

'''simple docstring'''


import qiskit
from qiskit.providers import Backend







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     tuple[qiskit.QuantumCircuit, str, str]:
	UpperCAmelCase_		:   str													=       bin(UpperCamelCase	)[2:], bin(UpperCamelCase	)[2:]  # Remove leading '0b'

	# Ensure that both strings are of the same length
	if len(UpperCamelCase	) > len(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[int]													=       y.zfill(len(UpperCamelCase	)	)
	else:
		UpperCAmelCase_		:   Any													=       x.zfill(len(UpperCamelCase	)	)

	# We need (3 * number of bits in the larger number)+1 qBits
	# The second parameter is the number of classical registers, to measure the result
	UpperCAmelCase_		:   Union[str, Any]													=       qiskit.QuantumCircuit((len(UpperCamelCase	) * 3) + 1 ,len(UpperCamelCase	) + 1	)

	# We are essentially "not-ing" the bits that are 1
	# Reversed because it's easier to perform ops on more significant bits
	for i in range(len(UpperCamelCase	)	):
		if x[::-1][i] == "1":
			circuit.x(UpperCamelCase	)
	for j in range(len(UpperCamelCase	)	):
		if y[::-1][j] == "1":
			circuit.x(len(UpperCamelCase	) + j	)

	return circuit, x, y







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,)     ->     Union[str, Any]:
	circuit.ccx(UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)
	circuit.cx(UpperCamelCase ,UpperCamelCase	)
	circuit.ccx(UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)
	circuit.cx(UpperCamelCase ,UpperCamelCase	)
	circuit.cx(UpperCamelCase ,UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = qiskit.Aer.get_backend('aer_simulator'	) ,)     ->     int:







	if vala < 0 or vala < 0:
		raise ValueError('Both Integers must be positive!'	)

	# Store the Integers
	UpperCAmelCase_		:   List[str]													=       store_two_classics(UpperCamelCase ,UpperCamelCase	)
	for i in range(len(UpperCamelCase	)	):
		full_adder(UpperCamelCase ,UpperCamelCase ,len(UpperCamelCase	) + i ,len(UpperCamelCase	) + len(UpperCamelCase	) + i ,len(UpperCamelCase	) + len(UpperCamelCase	) + i + 1	)
		circuit.barrier()  # Optional, just for aesthetics

	# Measure the resultant qBits
	for i in range(len(UpperCamelCase	) + 1	):
		circuit.measure([(len(UpperCamelCase	) * 2) + i] ,[i]	)

	UpperCAmelCase_		:   Optional[int]													=       qiskit.execute(UpperCamelCase ,UpperCamelCase ,shots=1	).result()

	# The result is in binary. Convert it back to int
	return int(list(res.get_counts()	)[0] ,2	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()









"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
