


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

snake_case__				:	int					       =					logging.get_logger(__name__)


snake_case__				:	Tuple					       =					{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: str						,						A__						: List[str]						,						A__						: Optional[Any]					)     ->    List[str]:
   if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
      raise ValueError(f'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()					)}.'''					)

   if tokenizer_name is None:
      lowerCamelCase_						:   Any		= TOKENIZER_CLASSES
   else:
      lowerCamelCase_						:   Any		= {tokenizer_name: getattr(A__						,						tokenizer_name + """Fast"""					)}

   logger.info(f'''Loading tokenizer classes: {tokenizer_names}'''					)

   for tokenizer_name in tokenizer_names:
      lowerCamelCase_						:   Tuple		= TOKENIZER_CLASSES[tokenizer_name]

      lowerCamelCase_						:   Union[str, Any]		= True
      if checkpoint_name is None:
         lowerCamelCase_						:   str		= list(tokenizer_class.max_model_input_sizes.keys()					)
      else:
         lowerCamelCase_						:   Tuple		= [checkpoint_name]

      logger.info(f'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'''					)

      for checkpoint in checkpoint_names:
         logger.info(f'''Loading {tokenizer_class.__class__.__name__} {checkpoint}'''					)

         # Load tokenizer
         lowerCamelCase_						:   Tuple		= tokenizer_class.from_pretrained(A__						,						force_download=A__					)

         # Save fast tokenizer
         logger.info(f'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'''					)

         # For organization names we create sub-directories
         if "/" in checkpoint:
            lowerCamelCase_,	lowerCamelCase_						:   int		= checkpoint.split("""/"""					)
            lowerCamelCase_						:   List[Any]		= os.path.join(A__						,						A__					)
         elif add_prefix:
            lowerCamelCase_						:   List[Any]		= checkpoint
            lowerCamelCase_						:   int		= dump_path
         else:
            lowerCamelCase_						:   str		= None
            lowerCamelCase_						:   int		= dump_path

         logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''					)

         if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()					)[0]:
            lowerCamelCase_						:   str		= list(tokenizer.pretrained_vocab_files_map.values()					)[0][checkpoint]
            lowerCamelCase_						:   Any		= file_path.split(A__					)[-1][0]
            if next_char == "/":
               lowerCamelCase_						:   str		= os.path.join(A__						,						A__					)
               lowerCamelCase_						:   Optional[Any]		= None

            logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''					)

         lowerCamelCase_						:   Any		= tokenizer.save_pretrained(
             A__						,						legacy_format=A__						,						filename_prefix=A__					)
         logger.info(f'''=> File names {file_names}'''					)

         for file_name in file_names:
            if not file_name.endswith("""tokenizer.json"""					):
               os.remove(A__					)
               logger.info(f'''=> removing {file_name}'''					)


if __name__ == "__main__":
      snake_case__				:	int					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
      )
      parser.add_argument(
          '--tokenizer_name',
          default=None,
          type=str,
          help=(
              F'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
              'download and convert all the checkpoints from AWS.'
          ),
      )
      parser.add_argument(
          '--checkpoint_name',
          default=None,
          type=str,
          help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
      )
      parser.add_argument(
          '--force_download',
          action='store_true',
          help='Re-download checkpoints.',
      )
      snake_case__				:	str					       =					parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)









import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__				:	str					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
       _a   							=							field(
           metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
       _a   							=							field(
           default=128						,       metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Overwrite the cached training and evaluation sets"}						)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   Optional[Any]		= self.task_name.lower()







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"train"
       _a   							=							"dev"
       _a   							=							"test"







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							42
       _a   							=							42
       _a   							=							42



       def __init__(						self				:				Any		,				__a				:				GlueDataTrainingArguments		,				__a				:				PreTrainedTokenizerBase		,				__a				:				Optional[int] = None		,				__a				:				Union[str, Split] = Split.train		,				__a				:				Optional[str] = None		,				)					->Optional[int]:
          warnings.warn(
              """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
              """library. You can have a look at this example script for pointers: """
              """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""		,				__a		,				)
          lowerCamelCase_						:   Optional[int]		= args
          lowerCamelCase_						:   Tuple		= glue_processors[args.task_name]()
          lowerCamelCase_						:   Optional[Any]		= glue_output_modes[args.task_name]
          if isinstance(__a		,				__a							):
             try:
                lowerCamelCase_						:   List[Any]		= Split[mode]
             except KeyError:
                raise KeyError("""mode is not a valid split name"""							)
        # Load data features from cache or dataset file
          lowerCamelCase_						:   List[Any]		= os.path.join(
              cache_dir if cache_dir is not None else args.data_dir		,				F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''		,				)
          lowerCamelCase_						:   Any		= self.processor.get_labels()
          if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
              "RobertaTokenizer",
              "RobertaTokenizerFast",
              "XLMRobertaTokenizer",
              "BartTokenizer",
              "BartTokenizerFast",
          ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase_,	lowerCamelCase_						:   int		= label_list[2], label_list[1]
          lowerCamelCase_						:   Any		= label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase_						:   List[Any]		= cached_features_file + """.lock"""
          with FileLock(__a							):
             if os.path.exists(__a							) and not args.overwrite_cache:
                lowerCamelCase_						:   str		= time.time()
                lowerCamelCase_						:   int		= torch.load(__a							)
                logger.info(
                    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''		,				time.time() - start							)
             else:
                logger.info(F'''Creating features from dataset file at {args.data_dir}'''							)

                if mode == Split.dev:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_dev_examples(args.data_dir							)
                elif mode == Split.test:
                   lowerCamelCase_						:   Tuple		= self.processor.get_test_examples(args.data_dir							)
                else:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_train_examples(args.data_dir							)
                if limit_length is not None:
                   lowerCamelCase_						:   Dict		= examples[:limit_length]
                lowerCamelCase_						:   Union[str, Any]		= glue_convert_examples_to_features(
                    __a		,				__a		,				max_length=args.max_seq_length		,				label_list=__a		,				output_mode=self.output_mode		,				)
                lowerCamelCase_						:   Optional[Any]		= time.time()
                torch.save(self.features		,				__a							)
                # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                logger.info(
                    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''							)



       def __len__(						self				:				Any							)					->Any:
          return len(self.features							)



       def __getitem__(						self				:				List[Any]		,				__a				:				Optional[int]							)					->InputFeatures:
          return self.features[i]




       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[int]:
          return self.label_list





