"""simple docstring"""



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowercase		:		str														=							logging.get_logger(__name__)

__lowercase		:		Any														=							{"vocab_file": "sentencepiece.bpe.model"}

__lowercase		:		Dict														=							{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__lowercase		:		List[str]														=							{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__lowercase		:		int														=							"▁"



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Union[str, Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					List[Any]							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Union[str, Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					Any							=					['''input_ids''', '''attention_mask''']
    def __init__(		self							:     str	,      A_							:     Any	,      A_							:     Optional[int]="<s>"	,      A_							:     Any="</s>"	,      A_							:     Optional[int]="</s>"	,      A_							:     Tuple="<s>"	,      A_							:     str="<unk>"	,      A_							:     int="<pad>"	,      A_							:     Any="<mask>"	,      A_							:     Optional[Dict[str, Any]] = None	,      **A_							:     str	,      )    ->       None:
         # Mask token behave like a normal word, i.e. include the space before it
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else mask_token

         __snake_case				=							{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=A_	,      eos_token=A_	,      unk_token=A_	,      sep_token=A_	,      cls_token=A_	,      pad_token=A_	,      mask_token=A_	,      sp_model_kwargs=self.sp_model_kwargs	,      **A_	,      )

         __snake_case				=							vocab_file
         __snake_case				=							spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(A_    )    )

         __snake_case				=							{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         __snake_case				=							len(self.sp_model    ) - 1
         __snake_case				=							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
    def  lowercase					(		self							:     List[Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case				=							[self.cls_token_id]
         __snake_case				=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_	,      token_ids_a=A_	,      already_has_special_tokens=A_    )

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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]
    @property
    def  lowercase					(		self							:     int    )    ->       Union[str, Any]:
         return len(self.sp_model    )
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							{self.convert_ids_to_tokens(A_    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab
    def  lowercase					(		self							:     Any	,      A_							:     str    )    ->       List[str]:
         return self.sp_model.encode(A_	,      out_type=A_    )
    def  lowercase					(		self							:     str	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         __snake_case				=							self.sp_model.PieceToId(A_    )

         return spm_id if spm_id else self.unk_token_id
    def  lowercase					(		self							:     Any	,      A_							:     str    )    ->       Dict:
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(A_    )
    def  lowercase					(		self							:     str	,      A_							:     int    )    ->       Union[str, Any]:
         __snake_case				=							[]
         __snake_case				=							''''''
         __snake_case				=							False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   if not prev_is_special:
                        out_string += " "
                   out_string += self.sp_model.decode(A_    ) + token
                   __snake_case				=							True
                   __snake_case				=							[]
              else:
                   current_sub_tokens.append(A_    )
                   __snake_case				=							False
         out_string += self.sp_model.decode(A_    )
         return out_string.strip()
    def __getstate__(		self							:     Dict    )    ->       int:
         __snake_case				=							self.__dict__.copy()
         __snake_case				=							None
         return state
    def __setstate__(		self							:     Union[str, Any]	,      A_							:     List[str]    )    ->       List[Any]:
         __snake_case				=							d

         # for backward compatibility
         if not hasattr(self	,      '''sp_model_kwargs'''    ):
              __snake_case				=							{}

         __snake_case				=							spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(self.vocab_file    )







    def  lowercase					(		self							:     Tuple	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

         if os.path.abspath(self.vocab_file    ) != os.path.abspath(A_    ) and os.path.isfile(self.vocab_file    ):
              copyfile(self.vocab_file	,      A_    )
         elif not os.path.isfile(self.vocab_file    ):
              with open(A_	,      '''wb'''    ) as fi:
                   __snake_case				=							self.sp_model.serialized_model_proto()
                   fi.write(A_    )

         return (out_vocab_file,)
"""simple docstring"""



import qiskit





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							qiskit.Aer.get_backend('''aer_simulator''')

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

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

     # Execute the circuit on the simulator
     __snake_case				=							qiskit.execute(snake_case,      snake_case,      shots=10_00)

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


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