



"""simple docstring"""







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

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


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		UpperCAmelCase:  Union[str, Any]			     =				None


UpperCAmelCase:  int			     =				logging.get_logger(__name__)

UpperCAmelCase:  Dict			     =				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase:  List[Any]			     =				{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase:  Tuple			     =				{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       Any						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  ["input_ids", "attention_mask"]
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  XLMRobertaTokenizer






	def __init__(     self				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="<unk>"				,UpperCAmelCase_="<pad>"				,UpperCAmelCase_="<mask>"				,**UpperCAmelCase_				,):
			# Mask token behave like a normal word, i.e. include the space before it
			_lowercase				: Optional[int]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else mask_token

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

			_lowercase				: Tuple     =   vocab_file
			_lowercase				: List[Any]     =   False if not self.vocab_file else True






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			_lowercase				: int     =   [self.cls_token_id]
			_lowercase				: Tuple     =   [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Dict     =   [self.sep_token_id]
			_lowercase				: Dict     =   [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 + sep + token_ids_a + sep   ) * [0]






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			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(UpperCAmelCase_   ):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""   )
					return
			_lowercase				: Union[str, Any]     =   os.path.join(
			    UpperCAmelCase_				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

			return (out_vocab_file,)







import argparse

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


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

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

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

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

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


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


