
           CenterCrop(snake_case ),
           ToTensor(),
           normalize,
       ] )

   def train_transforms(snake_case	:    int ):
      __lowerCamelCase:				str     =	[
          _train_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch["""image"""]
      ]
      return example_batch

   def val_transforms(snake_case	:    Dict ):
      __lowerCamelCase:				str     =	[_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:
         __lowerCamelCase:				Dict     =	(
             dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
         )
      # Set the training transforms
      dataset["train"].set_transform(snake_case )

   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:
         __lowerCamelCase:				List[str]     =	(
             dataset["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
         )
      # Set the validation transforms
      dataset["validation"].set_transform(snake_case )

   # Initalize our trainer
   __lowerCamelCase:				Any     =	Trainer(
       model=snake_case						,      args=snake_case						,      train_dataset=dataset["""train"""] if training_args.do_train else None						,      eval_dataset=dataset["""validation"""] if training_args.do_eval else None						,      compute_metrics=snake_case						,      tokenizer=snake_case						,      data_collator=snake_case						,      )

   # Training
   if training_args.do_train:
      __lowerCamelCase:				Optional[Any]     =	None
      if training_args.resume_from_checkpoint is not None:
         __lowerCamelCase:				str     =	training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         __lowerCamelCase:				Dict     =	last_checkpoint
      __lowerCamelCase:				Union[str, Any]     =	trainer.train(resume_from_checkpoint=snake_case )
      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:
      __lowerCamelCase:				Optional[int]     =	trainer.evaluate()
      trainer.log_metrics("""eval"""						,      snake_case )
      trainer.save_metrics("""eval"""						,      snake_case )

   # Write model card and (optionally) push to hub
   __lowerCamelCase:				Tuple     =	{
       """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(**snake_case )
   else:
      trainer.create_model_card(**snake_case )


if __name__ == "__main__":
   main()






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_A		:				List[str]									=       logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
_A		:				int									=       {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

_A		:				Dict									=       {
    '''allenai/led-base-16384''': 16_384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	(
       list(range(ord("""!""" )						,      ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )						,      ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )						,      ord("""ÿ""" ) + 1 ) )
   )
   __lowerCamelCase:				List[str]     =	bs[:]
   __lowerCamelCase:				Tuple     =	0
   for b in range(2**8 ):
      if b not in bs:
         bs.append(snake_case )
         cs.append(2**8 + n )
         n += 1
   __lowerCamelCase:				List[Any]     =	[chr(snake_case ) for n in cs]
   return dict(zip(snake_case						,      snake_case ) )



def     __lowerCAmelCase						(							snake_case	:    List[str] )       ->							str:
   __lowerCamelCase:				int     =	set()
   __lowerCamelCase:				str     =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      __lowerCamelCase:				Any     =	char
   return pairs







class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  Optional[Any]						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  List[str]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  List[str]						      =	["input_ids", "attention_mask"]





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict="replace"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="</s>"    ,	SCREAMING_SNAKE_CASE_					:				int="</s>"    ,	SCREAMING_SNAKE_CASE_					:				Any="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<unk>"    ,	SCREAMING_SNAKE_CASE_					:				Tuple="<pad>"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<mask>"    ,	SCREAMING_SNAKE_CASE_					:				int=False    ,	**SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else bos_token
        __lowerCamelCase:				Union[str, Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else eos_token
        __lowerCamelCase:				List[Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else sep_token
        __lowerCamelCase:				Dict     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else cls_token
        __lowerCamelCase:				Any     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else unk_token
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __lowerCamelCase:				List[str]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else mask_token

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

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __lowerCamelCase:				str     =	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.bart.tokenization_bart.BartTokenizer.vocab_size
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return len(self.encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return dict(self.encoder    ,	**self.added_tokens_encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if token in self.cache:
           return self.cache[token]
        __lowerCamelCase:				List[str]     =	tuple(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	get_pairs(SCREAMING_SNAKE_CASE_							)

        if not pairs:
           return token

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

              if word[i] == first and i < len(SCREAMING_SNAKE_CASE_							) - 1 and word[i + 1] == second:
                 new_word.append(first + second							)
                 i += 2
              else:
                 new_word.append(word[i]							)
                 i += 1
           __lowerCamelCase:				Optional[int]     =	tuple(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Any     =	new_word
           if len(SCREAMING_SNAKE_CASE_							) == 1:
              break
           else:
              __lowerCamelCase:				Tuple     =	get_pairs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	""" """.join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	word
        return word





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Any     =	[]
        for token in re.findall(self.pat    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				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(SCREAMING_SNAKE_CASE_							).split(""" """							)							)
        return bpe_tokens





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        return self.encoder.get(SCREAMING_SNAKE_CASE_    ,	self.encoder.get(self.unk_token							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str							):
        return self.decoder.get(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	"""""".join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""    ,	errors=self.errors							)
        return text





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        if not os.path.isdir(SCREAMING_SNAKE_CASE_							):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
           return
        __lowerCamelCase:				Dict     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
        __lowerCamelCase:				str     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

        __lowerCamelCase:				List[str]     =	0
        with open(SCREAMING_SNAKE_CASE_    ,	"""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 SCREAMING_SNAKE_CASE_							: 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!"""							)
                 __lowerCamelCase:				Tuple     =	token_index
              writer.write(""" """.join(SCREAMING_SNAKE_CASE_							) + """\n"""							)
              index += 1

        return vocab_file, merge_file





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __lowerCamelCase:				List[str]     =	[self.cls_token_id]
        __lowerCamelCase:				int     =	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE_    ,	token_ids_a=SCREAMING_SNAKE_CASE_    ,	already_has_special_tokens=SCREAMING_SNAKE_CASE_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        __lowerCamelCase:				int     =	[self.sep_token_id]
        __lowerCamelCase:				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 + sep + token_ids_a + sep							) * [0]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	kwargs.pop("""add_prefix_space"""    ,	self.add_prefix_space							)
        if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_							) > 0 and not text[0].isspace()):
           __lowerCamelCase:				Dict     =	""" """ + text
        return (text, kwargs)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[Dict[str, EncodedInput], BatchEncoding]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	):
        __lowerCamelCase:				Optional[Any]     =	super()._pad(
            encoded_inputs=SCREAMING_SNAKE_CASE_    ,	max_length=SCREAMING_SNAKE_CASE_    ,	padding_strategy=SCREAMING_SNAKE_CASE_    ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,	return_attention_mask=SCREAMING_SNAKE_CASE_    ,	)

        # Load from model defaults
        if return_attention_mask is None:
           __lowerCamelCase:				Optional[Any]     =	"""attention_mask""" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
           __lowerCamelCase:				List[str]     =	encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           __lowerCamelCase:				str     =	len(encoded_inputs["""global_attention_mask"""]							) != len(SCREAMING_SNAKE_CASE_							)

           if needs_to_be_padded:
              __lowerCamelCase:				Union[str, Any]     =	len(SCREAMING_SNAKE_CASE_							) - len(encoded_inputs["""global_attention_mask"""]							)

              if self.padding_side == "right":
                 # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                 __lowerCamelCase:				str     =	(
                     encoded_inputs["""global_attention_mask"""] + [-1] * difference
                 )
              elif self.padding_side == "left":
                 __lowerCamelCase:				Optional[int]     =	[-1] * difference + encoded_inputs[
                     """global_attention_mask"""
                 ]
              else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

        return encoded_inputs


