



'''simple docstring'''





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
      from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	c_float





    @property
    def 					__snake_case       (    self    :    Optional[Any]     ):



       '''simple docstring'''







       return sizeof(self.value     )




def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_ = 0							):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      snake_case   :							Any						  =fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   return num_parameters * dtype.size


def 		_a   (  lowerCamelCase_							):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowerCamelCase_							)
   except (ValueError, OSError, KeyError):
      snake_case  ,    snake_case   :							List[Any]						  =None, None
      try:
         snake_case   :							Union[str, Any]						  =AutoTokenizer.from_pretrained(lowerCamelCase_							)
      except (OSError, KeyError):
         pass
      try:
         snake_case   :							Optional[int]						  =AutoFeatureExtractor.from_pretrained(lowerCamelCase_							)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''							)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor










'''simple docstring'''





import doctest

import projectq
from projectq.ops import H, Measure
def 		_a   (  lowerCamelCase_							):
   snake_case   :							str						  =quantum_engine.allocate_qubit()
   H | qubit
   Measure | qubit
   return int(lowerCamelCase_							)


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

      # initialises a new quantum backend
      A			:   Dict			=      projectq.MainEngine()

      # Generate a list of 10 random numbers
      A			:   Dict			=      [get_random_number(quantum_engine) for _ in range(10)]

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

      print("""Random numbers""", random_numbers_list)





