
'''simple docstring'''
from typing import List, Optional, Tuple

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


lowerCAmelCase					:    List[str]           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[Any]           = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase					:    Any           = {
    """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"""
    },
}

lowerCAmelCase					:    str           = {"""allegro/herbert-base-cased""": 5_14}
lowerCAmelCase					:    Union[str, Any]           = {}


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):
				lowerCAmelCase_     			=							VOCAB_FILES_NAMES
				lowerCAmelCase_     			=							PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase_     			=							PRETRAINED_INIT_CONFIGURATION
				lowerCAmelCase_     			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase_     			=							HerbertTokenizer







				def __init__(       self						,				A_=None						,				A_=None						,				A_=None						,				A_="<s>"						,				A_="<unk>"						,				A_="<pad>"						,				A_="<mask>"						,				A_="</s>"						,				**A_						,				)->		Optional[int]:







											'''simple docstring'''






											super().__init__(
											    _UpperCAmelCase						,				_UpperCAmelCase						,				tokenizer_file=_UpperCAmelCase						,				cls_token=_UpperCAmelCase						,				unk_token=_UpperCAmelCase						,				pad_token=_UpperCAmelCase						,				mask_token=_UpperCAmelCase						,				sep_token=_UpperCAmelCase						,				**_UpperCAmelCase						,				)







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											UpperCamelCase   			=     [self.cls_token_id]
											UpperCamelCase   			=     [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						,				A_						,				A_ = None						,				A_ = False     )->		List[int]:







											'''simple docstring'''






											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=_UpperCAmelCase						,				token_ids_a=_UpperCAmelCase						,				already_has_special_tokens=_UpperCAmelCase     )

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											UpperCamelCase   			=     [self.sep_token_id]
											UpperCamelCase   			=     [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						,				A_						,				A_ = None     )->		Tuple[str]:







											'''simple docstring'''






											UpperCamelCase   			=     self._tokenizer.model.save(_UpperCAmelCase						,				name=_UpperCAmelCase     )
											return tuple(_UpperCAmelCase     )












import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   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(
     """--mobilebert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained MobileBERT 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."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





