



import math

import qiskit

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple = 1				,     _SCREAMING_SNAKE_CASE       :			Tuple = 1				,     _SCREAMING_SNAKE_CASE       :			int = 1    ):
    """simple docstring"""







    if (
        isinstance(lowerCamelCase_				,     lowerCamelCase_    )
        or isinstance(lowerCamelCase_				,     lowerCamelCase_    )
        or isinstance(lowerCamelCase_				,     lowerCamelCase_    )
    ):
        raise TypeError("""inputs must be integers."""    )

    if (input_a < 0) or (input_a < 0) or (carry_in < 0):
        raise ValueError("""inputs must be positive."""    )

    if (
        (math.floor(lowerCamelCase_    ) != input_a)
        or (math.floor(lowerCamelCase_    ) != input_a)
        or (math.floor(lowerCamelCase_    ) != carry_in)
    ):
        raise ValueError("""inputs must be exact integers."""    )

    if (input_a > 2) or (input_a > 2) or (carry_in > 2):
        raise ValueError("""inputs must be less or equal to 2."""    )

    # build registers
    __a       =		qiskit.QuantumRegister(4				,     """qr"""    )
    __a       =		qiskit.ClassicalRegister(2				,     """cr"""    )
    # list the entries
    __a       =		[input_a, input_a, carry_in]

    __a       =		qiskit.QuantumCircuit(lowerCamelCase_				,     lowerCamelCase_    )

    for i in range(0				,     3    ):
        if entry[i] == 2:
            quantum_circuit.h(lowerCamelCase_    )  # for hadamard entries
        elif entry[i] == 1:
            quantum_circuit.x(lowerCamelCase_    )  # for 1 entries
        elif entry[i] == 0:
            quantum_circuit.i(lowerCamelCase_    )  # for 0 entries

    # build the circuit
    quantum_circuit.ccx(0				,     1				,     3    )  # ccx = toffoli gate
    quantum_circuit.cx(0				,     1    )
    quantum_circuit.ccx(1				,     2				,     3    )
    quantum_circuit.cx(1				,     2    )
    quantum_circuit.cx(0				,     1    )

    quantum_circuit.measure([2, 3]				,     lowerCamelCase_    )  # measure the last two qbits

    __a       =		qiskit.Aer.get_backend("""aer_simulator"""    )
    __a       =		qiskit.execute(lowerCamelCase_				,     lowerCamelCase_				,     shots=1000    )

    return job.result().get_counts(lowerCamelCase_    )


if __name__ == "__main__":
      print(F"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
