import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


lowercase_   				=       logging.get_logger(__name__)

lowercase_   				=       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_   				=       {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowercase_   				=       {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

lowercase_   				=       {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}







class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase  ):
       A				:     Union[str, Any]					    =     VOCAB_FILES_NAMES
       A				:     Tuple					    =     PRETRAINED_VOCAB_FILES_MAP
       A				:     Any					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A				:     Tuple					    =     ["input_ids", "attention_mask", "token_type_ids"]
       A				:     Any					    =     DebertaTokenizer

       def __init__(				self :			Optional[int]       ,       _lowerCAmelCase :			int=None       ,       _lowerCAmelCase :			str=None       ,       _lowerCAmelCase :			Dict=None       ,       _lowerCAmelCase :			Tuple="replace"       ,       _lowerCAmelCase :			List[str]="[CLS]"       ,       _lowerCAmelCase :			Dict="[SEP]"       ,       _lowerCAmelCase :			int="[SEP]"       ,       _lowerCAmelCase :			Any="[CLS]"       ,       _lowerCAmelCase :			Dict="[UNK]"       ,       _lowerCAmelCase :			Dict="[PAD]"       ,       _lowerCAmelCase :			Optional[int]="[MASK]"       ,       _lowerCAmelCase :			List[str]=False       ,       **_lowerCAmelCase :			int       ,       ):
           super().__init__(
               _lowerCAmelCase       ,       _lowerCAmelCase       ,       tokenizer_file=_lowerCAmelCase       ,       errors=_lowerCAmelCase       ,       bos_token=_lowerCAmelCase       ,       eos_token=_lowerCAmelCase       ,       unk_token=_lowerCAmelCase       ,       sep_token=_lowerCAmelCase       ,       cls_token=_lowerCAmelCase       ,       pad_token=_lowerCAmelCase       ,       mask_token=_lowerCAmelCase       ,       add_prefix_space=_lowerCAmelCase       ,       **_lowerCAmelCase       ,       )
           __snake_case    :							str             =  kwargs.pop("""add_bos_token"""       ,       _lowerCAmelCase       )

           __snake_case    :							Dict             =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""       ,       _lowerCAmelCase       ) != add_prefix_space:
               __snake_case    :							Union[str, Any]             =  getattr(_lowerCAmelCase       ,       pre_tok_state.pop("""type"""       )       )
               __snake_case    :							List[str]             =  add_prefix_space
               __snake_case    :							Any             =  pre_tok_class(**_lowerCAmelCase       )

           __snake_case    :							Tuple             =  add_prefix_space

       @property
       def        snake_case__  (				self :			Any       ):
           if self._mask_token is None:
               if self.verbose:
                   logger.error("""Using mask_token, but it is not set yet."""       )
               return None
           return str(self._mask_token       )

       @mask_token.setter
       def        snake_case__  (				self :			str       ,       _lowerCAmelCase :			List[str]       ):
           __snake_case    :							Optional[int]             =  AddedToken(_lowerCAmelCase       ,       lstrip=_lowerCAmelCase       ,       rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase       ,       _lowerCAmelCase       ) else value
           __snake_case    :							Any             =  value

       def        snake_case__  (				self :			Dict       ,       _lowerCAmelCase :			List[int]       ,       _lowerCAmelCase :			Optional[List[int]] = None       ):
           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           __snake_case    :							Optional[int]             =  [self.cls_token_id]
           __snake_case    :							List[str]             =  [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep

       def        snake_case__  (				self :			str       ,       _lowerCAmelCase :			List[int]       ,       _lowerCAmelCase :			Optional[List[int]] = None       ):
           __snake_case    :							Dict             =  [self.sep_token_id]
           __snake_case    :							List[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        snake_case__  (				self :			Dict       ,       *_lowerCAmelCase :			List[str]       ,       **_lowerCAmelCase :			Optional[Any]       ):
           __snake_case    :							List[Any]             =  kwargs.get("""is_split_into_words"""       ,       _lowerCAmelCase       )
           assert self.add_prefix_space or not is_split_into_words, (
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*_lowerCAmelCase       ,       **_lowerCAmelCase       )

       def        snake_case__  (				self :			List[str]       ,       *_lowerCAmelCase :			Any       ,       **_lowerCAmelCase :			int       ):
           __snake_case    :							Optional[int]             =  kwargs.get("""is_split_into_words"""       ,       _lowerCAmelCase       )

           assert self.add_prefix_space or not is_split_into_words, (
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*_lowerCAmelCase       ,       **_lowerCAmelCase       )

       def        snake_case__  (				self :			List[Any]       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			Optional[str] = None       ):
           __snake_case    :							int             =  self._tokenizer.model.save(_lowerCAmelCase       ,       name=_lowerCAmelCase       )
           return tuple(_lowerCAmelCase       )





       def        snake_case__  (				self :			Union[str, Any]       ,       _lowerCAmelCase :			"Conversation"       ):
           __snake_case    :							Tuple             =  []
           for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(_lowerCAmelCase       ,       add_special_tokens=_lowerCAmelCase       ) + [self.eos_token_id]       )

           if len(_lowerCAmelCase       ) > self.model_max_length:
               __snake_case    :							Union[str, Any]             =  input_ids[-self.model_max_length :]
           return input_ids







import doctest

import projectq
from projectq.ops import H, Measure
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int							=						quantum_engine.allocate_qubit()
 H | qubit
 Measure | qubit
 return int(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       doctest.testmod()

       # initialises a new quantum backend
       lowercase_           =	projectq.MainEngine()

       # Generate a list of 10 random numbers
       lowercase_           =	[get_random_number(quantum_engine) for _ in range(1_0)]

       # Flushes the quantum engine from memory
       quantum_engine.flush()

       print('Random numbers', random_numbers_list)

