
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  :					Optional[Any]						    =							logging.get_logger(__name__)

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

lowercase  :					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"""
        ),
    },
}

lowercase  :					List[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  :					Tuple						    =							{
    """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		(      lowerCAmelCase     ):
    _a		:						str=				VOCAB_FILES_NAMES
    _a		:						Union[str, Any]=				PRETRAINED_VOCAB_FILES_MAP
    _a		:						Tuple=				PRETRAINED_INIT_CONFIGURATION
    _a		:						Optional[int]=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						List[Any]=				RealmTokenizer
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=True		,snake_case="[UNK]"		,snake_case="[SEP]"		,snake_case="[PAD]"		,snake_case="[CLS]"		,snake_case="[MASK]"		,snake_case=True		,snake_case=None		,**snake_case		,):



           '''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		,)

           lowercase							:     int       	=      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
           ):
                  lowercase							:     Union[str, Any]       	=      getattr(snake_case		,normalizer_state.pop("""type"""      )      )
                  lowercase							:     str       	=      do_lower_case
                  lowercase							:     str       	=      strip_accents
                  lowercase							:     Optional[Any]       	=      tokenize_chinese_chars
                  lowercase							:     Tuple       	=      normalizer_class(**snake_case      )

           lowercase							:     List[str]       	=      do_lower_case
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      PaddingStrategy.MAX_LENGTH

           lowercase							:     int       	=      text
           lowercase							:     int       	=      kwargs.pop("""text_pair"""		,snake_case      )
           lowercase							:     List[str]       	=      kwargs.pop("""return_tensors"""		,snake_case      )

           lowercase							:     Optional[int]       	=      {
               """input_ids""": [],
               """attention_mask""": [],
               """token_type_ids""": [],
           }

           for idx, candidate_text in enumerate(snake_case      ):
                  if batch_text_pair is not None:
                         lowercase							:     str       	=      batch_text_pair[idx]
                  else:
                         lowercase							:     Optional[Any]       	=      None

                  lowercase							:     Any       	=      super().__call__(snake_case		,snake_case		,return_tensors=snake_case		,**snake_case      )

                  lowercase							:     Tuple       	=      encoded_candidates.get("""input_ids"""      )
                  lowercase							:     Optional[int]       	=      encoded_candidates.get("""attention_mask"""      )
                  lowercase							:     Tuple       	=      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      )

           lowercase							:     Tuple       	=      {key: item for key, item in output_data.items() if len(snake_case      ) != 0}

           return BatchEncoding(snake_case		,tensor_type=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Optional[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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     str       	=      [self.sep_token_id]
           lowercase							:     List[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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self._tokenizer.model.save(snake_case		,name=snake_case      )
           return tuple(snake_case      )


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

       # Validation
       def count_nodes(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left				) + count_nodes(node.right				) + 1

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_coins(node.left				) + count_coins(node.right				) + node.data

       if count_nodes(SCREAMING_SNAKE_CASE__				) != count_coins(SCREAMING_SNAKE_CASE__				):
              raise ValueError("""The nodes number should be same as the number of coins"""				)

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0					,	1				)

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
