



"""simple docstring"""






import copy

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


_lowercase					:					Optional[Any] 		=		logging.get_logger(__name__)

_lowercase					:					int 		=		{}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				'fsmt_decoder'



					def __init__(			self				:    Any,    lowerCamelCase				:    Union[str, Any]=0,    lowerCamelCase				:    List[str]=0	)->   Tuple:
								super().__init__()
								lowerCamelCase__						:						List[str]						      =vocab_size
								lowerCamelCase__						:						int						      =bos_token_id





class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				'fsmt'
					_a       =				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



					def __init__(			self				:    int,    lowerCamelCase				:    Optional[Any]=["en", "de"],    lowerCamelCase				:    Any=4_2024,    lowerCamelCase				:    Tuple=4_2024,    lowerCamelCase				:    Dict="relu",    lowerCamelCase				:    str=1024,    lowerCamelCase				:    int=200,    lowerCamelCase				:    Union[str, Any]=1024,    lowerCamelCase				:    Dict=4096,    lowerCamelCase				:    Optional[int]=12,    lowerCamelCase				:    List[Any]=16,    lowerCamelCase				:    Optional[Any]=0.0,    lowerCamelCase				:    List[Any]=4096,    lowerCamelCase				:    int=12,    lowerCamelCase				:    Tuple=16,    lowerCamelCase				:    List[Any]=0.0,    lowerCamelCase				:    List[str]=0.0,    lowerCamelCase				:    int=0.1,    lowerCamelCase				:    Optional[int]=0.0,    lowerCamelCase				:    Union[str, Any]=0.02,    lowerCamelCase				:    List[Any]=2,    lowerCamelCase				:    List[Any]=True,    lowerCamelCase				:    str=True,    lowerCamelCase				:    int=False,    lowerCamelCase				:    Optional[Any]=5,    lowerCamelCase				:    Optional[Any]=1.0,    lowerCamelCase				:    Any=False,    lowerCamelCase				:    Union[str, Any]=True,    lowerCamelCase				:    Any=1,    lowerCamelCase				:    Optional[Any]=0,    lowerCamelCase				:    Tuple=2,    lowerCamelCase				:    Tuple=2,    **lowerCamelCase				:    Tuple,    )->   Dict:
								lowerCamelCase__						:						Tuple						      =langs
								lowerCamelCase__						:						str						      =src_vocab_size
								lowerCamelCase__						:						str						      =tgt_vocab_size
								lowerCamelCase__						:						List[Any]						      =d_model  # encoder_embed_dim and decoder_embed_dim

								lowerCamelCase__						:						Union[str, Any]						      =encoder_ffn_dim
								lowerCamelCase__						:						List[str]						      =encoder_layers
								lowerCamelCase__						:						Any						      =encoder_attention_heads
								lowerCamelCase__						:						Optional[int]						      =encoder_layerdrop
								lowerCamelCase__						:						Optional[Any]						      =decoder_layerdrop
								lowerCamelCase__						:						str						      =decoder_ffn_dim
								lowerCamelCase__						:						Union[str, Any]						      =decoder_layers
								lowerCamelCase__						:						Union[str, Any]						      =decoder_attention_heads
								lowerCamelCase__						:						Optional[Any]						      =max_position_embeddings
								lowerCamelCase__						:						Dict						      =init_std  # Normal(0, this parameter)
								lowerCamelCase__						:						List[str]						      =activation_function

								lowerCamelCase__						:						Optional[Any]						      =DecoderConfig(vocab_size=lowerCamelCase,    bos_token_id=lowerCamelCase	)
								if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

								lowerCamelCase__						:						int						      =scale_embedding  # scale factor will be sqrt(d_model) if True

								# 3 Types of Dropout
								lowerCamelCase__						:						int						      =attention_dropout
								lowerCamelCase__						:						int						      =activation_dropout
								lowerCamelCase__						:						Optional[int]						      =dropout

								lowerCamelCase__						:						int						      =use_cache
								super().__init__(
								    pad_token_id=lowerCamelCase,    bos_token_id=lowerCamelCase,    eos_token_id=lowerCamelCase,    decoder_start_token_id=lowerCamelCase,    is_encoder_decoder=lowerCamelCase,    tie_word_embeddings=lowerCamelCase,    forced_eos_token_id=lowerCamelCase,    max_length=lowerCamelCase,    num_beams=lowerCamelCase,    length_penalty=lowerCamelCase,    early_stopping=lowerCamelCase,    **lowerCamelCase,    )




					def 						snake_case			(			self				:    List[str]	)->   int:
								lowerCamelCase__						:						List[str]						      =copy.deepcopy(self.__dict__	)
								lowerCamelCase__						:						Union[str, Any]						      =self.decoder.to_dict()
								lowerCamelCase__						:						int						      =self.__class__.model_type
								return output









import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def  _snake_case			(							__snake_case = 1       ,	__snake_case = 1     )  ->  qiskit.result.counts.Counts:






      '''simple docstring'''







      if isinstance(__snake_case       ,	__snake_case     ) or isinstance(__snake_case       ,	__snake_case     ):
            raise TypeError("inputs must be integers."     )
      if (bit_a < 0) or (bit_a < 0):
            raise ValueError("inputs must be positive."     )
      if (math.floor(__snake_case     ) != bit_a) or (math.floor(__snake_case     ) != bit_a):
            raise ValueError("inputs must be exact integers."     )
      if (bit_a > 1) or (bit_a > 1):
            raise ValueError("inputs must be less or equal to 1."     )

      # build registers
      UpperCAmelCase_	:						Union[str, Any]					   =		QuantumRegister(2       ,	"qr"     )
      UpperCAmelCase_	:						Tuple					   =		ClassicalRegister(2       ,	"cr"     )

      UpperCAmelCase_	:						Union[str, Any]					   =		QuantumCircuit(__snake_case       ,	__snake_case     )

      # entanglement the qubits
      quantum_circuit.h(1     )
      quantum_circuit.cx(1       ,	0     )

      # send the information
      UpperCAmelCase_	:						Any					   =		str(__snake_case     ) + str(__snake_case     )

      if c_information == "11":
            quantum_circuit.x(1     )
            quantum_circuit.z(1     )
      elif c_information == "10":
            quantum_circuit.z(1     )
      elif c_information == "01":
            quantum_circuit.x(1     )
      else:
            quantum_circuit.i(1     )

      # unentangled the circuit
      quantum_circuit.cx(1       ,	0     )
      quantum_circuit.h(1     )

      # measure the circuit
      quantum_circuit.measure(__snake_case       ,	__snake_case     )

      UpperCAmelCase_	:						Union[str, Any]					   =		Aer.get_backend("aer_simulator"     )
      UpperCAmelCase_	:						Dict					   =		execute(__snake_case       ,	__snake_case       ,	shots=1_0_0_0     )

      return job.result().get_counts(__snake_case     )


if __name__ == "__main__":
   print(F'''Counts for classical state send: {superdense_coding(1,1)}''')
