




import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def        __SCREAMING_SNAKE_CASE (	lowercase_ = np.pi / 2       ,  lowercase_ = np.pi / 2       ,  lowercase_ = np.pi / 2   )							->						qiskit.result.counts.Counts:

    """simple docstring"""


    __UpperCamelCase			=    QuantumRegister(3       ,  '''qr'''   )  # Define the number of quantum bits
    __UpperCamelCase			=    ClassicalRegister(1       ,  '''cr'''   )  # Define the number of classical bits

    __UpperCamelCase			=    QuantumCircuit(lowercase_       ,  lowercase_   )  # Define the quantum circuit.

    # Build the circuit
    quantum_circuit.u(lowercase_       ,  lowercase_       ,  lowercase_       ,  0   )  # Quantum State to teleport
    quantum_circuit.h(1   )  # add hadamard gate
    quantum_circuit.cx(
        1       ,  2   )  # add control gate with qubit 1 as control and 2 as target.
    quantum_circuit.cx(0       ,  1   )
    quantum_circuit.h(0   )
    quantum_circuit.cz(0       ,  2   )  # add control z gate.
    quantum_circuit.cx(1       ,  2   )

    quantum_circuit.measure([2]       ,  [0]   )  # measure the qubit.

    # Simulate the circuit using qasm simulator
    __UpperCamelCase			=    Aer.get_backend('''aer_simulator'''   )
    __UpperCamelCase			=    execute(lowercase_       ,  lowercase_       ,  shots=10_00   )

    return job.result().get_counts(lowercase_   )


if __name__ == "__main__":
 print(
     "Total count for teleported state is: "
     f"""{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"""
 )













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


__UpperCAmelCase			:  List[str]										=				logging.get_logger(__name__)

__UpperCAmelCase			:  int										=				{
    # pcqm4mv1 now deprecated
    "graphormer-base": "https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""graphormer"""
       lowerCAmelCase__ 						=	["""past_key_values"""]




       def __init__(    self					:       List[Any]				,							A					:       int = 1				,							A					:       int = 512 * 9				,							A					:       int = 512 * 3				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 128				,							A					:       int = 5				,							A					:       int = 1_024				,							A					:       str = "multi_hop"				,							A					:       int = 512				,							A					:       bool = False				,							A					:       int = 12				,							A					:       int = 768				,							A					:       int = 768				,							A					:       int = 32				,							A					:       float = 0.1				,							A					:       float = 0.1				,							A					:       float = 0.0				,							A					:       bool = False				,							A					:       bool = False				,							A					:       bool = False				,							A					:       str = "gelu"				,							A					:       float = None				,							A					:       bool = False				,							A					:       int = 0				,							A					:       bool = False				,							A					:       float = 0.0				,							A					:       int = 8				,							A					:       int = None				,							A					:       int = None				,							A					:       bool = True				,							A					:       bool = True				,							A					:       Any=0				,							A					:       Optional[int]=1				,							A					:       Any=2				,							**A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		num_classes
         __snake_case:		int					    =		num_atoms
         __snake_case:		List[str]					    =		num_in_degree
         __snake_case:		str					    =		num_out_degree
         __snake_case:		Tuple					    =		num_edges
         __snake_case:		Optional[int]					    =		num_spatial
         __snake_case:		int					    =		num_edge_dis
         __snake_case:		Tuple					    =		edge_type
         __snake_case:		str					    =		multi_hop_max_dist
         __snake_case:		List[str]					    =		spatial_pos_max
         __snake_case:		Optional[Any]					    =		max_nodes
         __snake_case:		Optional[int]					    =		num_hidden_layers
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Optional[int]					    =		ffn_embedding_dim
         __snake_case:		List[str]					    =		num_attention_heads
         __snake_case:		Any					    =		dropout
         __snake_case:		Dict					    =		attention_dropout
         __snake_case:		Any					    =		layerdrop
         __snake_case:		Dict					    =		encoder_normalize_before
         __snake_case:		Any					    =		pre_layernorm
         __snake_case:		Optional[int]					    =		apply_graphormer_init
         __snake_case:		List[str]					    =		activation_fn
         __snake_case:		Optional[int]					    =		embed_scale
         __snake_case:		Dict					    =		freeze_embeddings
         __snake_case:		Any					    =		num_trans_layers_to_freeze
         __snake_case:		int					    =		share_input_output_embed
         __snake_case:		Dict					    =		traceable
         __snake_case:		str					    =		q_noise
         __snake_case:		Union[str, Any]					    =		qn_block_size

         # These parameters are here for future extensions
         # atm, the model only supports self attention
         __snake_case:		str					    =		kdim
         __snake_case:		Dict					    =		vdim
         __snake_case:		List[Any]					    =		self_attention
         __snake_case:		Tuple					    =		bias

         super().__init__(
             pad_token_id=A				,							bos_token_id=A				,							eos_token_id=A				,							**A				,							)






