
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


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

_snake_case					:		str      =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case					:		Optional[int]      =    {
    "vocab_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
        ),
        "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
        "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-openqa": (
            "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-reader": (
            "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-openqa": (
            "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-reader": (
            "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case					:		Optional[Any]      =    {
    "google/realm-cc-news-pretrained-embedder": 512,
    "google/realm-cc-news-pretrained-encoder": 512,
    "google/realm-cc-news-pretrained-scorer": 512,
    "google/realm-cc-news-pretrained-openqa": 512,
    "google/realm-orqa-nq-openqa": 512,
    "google/realm-orqa-nq-reader": 512,
    "google/realm-orqa-wq-openqa": 512,
    "google/realm-orqa-wq-reader": 512,
}

_snake_case					:		Dict      =    {
    "google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-reader": {"do_lower_case": True},
    "google/realm-orqa-wq-openqa": {"do_lower_case": True},
    "google/realm-orqa-wq-reader": {"do_lower_case": True},
}

class     a		(__lowercase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[Any]					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Union[str, Any]					  =  PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase   :							List[str]					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							List[str]					  =  RealmTokenizer

							def __init__(							self	:  str   ,     lowerCamelCase	:  str=None   ,     lowerCamelCase	:  List[str]=None   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="[UNK]"   ,     lowerCamelCase	:  str="[SEP]"   ,     lowerCamelCase	:  Optional[int]="[PAD]"   ,     lowerCamelCase	:  List[str]="[CLS]"   ,     lowerCamelCase	:  Any="[MASK]"   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  Union[str, Any]=None   ,     **lowerCamelCase	:  Tuple   ,     )     ->   Any:
														super().__init__(
														    UpperCAmelCase__   ,     tokenizer_file=UpperCAmelCase__   ,     do_lower_case=UpperCAmelCase__   ,     unk_token=UpperCAmelCase__   ,     sep_token=UpperCAmelCase__   ,     pad_token=UpperCAmelCase__   ,     cls_token=UpperCAmelCase__   ,     mask_token=UpperCAmelCase__   ,     tokenize_chinese_chars=UpperCAmelCase__   ,     strip_accents=UpperCAmelCase__   ,     **UpperCAmelCase__   ,     )

														__snake_case		:      Any										=       json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
														if (
														    normalizer_state.get("lowercase"   ,     UpperCAmelCase__    ) != do_lower_case
														    or normalizer_state.get("strip_accents"   ,     UpperCAmelCase__    ) != strip_accents
														    or normalizer_state.get("handle_chinese_chars"   ,     UpperCAmelCase__    ) != tokenize_chinese_chars
														):
																					__snake_case		:      Any										=       getattr(UpperCAmelCase__   ,     normalizer_state.pop("type"    )    )
																					__snake_case		:      Optional[int]										=       do_lower_case
																					__snake_case		:      int										=       strip_accents
																					__snake_case		:      Optional[Any]										=       tokenize_chinese_chars
																					__snake_case		:      Dict										=       normalizer_class(**UpperCAmelCase__    )

														__snake_case		:      Union[str, Any]										=       do_lower_case

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Dict   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														__snake_case		:      Any										=       PaddingStrategy.MAX_LENGTH

														__snake_case		:      Dict										=       text
														__snake_case		:      Any										=       kwargs.pop("text_pair"   ,     UpperCAmelCase__    )
														__snake_case		:      Tuple										=       kwargs.pop("return_tensors"   ,     UpperCAmelCase__    )

														__snake_case		:      Any										=       {
														    "input_ids": [],
														    "attention_mask": [],
														    "token_type_ids": [],
														}

														for idx, candidate_text in enumerate(UpperCAmelCase__    ):
																					if batch_text_pair is not None:
																												__snake_case		:      Optional[int]										=       batch_text_pair[idx]
																					else:
																												__snake_case		:      Optional[Any]										=       None

																					__snake_case		:      List[Any]										=       super().__call__(UpperCAmelCase__   ,     UpperCAmelCase__   ,     return_tensors=UpperCAmelCase__   ,     **UpperCAmelCase__    )

																					__snake_case		:      List[Any]										=       encoded_candidates.get("input_ids"    )
																					__snake_case		:      Dict										=       encoded_candidates.get("attention_mask"    )
																					__snake_case		:      List[str]										=       encoded_candidates.get("token_type_ids"    )

																					if encoded_input_ids is not None:
																												output_data["input_ids"].append(UpperCAmelCase__    )
																					if encoded_attention_mask is not None:
																												output_data["attention_mask"].append(UpperCAmelCase__    )
																					if encoded_token_type_ids is not None:
																												output_data["token_type_ids"].append(UpperCAmelCase__    )

														__snake_case		:      List[Any]										=       {key: item for key, item in output_data.items() if len(UpperCAmelCase__    ) != 0}

														return BatchEncoding(UpperCAmelCase__   ,     tensor_type=UpperCAmelCase__    )

							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Tuple=None    )     ->   Optional[int]:
														__snake_case		:      int										=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														__snake_case		:      Union[str, Any]										=       [self.sep_token_id]
														__snake_case		:      Optional[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    ) * [0] + len(token_ids_a + sep    ) * [1]






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





def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return x if y == 0 else greatest_common_divisor(__lowerCamelCase							,     x % y     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (x * y) // greatest_common_divisor(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase = 2_0     ):
							__snake_case		:      Optional[Any]										=       1
							for i in range(1							,     n + 1     ):
														__snake_case		:      Any										=       lcm(__lowerCamelCase							,     __lowerCamelCase     )
							return g


if __name__ == "__main__":
			print(f'''{solution() = }''')

