


'''simple docstring'''

import qiskit





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						qiskit.Aer.get_backend("aer_simulator"	)

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

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

 # Execute the circuit on the simulator
 lowerCamelCase_  				=						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__":
     print(f'''Total count for various states are: {single_qubit_measure(1, 1)}''')







'''simple docstring'''

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


a_						:       Dict      			=				logging.get_logger(__name__)

a_						:       List[Any]      			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_						:       Optional[Any]      			=				{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

a_						:       str      			=				{
    """gpt-neox-20b""": 2048,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

   lowerCamelCase_  				=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
   if pre_tok_state.get("add_prefix_space"    ,						UpperCamelCase  ) != add_prefix_space:
    lowerCamelCase_  				=						getattr(UpperCamelCase    ,						pre_tok_state.pop("type"  )  )
    lowerCamelCase_  				=						add_prefix_space
    lowerCamelCase_  				=						pre_tok_class(**UpperCamelCase  )

   lowerCamelCase_  				=						add_prefix_space






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ) + [self.eos_token_id]  )

   if len(UpperCamelCase  ) > self.model_max_length:
    lowerCamelCase_  				=						input_ids[-self.model_max_length :]
   return input_ids



