import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		__lowerCamelCase :     str          =  None


__lowerCamelCase :     Optional[int]          =  logging.get_logger(__name__)

__lowerCamelCase :     int          =  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase :     Dict          =  {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase :     str          =  {
    '''facebook/xglm-564M''': 20_48,
}






class 		a__						( A__ ):
					A     	=							VOCAB_FILES_NAMES
					A     	=							PRETRAINED_VOCAB_FILES_MAP
					A     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A     	=							['input_ids', 'attention_mask']
					A     	=							XGLMTokenizer

					def __init__(   self				:	Dict,_A				:	int=None,_A				:	Dict=None,_A				:	Optional[Any]="<s>",_A				:	Optional[Any]="</s>",_A				:	Dict="</s>",_A				:	Union[str, Any]="<s>",_A				:	Tuple="<unk>",_A				:	int="<pad>",**_A				:	Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   7
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   [F'<madeupword{i}>' for i in range(self.num_madeup_words   )]

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   kwargs.get("additional_special_tokens",[]   )
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    _A,tokenizer_file=_A,bos_token=_A,eos_token=_A,sep_token=_A,cls_token=_A,unk_token=_A,pad_token=_A,**_A,)

							SCREAMING_SNAKE_CASE_  :		Any    				=   vocab_file
							SCREAMING_SNAKE_CASE_  :		int    				=   False if not self.vocab_file else True

					def     __UpperCamelCase       (   self				:	List[str],_A				:	List[int],_A				:	Optional[List[int]] = None   ):







							"""simple docstring"""






							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							SCREAMING_SNAKE_CASE_  :		Dict    				=   [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

					def     __UpperCamelCase       (   self				:	Dict,_A				:	List[int],_A				:	Optional[List[int]] = None   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   [self.sep_token_id]

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






					def     __UpperCamelCase       (   self				:	str,_A				:	str,_A				:	Optional[str] = None   ):







							"""simple docstring"""






							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."   )

							if not os.path.isdir(_A   ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'   )
									return
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   os.path.join(
							    _A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(_A   ):
									copyfile(self.vocab_file,_A   )

							return (out_vocab_file,)

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


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