"""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__							:List[str]       =  None


UpperCamelCase__							:str       =  logging.get_logger(__name__)

UpperCamelCase__							:List[Any]       =  {"""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__							:Dict       =  {
    """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(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       VOCAB_FILES_NAMES
		A         =       PRETRAINED_VOCAB_FILES_MAP
		A         =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A         =       ["input_ids", "attention_mask"]
		A         =       XLMRobertaTokenizer
		def __init__(   self				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__="<s>"				,							SCREAMING_SNAKE_CASE__="</s>"				,							SCREAMING_SNAKE_CASE__="</s>"				,							SCREAMING_SNAKE_CASE__="<s>"				,							SCREAMING_SNAKE_CASE__="<unk>"				,							SCREAMING_SNAKE_CASE__="<pad>"				,							SCREAMING_SNAKE_CASE__="<mask>"				,							**SCREAMING_SNAKE_CASE__				,							)			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				: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_				,							)

					_UpperCamelCase				:Any				    =  vocab_file
					_UpperCamelCase				:List[Any]				    =  False if not self.vocab_file else True
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  Any:

					"""simple docstring"""



					if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_UpperCamelCase				:List[Any]				    =  [self.cls_token_id]
					_UpperCamelCase				:str				    =  [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  [self.sep_token_id]
					_UpperCamelCase				: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      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  List[str]:

					"""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(SCREAMING_SNAKE_CASE_      ):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory."      )
								return
					_UpperCamelCase				:List[str]				    =  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,)





"""simple docstring"""






def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:Tuple				    =  word.upper()
			_UpperCamelCase				:Dict				    =  0
			_UpperCamelCase				:Union[str, Any]				    =  {}
			_UpperCamelCase				:Optional[int]				    =  []

			for letter in word:
						if letter not in letter_nums:
									_UpperCamelCase				:Dict				    =  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
					import pprint
					import time

					UpperCamelCase__							:Dict       =  time.time()
					with open("""dictionary.txt""") as in_file:
										UpperCamelCase__							:str       =  in_file.read().splitlines()

					UpperCamelCase__							:dict       =  {}
					for word in word_list:
										UpperCamelCase__							:List[Any]       =  get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															UpperCamelCase__							:Tuple       =  [word]

					with open("""word_patterns.txt""", """w""") as out_file:
										out_file.write(pprint.pformat(all_patterns))

					UpperCamelCase__							:Dict       =  round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.

