


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


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

snake_case							:       Optional[int]										=	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       Union[str, Any]										=	{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:       Optional[int]										=	{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			['input_ids', 'attention_mask']
 SCREAMING_SNAKE_CASE__ =			RobertaTokenizer




 def __init__(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase="replace"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="<unk>"   ,    _lowerCamelCase="<pad>"   ,    _lowerCamelCase="<mask>"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(
           _lowerCamelCase   ,    _lowerCamelCase   ,    tokenizer_file=_lowerCamelCase   ,    errors=_lowerCamelCase   ,    bos_token=_lowerCamelCase   ,    eos_token=_lowerCamelCase   ,    sep_token=_lowerCamelCase   ,    cls_token=_lowerCamelCase   ,    unk_token=_lowerCamelCase   ,    pad_token=_lowerCamelCase   ,    mask_token=_lowerCamelCase   ,    add_prefix_space=_lowerCamelCase   ,    trim_offsets=_lowerCamelCase   ,    **_lowerCamelCase   ,    )

       a		:List[Any]									=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
       if pre_tok_state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
             a		:Optional[int]									=			getattr(_lowerCamelCase   ,    pre_tok_state.pop('''type'''       )       )
             a		:Union[str, Any]									=			add_prefix_space
             a		:Dict									=			pre_tok_class(**_lowerCamelCase       )

       a		:Union[str, Any]									=			add_prefix_space

       a		:List[Any]									=			'''post_processor'''
       a		:List[str]									=			getattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )
       if tokenizer_component_instance:
             a		:Optional[Any]									=			json.loads(tokenizer_component_instance.__getstate__()       )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                   a		:Optional[int]									=			tuple(state['''sep''']       )
             if "cls" in state:
                   a		:Union[str, Any]									=			tuple(state['''cls''']       )

             a		:Optional[int]									=			False

             if state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
                   a		:int									=			add_prefix_space
                   a		:Union[str, Any]									=			True

             if state.get('''trim_offsets'''   ,    _lowerCamelCase       ) != trim_offsets:
                   a		:int									=			trim_offsets
                   a		:List[str]									=			True

             if changes_to_apply:
                   a		:Union[str, Any]									=			getattr(_lowerCamelCase   ,    state.pop('''type'''       )       )
                   a		:str									=			component_class(**_lowerCamelCase       )
                   setattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Union[str, Any]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase   ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else value
       a		:int									=			value




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Union[str, 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 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		: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()._encode_plus(*_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Any									=			self._tokenizer.model.save(_lowerCamelCase   ,    name=_lowerCamelCase       )
       return tuple(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a		:Union[str, Any]									=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
             return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Dict									=			[self.sep_token_id]
       a		:int									=			[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 + sep + token_ids_a + sep       ) * [0]









import qiskit
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      a		:Tuple									=			qiskit.Aer.get_backend('''aer_simulator'''		)

      # Create a Quantum Circuit acting on the q register
      a		:str									=			qiskit.QuantumCircuit(UpperCAmelCase_					,    UpperCAmelCase_		)

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0		)
      circuit.x(1		)

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1]					,    [0, 1]		)

      # Execute the circuit on the qasm simulator
      a		:Optional[Any]									=			qiskit.execute(UpperCAmelCase_					,    UpperCAmelCase_					,    shots=1000		)

      # Return the histogram data of the results of the experiment.
      return job.result().get_counts(UpperCAmelCase_		)


if __name__ == "__main__":
     snake_case							:       Any										=	single_qubit_measure(2, 2)
     print(F"""Total count for various states are: {counts}""")





