






"""simple docstring"""



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


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

_lowerCAmelCase	:	int				=      {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase	:	Any				=      {
    """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"""
        ),
    },
}

_lowerCAmelCase	:	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,
}

_lowerCAmelCase	:	Optional[int]				=      {
    """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 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_INIT_CONFIGURATION
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_    =RealmTokenizer
   def __init__(		self      :	Any							,			snake_case__      :	Tuple=None							,			snake_case__      :	Optional[Any]=None							,			snake_case__      :	Union[str, Any]=True							,			snake_case__      :	List[str]="[UNK]"							,			snake_case__      :	Optional[Any]="[SEP]"							,			snake_case__      :	Tuple="[PAD]"							,			snake_case__      :	Optional[Any]="[CLS]"							,			snake_case__      :	Any="[MASK]"							,			snake_case__      :	int=True							,			snake_case__      :	Optional[Any]=None							,			**snake_case__      :	Optional[int]							,			):


        '''simple docstring'''
        super().__init__(
            snake_case__							,			tokenizer_file=snake_case__							,			do_lower_case=snake_case__							,			unk_token=snake_case__							,			sep_token=snake_case__							,			pad_token=snake_case__							,			cls_token=snake_case__							,			mask_token=snake_case__							,			tokenize_chinese_chars=snake_case__							,			strip_accents=snake_case__							,			**snake_case__							,			)

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

        UpperCAmelCase__						:    List[str]							=							do_lower_case
   def       __a							(		self      :	int							,			snake_case__      :	Dict							,			**snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							PaddingStrategy.MAX_LENGTH

        UpperCAmelCase__						:    Any							=							text
        UpperCAmelCase__						:    Dict							=							kwargs.pop("text_pair"							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.pop("return_tensors"							,			snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							{
            "input_ids": [],
            "attention_mask": [],
            "token_type_ids": [],
        }

        for idx, candidate_text in enumerate(snake_case__							):
             if batch_text_pair is not None:
                  UpperCAmelCase__						:    str							=							batch_text_pair[idx]
             else:
                  UpperCAmelCase__						:    Dict							=							None

             UpperCAmelCase__						:    List[str]							=							super().__call__(snake_case__							,			snake_case__							,			return_tensors=snake_case__							,			**snake_case__							)

             UpperCAmelCase__						:    Dict							=							encoded_candidates.get("input_ids"							)
             UpperCAmelCase__						:    Tuple							=							encoded_candidates.get("attention_mask"							)
             UpperCAmelCase__						:    Dict							=							encoded_candidates.get("token_type_ids"							)

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

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

        return BatchEncoding(snake_case__							,			tensor_type=snake_case__							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Union[str, Any]=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							[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       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[int]							,			snake_case__      :	Optional[List[int]] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							[self.sep_token_id]
        UpperCAmelCase__						:    Tuple							=							[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       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							self._tokenizer.model.save(snake_case__							,			name=snake_case__							)
        return tuple(snake_case__							)










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


