





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


UpperCAmelCase_												=   logging.get_logger(__name__)

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

UpperCAmelCase_												=   {
    """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"""
        ),
    },
}

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

UpperCAmelCase_												=   {
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}
class 	UpperCamelCase_						(     __UpperCAmelCase    ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		LayoutLMTokenizer




					def __init__(						self ,  lowerCAmelCase_=None ,  lowerCAmelCase_=None ,  lowerCAmelCase_=True ,  lowerCAmelCase_="[UNK]" ,  lowerCAmelCase_="[SEP]" ,  lowerCAmelCase_="[PAD]" ,  lowerCAmelCase_="[CLS]" ,  lowerCAmelCase_="[MASK]" ,  lowerCAmelCase_=True ,  lowerCAmelCase_=None ,  **lowerCAmelCase_ ,  )						->							List[Any]:
										super().__init__(
										    _lowerCamelCase ,  tokenizer_file=_lowerCamelCase ,  do_lower_case=_lowerCamelCase ,  unk_token=_lowerCamelCase ,  sep_token=_lowerCamelCase ,  pad_token=_lowerCamelCase ,  cls_token=_lowerCamelCase ,  mask_token=_lowerCamelCase ,  tokenize_chinese_chars=_lowerCamelCase ,  strip_accents=_lowerCamelCase ,  **_lowerCamelCase ,  )

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

										_snake_case													= do_lower_case




					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_ ,  lowerCAmelCase_=None     )						->							Optional[Any]:
										_snake_case													= [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 ,  lowerCAmelCase_ ,  lowerCAmelCase_ = None     )						->							Optional[int]:
										_snake_case													= [self.sep_token_id]
										_snake_case													= [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 ,  lowerCAmelCase_ ,  lowerCAmelCase_ = None     )						->							Tuple:
										_snake_case													= self._tokenizer.model.save(_lowerCamelCase ,  name=_lowerCamelCase     )
										return tuple(_lowerCamelCase     )










"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	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(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

