


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE			: str				   =					logging.get_logger(__name__)


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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		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:
        _lowercase			:				Tuple 						=						TOKENIZER_CLASSES
    else:
        _lowercase			:				Union[str, Any] 						=						{tokenizer_name: getattr(lowerCamelCase_				,					tokenizer_name + 'Fast'					)}

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

    for tokenizer_name in tokenizer_names:
        _lowercase			:				Union[str, Any] 						=						TOKENIZER_CLASSES[tokenizer_name]

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

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

            _lowercase			:				str 						=						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__":
 SCREAMING_SNAKE_CASE			: Any				   =					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.",
 )
 SCREAMING_SNAKE_CASE			: List[Any]				   =					parser.parse_args()

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__				:	List[Any]					       =					logging.get_logger(__name__)

snake_case__				:	str					       =					{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"dpr"



       def __init__(						self				:				Union[str, Any]		,				__a				:				Optional[Any]=30_522		,				__a				:				List[Any]=768		,				__a				:				List[Any]=12		,				__a				:				Dict=12		,				__a				:				Union[str, Any]=3_072		,				__a				:				Any="gelu"		,				__a				:				Any=0.1		,				__a				:				Any=0.1		,				__a				:				Tuple=512		,				__a				:				int=2		,				__a				:				Optional[Any]=0.02		,				__a				:				List[Any]=1e-12		,				__a				:				int=0		,				__a				:				int="absolute"		,				__a				:				int = 0		,				**__a				:				Optional[int]		,				)					->Tuple:
          super().__init__(pad_token_id=__a		,				**__a							)

          lowerCamelCase_						:   str		= vocab_size
          lowerCamelCase_						:   List[Any]		= hidden_size
          lowerCamelCase_						:   Optional[int]		= num_hidden_layers
          lowerCamelCase_						:   Optional[Any]		= num_attention_heads
          lowerCamelCase_						:   List[Any]		= hidden_act
          lowerCamelCase_						:   Optional[Any]		= intermediate_size
          lowerCamelCase_						:   List[Any]		= hidden_dropout_prob
          lowerCamelCase_						:   Any		= attention_probs_dropout_prob
          lowerCamelCase_						:   Any		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= type_vocab_size
          lowerCamelCase_						:   Dict		= initializer_range
          lowerCamelCase_						:   List[Any]		= layer_norm_eps
          lowerCamelCase_						:   List[str]		= projection_dim
          lowerCamelCase_						:   Optional[int]		= position_embedding_type





