"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__magic_name__    				=					{
    "vocab_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__magic_name__    				=					{
    "microsoft/layoutlm-base-uncased": 512,
    "microsoft/layoutlm-large-uncased": 512,
}

__magic_name__    				=					{
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}

class SCREAMING_SNAKE_CASE     (    lowerCamelCase__	):







 """simple docstring"""


 a_       :	List[str]  			=VOCAB_FILES_NAMES
 a_       :	Dict  			=PRETRAINED_VOCAB_FILES_MAP
 a_       :	List[Any]  			=PRETRAINED_INIT_CONFIGURATION
 a_       :	Tuple  			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a_       :	Optional[Any]  			=LayoutLMTokenizer


 def __init__(   self		:      Optional[int]					,   _snake_case		:      Optional[int]=None					,   _snake_case		:      List[str]=None					,   _snake_case		:      List[str]=True					,   _snake_case		:      Union[str, Any]="[UNK]"					,   _snake_case		:      Tuple="[SEP]"					,   _snake_case		:      List[Any]="[PAD]"					,   _snake_case		:      Dict="[CLS]"					,   _snake_case		:      Optional[int]="[MASK]"					,   _snake_case		:      Optional[int]=True					,   _snake_case		:      Optional[int]=None					,   **_snake_case		:      List[Any]					,   )				->			Tuple:




    '''simple docstring'''






    super().__init__(
        lowercase_					,   tokenizer_file=lowercase_					,   do_lower_case=lowercase_					,   unk_token=lowercase_					,   sep_token=lowercase_					,   pad_token=lowercase_					,   cls_token=lowercase_					,   mask_token=lowercase_					,   tokenize_chinese_chars=lowercase_					,   strip_accents=lowercase_					,   **lowercase_					,   )

    a__     				=		json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
    if (
        normalizer_state.get('lowercase'					,   lowercase_				) != do_lower_case
        or normalizer_state.get('strip_accents'					,   lowercase_				) != strip_accents
        or normalizer_state.get('handle_chinese_chars'					,   lowercase_				) != tokenize_chinese_chars
    ):
       a__     				=		getattr(lowercase_					,   normalizer_state.pop('type'				)				)
       a__     				=		do_lower_case
       a__     				=		strip_accents
       a__     				=		tokenize_chinese_chars
       a__     				=		normalizer_class(**lowercase_				)

    a__     				=		do_lower_case


 def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      Optional[int]					,   _snake_case		:      Optional[Any]=None				)				->			int:




    '''simple docstring'''






    a__     				=		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

    return output


 def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      List[str]					,   _snake_case		:      int = None				)				->			List[int]:




    '''simple docstring'''






    a__     				=		[self.sep_token_id]
    a__     				=		[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        _lowerCAmelCase				(   self		:      Union[str, Any]					,   _snake_case		:      Union[str, Any]					,   _snake_case		:      Any = None				)				->			Tuple[str]:




    '''simple docstring'''






    a__     				=		self._tokenizer.model.save(lowercase_					,   name=lowercase_				)
    return tuple(lowercase_				)



"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


