


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


_snake_case					:		List[str]      =    logging.get_logger(__name__)

_snake_case					:		int      =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_snake_case					:		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"
        ),
    },
}

_snake_case					:		str      =    {
    "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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Any					  =  ["input_ids", "attention_mask"]
							__UpperCAmelCase   :							Tuple					  =  RobertaTokenizer

							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Any="replace"   ,     lowerCamelCase	:  Optional[Any]="<s>"   ,     lowerCamelCase	:  List[Any]="</s>"   ,     lowerCamelCase	:  List[str]="</s>"   ,     lowerCamelCase	:  Tuple="<s>"   ,     lowerCamelCase	:  Union[str, Any]="<unk>"   ,     lowerCamelCase	:  Union[str, Any]="<pad>"   ,     lowerCamelCase	:  Union[str, Any]="<mask>"   ,     lowerCamelCase	:  Dict=False   ,     lowerCamelCase	:  Tuple=True   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   int:
														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   ,     )

														__snake_case		:      str										=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
														if pre_tok_state.get("add_prefix_space"   ,     lowerCamelCase    ) != add_prefix_space:
																					__snake_case		:      Optional[Any]										=       getattr(lowerCamelCase   ,     pre_tok_state.pop("type"    )    )
																					__snake_case		:      List[str]										=       add_prefix_space
																					__snake_case		:      List[Any]										=       pre_tok_class(**lowerCamelCase    )

														__snake_case		:      str										=       add_prefix_space

														__snake_case		:      List[Any]										=       "post_processor"
														__snake_case		:      int										=       getattr(self.backend_tokenizer   ,     lowerCamelCase   ,     lowerCamelCase    )
														if tokenizer_component_instance:
																					__snake_case		:      Union[str, 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:
																												__snake_case		:      List[str]										=       tuple(state["sep"]    )
																					if "cls" in state:
																												__snake_case		:      List[str]										=       tuple(state["cls"]    )

																					__snake_case		:      int										=       False

																					if state.get("add_prefix_space"   ,     lowerCamelCase    ) != add_prefix_space:
																												__snake_case		:      str										=       add_prefix_space
																												__snake_case		:      Dict										=       True

																					if state.get("trim_offsets"   ,     lowerCamelCase    ) != trim_offsets:
																												__snake_case		:      str										=       trim_offsets
																												__snake_case		:      Optional[Any]										=       True

																					if changes_to_apply:
																												__snake_case		:      Tuple										=       getattr(lowerCamelCase   ,     state.pop("type"    )    )
																												__snake_case		:      Optional[Any]										=       component_class(**lowerCamelCase    )
																												setattr(self.backend_tokenizer   ,     lowerCamelCase   ,     lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   str:
														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	:  Tuple   ,     lowerCamelCase	:  Optional[Any]    )     ->   List[Any]:
														__snake_case		:      Tuple										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else value
														__snake_case		:      Any										=       value

							def    __snake_case (							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   BatchEncoding:
														__snake_case		:      Tuple										=       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	:  Dict   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[int]    )     ->   BatchEncoding:
														__snake_case		:      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	:  List[Any]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Optional[str] = None    )     ->   Tuple[str]:
														__snake_case		:      str										=       self._tokenizer.model.save(lowerCamelCase   ,     name=lowerCamelCase    )
														return tuple(lowerCamelCase    )

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  str=None    )     ->   Union[str, Any]:
														__snake_case		:      Optional[Any]										=       [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	:  str   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														__snake_case		:      Union[str, Any]										=       [self.sep_token_id]
														__snake_case		:      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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if index == r:
														for j in range(__lowerCamelCase     ):
																					print(data[j]							,     end=" "     )
														print(" "     )
														return
							#  When no more elements are there to put in data[]
							if i >= n:
														return
							# current is included, put next at next location
							__snake_case		:      Union[str, Any]										=       arr[i]
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     index + 1							,     __lowerCamelCase							,     i + 1     )
							# current is excluded, replace it with
							# next (Note that i+1 is passed, but
							# index is not changed)
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     i + 1     )
							# The main function that prints all combinations
							# of size r in arr[] of size n. This function
							# mainly uses combinationUtil()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# A temporary array to store all combination one by one
							__snake_case		:      Union[str, Any]										=       [0] * r
							# Print all combination using temporary array 'data[]'
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     0							,     __lowerCamelCase							,     0     )


if __name__ == "__main__":
			# Driver code to check the function above
			_snake_case					:		List[str]      =    [10, 20, 30, 40, 50]
			print_combination(arr, len(arr), 3)
			# This code is contributed by Ambuj sahu

