



'''simple docstring'''




import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase							:Optional[Any]				    =					logging.get_logger(__name__)


lowerCamelCase							:List[str]				    =					{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			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:
						A_	:						Optional[Any]	=     TOKENIZER_CLASSES
			else:
						A_	:						List[Any]	=     {tokenizer_name: getattr(lowerCamelCase__						,      tokenizer_name + """Fast"""    )}

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

			for tokenizer_name in tokenizer_names:
						A_	:						int	=     TOKENIZER_CLASSES[tokenizer_name]

						A_	:						Union[str, Any]	=     True
						if checkpoint_name is None:
									A_	:						Optional[int]	=     list(tokenizer_class.max_model_input_sizes.keys()    )
						else:
									A_	:						Dict	=     [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
									A_	:						Optional[Any]	=     tokenizer_class.from_pretrained(lowerCamelCase__						,      force_download=lowerCamelCase__    )

									# 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:
												A_,    A_	:						Optional[Any]	=     checkpoint.split("""/"""    )
												A_	:						Optional[int]	=     os.path.join(lowerCamelCase__						,      lowerCamelCase__    )
									elif add_prefix:
												A_	:						Union[str, Any]	=     checkpoint
												A_	:						List[str]	=     dump_path
									else:
												A_	:						Optional[Any]	=     None
												A_	:						List[str]	=     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]:
												A_	:						int	=     list(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
												A_	:						List[Any]	=     file_path.split(lowerCamelCase__    )[-1][0]
												if next_char == "/":
															A_	:						Any	=     os.path.join(lowerCamelCase__						,      lowerCamelCase__    )
															A_	:						Union[str, Any]	=     None

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

									A_	:						List[Any]	=     tokenizer.save_pretrained(
									    lowerCamelCase__						,      legacy_format=lowerCamelCase__						,      filename_prefix=lowerCamelCase__    )
									logger.info(f'=> File names {file_names}'    )

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


if __name__ == "__main__":
						lowerCamelCase							: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.''',
						)
						lowerCamelCase							:Optional[Any]				    =					parser.parse_args()

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




'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


if __name__ == "__main__":
						fire.Fire(save_len_file)