


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:
   _A		:					Optional[Any]          =							None


_A		:					Tuple          =							logging.get_logger(__name__)

_A		:					Tuple          =							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_A		:					Optional[int]          =							{
    '''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'''
        ),
    },
}

_A		:					Optional[int]          =							{
    '''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 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Optional[int]       =						VOCAB_FILES_NAMES
     UpperCAmelCase__			:	str       =						PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__			:	Optional[int]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__			:	Union[str, Any]       =						["input_ids", "attention_mask"]
     UpperCAmelCase__			:	str       =						XLMRobertaTokenizer





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

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

        __lowerCamelCase:					Dict	 =						vocab_file
        __lowerCamelCase:					Union[str, Any]	 =						False if not self.vocab_file else True





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					List[int]     ,						SCREAMING_SNAKE_CASE_       :					Optional[List[int]] = None				):

        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __lowerCamelCase:					Optional[Any]	 =						[self.cls_token_id]
        __lowerCamelCase:					str	 =						[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					List[int]     ,						SCREAMING_SNAKE_CASE_       :					Optional[List[int]] = None				):
        __lowerCamelCase:					Optional[Any]	 =						[self.sep_token_id]
        __lowerCamelCase:					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 + sep + token_ids_a + sep				) * [0]







     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Optional[str] = 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(SCREAMING_SNAKE_CASE_				):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory.'				)
           return
        __lowerCamelCase:					Optional[Any]	 =						os.path.join(
            SCREAMING_SNAKE_CASE_     ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

        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)


