





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCAmelCase							:       List[Any]					 =	logging.get_logger(__name__)

lowerCAmelCase							:       Optional[int]					 =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:       Dict					 =	{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase							:       int					 =	{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__     :   int		      =     PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__     :   int		      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''input_ids''', '''attention_mask''']
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     RobertaTokenizer






				def __init__(  self			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, _lowerCAmelCase="replace"			, _lowerCAmelCase="<s>"			, _lowerCAmelCase="</s>"			, _lowerCAmelCase="</s>"			, _lowerCAmelCase="<s>"			, _lowerCAmelCase="<unk>"			, _lowerCAmelCase="<pad>"			, _lowerCAmelCase="<mask>"			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    _lowerCAmelCase			, _lowerCAmelCase			, tokenizer_file=_lowerCAmelCase			, errors=_lowerCAmelCase			, bos_token=_lowerCAmelCase			, eos_token=_lowerCAmelCase			, sep_token=_lowerCAmelCase			, cls_token=_lowerCAmelCase			, unk_token=_lowerCAmelCase			, pad_token=_lowerCAmelCase			, mask_token=_lowerCAmelCase			, add_prefix_space=_lowerCAmelCase			, trim_offsets=_lowerCAmelCase			, **_lowerCAmelCase			, )

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
										if pre_tok_state.get('''add_prefix_space'''			, _lowerCAmelCase					) != add_prefix_space:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   getattr(_lowerCAmelCase			, pre_tok_state.pop('''type'''					)					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   add_prefix_space
																__SCREAMING_SNAKE_CASE:				Any           =   pre_tok_class(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   add_prefix_space

										__SCREAMING_SNAKE_CASE:				str           =   '''post_processor'''
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   getattr(self.backend_tokenizer			, _lowerCAmelCase			, _lowerCAmelCase					)
										if tokenizer_component_instance:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   json.loads(tokenizer_component_instance.__getstate__()					)

																# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																if "sep" in state:
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   tuple(state['''sep''']					)
																if "cls" in state:
																						__SCREAMING_SNAKE_CASE:				Any           =   tuple(state['''cls''']					)

																__SCREAMING_SNAKE_CASE:				List[Any]           =   False

																if state.get('''add_prefix_space'''			, _lowerCAmelCase					) != add_prefix_space:
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   add_prefix_space
																						__SCREAMING_SNAKE_CASE:				int           =   True

																if state.get('''trim_offsets'''			, _lowerCAmelCase					) != trim_offsets:
																						__SCREAMING_SNAKE_CASE:				Dict           =   trim_offsets
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   True

																if changes_to_apply:
																						__SCREAMING_SNAKE_CASE:				Tuple           =   getattr(_lowerCAmelCase			, state.pop('''type'''					)					)
																						__SCREAMING_SNAKE_CASE:				int           =   component_class(**_lowerCAmelCase					)
																						setattr(self.backend_tokenizer			, _lowerCAmelCase			, _lowerCAmelCase					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self._mask_token is None:
																if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''					)
																return None
										return str(self._mask_token					)






				@mask_token.setter
				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   AddedToken(_lowerCAmelCase			, lstrip=_lowerCAmelCase			, rstrip=_lowerCAmelCase					) if isinstance(_lowerCAmelCase			, _lowerCAmelCase					) else value
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   value






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   kwargs.get('''is_split_into_words'''			, _lowerCAmelCase					)
										assert self.add_prefix_space or not is_split_into_words, (
										    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   kwargs.get('''is_split_into_words'''			, _lowerCAmelCase					)

										assert self.add_prefix_space or not is_split_into_words, (
										    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   self._tokenizer.model.save(_lowerCAmelCase			, name=_lowerCAmelCase					)
										return tuple(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   [self.sep_token_id]
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [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  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



