





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

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


_UpperCAmelCase				:       Optional[int]	   =				{name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:


    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__    :      str					 =		TOKENIZER_CLASSES
    else:
        lowerCamelCase__    :      List[Any]					 =		{tokenizer_name: getattr(__A						,	tokenizer_name + 'Fast' )}

    logger.info(F"""Loading tokenizer classes: {tokenizer_names}""" )

    for tokenizer_name in tokenizer_names:
        lowerCamelCase__    :      Optional[int]					 =		TOKENIZER_CLASSES[tokenizer_name]

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

                logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )

            lowerCamelCase__    :      str					 =		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__":
    _UpperCAmelCase				:       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.""",
    )
    _UpperCAmelCase				:       Union[str, Any]	   =				parser.parse_args()

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











import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class __snake_case			(			a		):
   UpperCAmelCase__  :    Dict          =  ['''image_processor''', '''tokenizer''']
   UpperCAmelCase__  :    Dict          =  '''FlavaImageProcessor'''
   UpperCAmelCase__  :    Dict          =  ('''BertTokenizer''', '''BertTokenizerFast''')







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]=None					,						_snake_case		:						str=None					,						**_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''					,						_snake_case					,						)
             UpperCAmelCase_          =							kwargs.pop('''feature_extractor''')

        UpperCAmelCase_          =							image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.''')
        if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.''')

        super().__init__(_snake_case					,						_snake_case)
        UpperCAmelCase_          =							self.image_processor







   def __call__(      self		:						List[Any]					,						_snake_case		:						Optional[ImageInput] = None					,						_snake_case		:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None					,						_snake_case		:						bool = True					,						_snake_case		:						Union[bool, str, PaddingStrategy] = False					,						_snake_case		:						Union[bool, str, TruncationStrategy] = False					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						int = 0					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = True					,						_snake_case		:						Optional[Union[str, TensorType]] = None					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        if text is None and images is None:
             raise ValueError('''You have to specify either text or images. Both cannot be none.''')

        if text is not None:
             UpperCAmelCase_          =							self.tokenizer(
                 text=_snake_case					,						add_special_tokens=_snake_case					,						padding=_snake_case					,						truncation=_snake_case					,						max_length=_snake_case					,						stride=_snake_case					,						pad_to_multiple_of=_snake_case					,						return_token_type_ids=_snake_case					,						return_attention_mask=_snake_case					,						return_overflowing_tokens=_snake_case					,						return_special_tokens_mask=_snake_case					,						return_offsets_mapping=_snake_case					,						return_length=_snake_case					,						verbose=_snake_case					,						return_tensors=_snake_case					,						**_snake_case					,						)
        if images is not None:
             UpperCAmelCase_          =							self.image_processor(
                 _snake_case					,						return_image_mask=_snake_case					,						return_codebook_pixels=_snake_case					,						return_tensors=_snake_case					,						**_snake_case					,						)

        if text is not None and images is not None:
             encoding.update(_snake_case)
             return encoding
        elif text is not None:
             return encoding
        else:
             return BatchEncoding(data=dict(**_snake_case)					,						tensor_type=_snake_case)







   def lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Optional[Any]					,						**_snake_case		:						int):

        """simple docstring"""


        return self.tokenizer.batch_decode(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]					,						*_snake_case		:						int					,						**_snake_case		:						Dict):

        """simple docstring"""


        return self.tokenizer.decode(*_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer.model_input_names
        UpperCAmelCase_          =							self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,						_snake_case					,						)
        return self.image_processor_class







   @property
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,						_snake_case					,						)
        return self.image_processor






