



"""simple docstring"""




class      UpperCAmelCase_  :



      def __init__(					self					:     int					,     A					:     Any = ""					,     A					:     Any = False						):
           _UpperCAmelCase      :     Dict        =   {}

           # A node will be a leaf if the tree contains its word
           _UpperCAmelCase      :     int        =   is_leaf

           _UpperCAmelCase      :     int        =   prefix



      def 						snake_case_ (					self					:     Any					,     A					:     Tuple						):
           _UpperCAmelCase      :     List[str]        =   0
           for q, w in zip(self.prefix					,     A						):
                if q != w:
                     break

                x += 1

           return self.prefix[:x], self.prefix[x:], word[x:]



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     Tuple						):







           for word in words:
                self.insert(A						)



      def 						snake_case_ (					self					:     str					,     A					:     List[Any]						):







           if self.prefix == word:
                _UpperCAmelCase      :     List[Any]        =   True

           # Case 2: The node has no edges that have a prefix to the word
           # Solution: We create an edge from the current node to a new one
           # containing the word
           elif word[0] not in self.nodes:
                _UpperCAmelCase      :     Dict        =   RadixNode(prefix=A					,     is_leaf=A						)

           else:
                _UpperCAmelCase      :     Dict        =   self.nodes[word[0]]
                _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   incoming_node.match(
                    A						)

                # Case 3: The node prefix is equal to the matching
                # Solution: We insert remaining word on the next node
                if remaining_prefix == "":
                     self.nodes[matching_string[0]].insert(A						)

                # Case 4: The word is greater equal to the matching
                # Solution: Create a node in between both nodes, change
                # prefixes and add the new node for the remaining word
                else:
                     _UpperCAmelCase      :     Any        =   remaining_prefix

                     _UpperCAmelCase      :     Dict        =   self.nodes[matching_string[0]]
                     _UpperCAmelCase      :     List[Any]        =   RadixNode(A					,     A						)
                     _UpperCAmelCase      :     str        =   aux_node

                     if remaining_word == "":
                          _UpperCAmelCase      :     int        =   True
                     else:
                          self.nodes[matching_string[0]].insert(A						)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     List[Any]						):
           _UpperCAmelCase      :     Optional[int]        =   self.nodes.get(word[0]					,     A						)
           if not incoming_node:
                return False
           else:
                _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   incoming_node.match(
                    A						)
                # If there is remaining prefix, the word can't be on the tree
                if remaining_prefix != "":
                     return False
                # This applies when the word and the prefix are equal
                elif remaining_word == "":
                     return incoming_node.is_leaf
                # We have word remaining so we check the next node
                else:
                     return incoming_node.find(A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     Any						):
           _UpperCAmelCase      :     Optional[int]        =   self.nodes.get(word[0]					,     A						)
           if not incoming_node:
                return False
           else:
                _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   incoming_node.match(
                    A						)
                # If there is remaining prefix, the word can't be on the tree
                if remaining_prefix != "":
                     return False
                # We have word remaining so we check the next node
                elif remaining_word != "":
                     return incoming_node.delete(A						)
                else:
                     # If it is not a leaf, we don't have to delete
                     if not incoming_node.is_leaf:
                          return False
                     else:
                          # We delete the nodes if no edges go from it
                          if len(incoming_node.nodes						) == 0:
                               del self.nodes[word[0]]
                               # We merge the current node with its only child
                               if len(self.nodes						) == 1 and not self.is_leaf:
                                    _UpperCAmelCase      :     Dict        =   list(self.nodes.values()						)[0]
                                    _UpperCAmelCase      :     Optional[int]        =   merging_node.is_leaf
                                    self.prefix += merging_node.prefix
                                    _UpperCAmelCase      :     Tuple        =   merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                          elif len(incoming_node.nodes						) > 1:
                               _UpperCAmelCase      :     int        =   False
                          # If there is 1 edge, we merge it with its child
                          else:
                               _UpperCAmelCase      :     Optional[int]        =   list(incoming_node.nodes.values()						)[0]
                               _UpperCAmelCase      :     List[Any]        =   merging_node.is_leaf
                               incoming_node.prefix += merging_node.prefix
                               _UpperCAmelCase      :     List[Any]        =   merging_node.nodes

                          return True



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     str = 0						):







           if self.prefix != "":
                print("-" * height					,     self.prefix					,     "  (leaf)" if self.is_leaf else ""						)

           for value in self.nodes.values():
                value.print_tree(height + 1						)







def 						__snake_case						(				)  ->       bool:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   "banana bananas bandana band apple all beast".split()
     _UpperCAmelCase      :     Optional[int]        =   RadixNode()
     root.insert_many(__snake_case    )

     assert all(root.find(__snake_case    ) for word in words    )
     assert not root.find("bandanas"    )
     assert not root.find("apps"    )
     root.delete("all"    )
     assert not root.find("all"    )
     root.delete("banana"    )
     assert not root.find("banana"    )
     assert root.find("bananas"    )

     return True

def 						__snake_case						(				)  ->       None:





     '''simple docstring'''

     assert test_trie()

def 						__snake_case						(				)  ->       None:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   RadixNode()
     _UpperCAmelCase      :     List[str]        =   "banana bananas bandanas bandana band apple all beast".split()
     root.insert_many(__snake_case    )

     print("Words:"						,  __snake_case    )
     print("Tree:"    )
     root.print_tree()


if __name__ == "__main__":
 main()





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_lowerCAmelCase    : List[str]							=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase    : int							=							{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase    : List[Any]							=							{
    "google/electra-small-generator": 5_12,
    "google/electra-base-generator": 5_12,
    "google/electra-large-generator": 5_12,
    "google/electra-small-discriminator": 5_12,
    "google/electra-base-discriminator": 5_12,
    "google/electra-large-discriminator": 5_12,
}

_lowerCAmelCase    : Optional[Any]							=							{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       Any								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_INIT_CONFIGURATION
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Tuple								=			ElectraTokenizer



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

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

           _UpperCAmelCase      :     int        =   do_lower_case



      def 						snake_case_ (					self					:     Tuple					,     A					:     str					,     A					:     int=None						):
           _UpperCAmelCase      :     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 						snake_case_ (					self					:     Any					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Any        =   [self.sep_token_id]
           _UpperCAmelCase      :     Dict        =   [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					:     Any					,     A					:     str					,     A					:     Optional[str] = None						):
           _UpperCAmelCase      :     List[Any]        =   self._tokenizer.model.save(A					,     name=A						)
           return tuple(A						)
