



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


_lowercase		:				str      		=logging.get_logger(__name__)

_lowercase		:				Any      		={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowercase		:				Tuple      		={
    "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"
        ),
    },
}

_lowercase		:				Union[str, 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,
}

_lowercase		:				Any      		={
    "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        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:List[Any]      =					VOCAB_FILES_NAMES
						__lowerCAmelCase					:List[Any]      =					PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase					:int      =					PRETRAINED_INIT_CONFIGURATION
						__lowerCAmelCase					:Any      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase					:List[Any]      =					RealmTokenizer

						def __init__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=True ,       __lowercase="[UNK]" ,       __lowercase="[SEP]" ,       __lowercase="[PAD]" ,       __lowercase="[CLS]" ,       __lowercase="[MASK]" ,       __lowercase=True ,       __lowercase=None ,       **__lowercase ,       ) ->				Optional[Any]:
									"""simple docstring"""




									super().__init__(
									    __lowercase ,       tokenizer_file=__lowercase ,       do_lower_case=__lowercase ,       unk_token=__lowercase ,       sep_token=__lowercase ,       pad_token=__lowercase ,       cls_token=__lowercase ,       mask_token=__lowercase ,       tokenize_chinese_chars=__lowercase ,       strip_accents=__lowercase ,       **__lowercase ,       )

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

									a__       :						str							=  do_lower_case

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       **__lowercase  ) ->				Tuple:
									"""simple docstring"""




									a__       :						List[Any]							=  PaddingStrategy.MAX_LENGTH

									a__       :						Union[str, Any]							=  text
									a__       :						Optional[Any]							=  kwargs.pop("""text_pair""" ,       __lowercase  )
									a__       :						str							=  kwargs.pop("""return_tensors""" ,       __lowercase  )

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

									for idx, candidate_text in enumerate(__lowercase  ):
												if batch_text_pair is not None:
															a__       :						Tuple							=  batch_text_pair[idx]
												else:
															a__       :						Union[str, Any]							=  None

												a__       :						Tuple							=  super().__call__(__lowercase ,       __lowercase ,       return_tensors=__lowercase ,       **__lowercase  )

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

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

									a__       :						Optional[int]							=  {key: item for key, item in output_data.items() if len(__lowercase  ) != 0}

									return BatchEncoding(__lowercase ,       tensor_type=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=None  ) ->				Dict:
									"""simple docstring"""




									a__       :						Dict							=  [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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[int]:
									"""simple docstring"""




									a__       :						Optional[int]							=  [self.sep_token_id]
									a__       :						str							=  [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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Tuple[str]:
									"""simple docstring"""




									a__       :						List[str]							=  self._tokenizer.model.save(__lowercase ,       name=__lowercase  )
									return tuple(__lowercase  )









_lowercase		:				Optional[int]      		=[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]

def      lowerCAmelCase_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
			a__       :						List[Any]							=  0
			a__       :						Dict							=  0
			while place < len(_lowercase):
						if (place + 1 < len(_lowercase)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def      lowerCAmelCase_					(  _lowercase    :       int)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]							=  []
			for arabic, roman in ROMAN:
						((a__)	,						(a__))       :						Optional[Any]							=  divmod(_lowercase						,       _lowercase)
						result.append(roman * factor)
						if number == 0:
									break
			return "".join(_lowercase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




