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_mvp import MvpTokenizer


__a  :     Tuple       =  logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
__a  :     Optional[int]       =  {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

__a  :     Dict       =  {
    """RUCAIBox/mvp""": 1_0_2_4,
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[int]			 =						VOCAB_FILES_NAMES
			__a  : Union[str, Any]			 =						PRETRAINED_VOCAB_FILES_MAP
			__a  : Optional[Any]			 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a  : Dict			 =						['''input_ids''', '''attention_mask''']
			__a  : Tuple			 =						MvpTokenizer





			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__      ,   )     ->					str:

							'''simple docstring'''


							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__      ,   )

							__lowercase 			=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get('''add_prefix_space'''      ,   lowerCAmelCase__						) != add_prefix_space:
											__lowercase 			=							getattr(lowerCAmelCase__      ,   pre_tok_state.pop('''type'''						)						)
											__lowercase 			=							add_prefix_space
											__lowercase 			=							pre_tok_class(**lowerCAmelCase__						)

							__lowercase 			=							add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							__lowercase 			=							'''post_processor'''
							__lowercase 			=							getattr(self.backend_tokenizer      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							if tokenizer_component_instance:
											__lowercase 			=							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:
															__lowercase 			=							tuple(state['''sep''']						)
											if "cls" in state:
															__lowercase 			=							tuple(state['''cls''']						)

											__lowercase 			=							False

											if state.get('''add_prefix_space'''      ,   lowerCAmelCase__						) != add_prefix_space:
															__lowercase 			=							add_prefix_space
															__lowercase 			=							True

											if state.get('''trim_offsets'''      ,   lowerCAmelCase__						) != trim_offsets:
															__lowercase 			=							trim_offsets
															__lowercase 			=							True

											if changes_to_apply:
															__lowercase 			=							getattr(lowerCAmelCase__      ,   state.pop('''type'''						)						)
															__lowercase 			=							component_class(**lowerCAmelCase__						)
															setattr(self.backend_tokenizer      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							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__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							AddedToken(lowerCAmelCase__      ,   lstrip=lowerCAmelCase__      ,   rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						) else value
							__lowercase 			=							value





			def        _SCREAMING_SNAKE_CASE			(   self      ,   *lowerCAmelCase__      ,   **lowerCAmelCase__						)     ->					BatchEncoding:

							'''simple docstring'''


							__lowercase 			=							kwargs.get('''is_split_into_words'''      ,   lowerCAmelCase__						)

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    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__						)     ->					BatchEncoding:

							'''simple docstring'''


							__lowercase 			=							kwargs.get('''is_split_into_words'''      ,   lowerCAmelCase__						)

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    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						)     ->					Tuple[str]:

							'''simple docstring'''


							__lowercase 			=							self._tokenizer.model.save(lowerCAmelCase__      ,   name=lowerCAmelCase__						)
							return tuple(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=None						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							[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						)     ->					List[int]:

							'''simple docstring'''


							__lowercase 			=							[self.sep_token_id]
							__lowercase 			=							[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 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							qiskit.Aer.get_backend('''aer_simulator'''							)

				# Create a Quantum Circuit acting on the q register
				__lowercase 			=							qiskit.QuantumCircuit(lowercase							,			lowercase							)

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

				# Execute the circuit on the simulator
				__lowercase 			=							qiskit.execute(lowercase							,			lowercase							,			shots=1000							)

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


if __name__ == "__main__":
		print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')