


"""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


SCREAMING_SNAKE_CASE					:       Tuple							   =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE					:       Any							   =							{
    """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"""
        ),
    },
}

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							{
    """microsoft/layoutlm-base-uncased""": 512,
    """microsoft/layoutlm-large-uncased""": 512,
}

SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}



class      _UpperCAmelCase					(				__lowercase							):






		'''simple docstring'''

		lowerCamelCase__           =VOCAB_FILES_NAMES
		lowerCamelCase__           =PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__           =PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase__           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__           =LayoutLMTokenizer



		def __init__(self							,			a_=None							,			a_=None							,			a_=True							,			a_="[UNK]"							,			a_="[SEP]"							,			a_="[PAD]"							,			a_="[CLS]"							,			a_="[MASK]"							,			a_=True							,			a_=None							,			**a_							,			):





			'''simple docstring'''

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

			__snake_case      :		Optional[Any]         =							json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
			if (
			    normalizer_state.get('''lowercase'''							,			A_   ) != do_lower_case
			    or normalizer_state.get('''strip_accents'''							,			A_   ) != strip_accents
			    or normalizer_state.get('''handle_chinese_chars'''							,			A_   ) != tokenize_chinese_chars
			):
				__snake_case      :		str         =							getattr(A_							,			normalizer_state.pop('''type'''   )   )
				__snake_case      :		Optional[Any]         =							do_lower_case
				__snake_case      :		Tuple         =							strip_accents
				__snake_case      :		List[Any]         =							tokenize_chinese_chars
				__snake_case      :		Optional[int]         =							normalizer_class(**A_   )

			__snake_case      :		int         =							do_lower_case



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_=None   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							[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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							[self.sep_token_id]
			__snake_case      :		int         =							[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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							self._tokenizer.model.save(A_							,			name=A_   )
			return tuple(A_   )






import argparse

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


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					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
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			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.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)