




import copy

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


__A						:		Dict          =						logging.get_logger(__name__)

__A						:		int          =						{}

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "fsmt_decoder"




					def __init__(   self:		Optional[Any],     _lowercase:		Optional[Any]=0,     _lowercase:		Dict=0):


									'''simple docstring'''


									super().__init__()
									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  bos_token_id





class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "fsmt"
					__UpperCamelCase     	= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(   self:		str,     _lowercase:		Optional[Any]=["en", "de"],     _lowercase:		List[str]=42024,     _lowercase:		List[str]=42024,     _lowercase:		Optional[int]="relu",     _lowercase:		Union[str, Any]=1024,     _lowercase:		Dict=200,     _lowercase:		Dict=1024,     _lowercase:		List[str]=4096,     _lowercase:		str=12,     _lowercase:		Optional[Any]=16,     _lowercase:		List[str]=0.0,     _lowercase:		List[Any]=4096,     _lowercase:		Dict=12,     _lowercase:		Optional[int]=16,     _lowercase:		Optional[int]=0.0,     _lowercase:		Union[str, Any]=0.0,     _lowercase:		Dict=0.1,     _lowercase:		List[Any]=0.0,     _lowercase:		Optional[Any]=0.02,     _lowercase:		Optional[int]=2,     _lowercase:		int=True,     _lowercase:		List[str]=True,     _lowercase:		Any=False,     _lowercase:		Union[str, Any]=5,     _lowercase:		Tuple=1.0,     _lowercase:		List[str]=False,     _lowercase:		Tuple=True,     _lowercase:		Union[str, Any]=1,     _lowercase:		Union[str, Any]=0,     _lowercase:		Optional[int]=2,     _lowercase:		Dict=2,     **_lowercase:		Optional[Any],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  langs
									__lowerCAmelCase					=  src_vocab_size
									__lowerCAmelCase					=  tgt_vocab_size
									__lowerCAmelCase					=  d_model  # encoder_embed_dim and decoder_embed_dim

									__lowerCAmelCase					=  encoder_ffn_dim
									__lowerCAmelCase					=  __lowerCAmelCase					=  encoder_layers
									__lowerCAmelCase					=  encoder_attention_heads
									__lowerCAmelCase					=  encoder_layerdrop
									__lowerCAmelCase					=  decoder_layerdrop
									__lowerCAmelCase					=  decoder_ffn_dim
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  max_position_embeddings
									__lowerCAmelCase					=  init_std  # Normal(0, this parameter)
									__lowerCAmelCase					=  activation_function

									__lowerCAmelCase					=  DecoderConfig(vocab_size=_lowercase,     bos_token_id=_lowercase)
									if "decoder" in common_kwargs:
													del common_kwargs["decoder"]

									__lowerCAmelCase					=  scale_embedding  # scale factor will be sqrt(d_model) if True

									# 3 Types of Dropout
									__lowerCAmelCase					=  attention_dropout
									__lowerCAmelCase					=  activation_dropout
									__lowerCAmelCase					=  dropout

									__lowerCAmelCase					=  use_cache
									super().__init__(
									    pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     decoder_start_token_id=_lowercase,     is_encoder_decoder=_lowercase,     tie_word_embeddings=_lowercase,     forced_eos_token_id=_lowercase,     max_length=_lowercase,     num_beams=_lowercase,     length_penalty=_lowercase,     early_stopping=_lowercase,     **_lowercase,     )







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  copy.deepcopy(self.__dict__)
									__lowerCAmelCase					=  self.decoder.to_dict()
									__lowerCAmelCase					=  self.__class__.model_type
									return output






"""simple docstring"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # 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
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 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(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



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

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

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

     lowercase__								=				qiskit.execute(A     ,					A     ,					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()




