from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase							 =					{'''allegro/herbert-base-cased''': 514}
__lowercase							 =					{}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Dict						=      VOCAB_FILES_NAMES
    a__	:						Tuple						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						Optional[Any]						=      PRETRAINED_INIT_CONFIGURATION
    a__	:						Any						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Optional[Any]						=      HerbertTokenizer







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase="</s>" , **__lowercase , )       ->	Optional[int]:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , sep_token=__lowercase , **__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[int]:
        __UpperCamelCase     :Union[str, Any]			  =		[self.cls_token_id]
        __UpperCamelCase     :Optional[Any]			  =		[self.sep_token_id]
        if token_ids_a is None:
            return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = False)       ->	List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[int]:
        __UpperCamelCase     :List[Any]			  =		[self.sep_token_id]
        __UpperCamelCase     :Any			  =		[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) * [0] + len(token_ids_a + sep) * [1]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        __UpperCamelCase     :Optional[int]			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE	)
    print(f"""Building PyTorch model from configuration: {config}"""	)
    __UpperCamelCase     :List[str]			  =		AlbertForPreTraining(SCREAMING_SNAKE_CASE	)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
    torch.save(model.state_dict()  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--albert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained ALBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





