

import math

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

def  lowerCamelCase__ (					_a = 3):


	if isinstance(_a							,			_a):
		raise TypeError("number of qubits must be a integer.")
	if number_of_qubits <= 0:
		raise ValueError("number of qubits must be > 0.")
	if math.floor(_a) != number_of_qubits:
		raise ValueError("number of qubits must be exact integer.")
	if number_of_qubits > 10:
		raise ValueError("number of qubits too large to simulate(>10).")

	SCREAMING_SNAKE_CASE   :      Dict				     =       QuantumRegister(_a							,			"qr")
	SCREAMING_SNAKE_CASE   :      int				     =       ClassicalRegister(_a							,			"cr")

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       QuantumCircuit(_a							,			_a)

	SCREAMING_SNAKE_CASE   :      Any				     =       number_of_qubits

	for i in range(_a):
		quantum_circuit.h(number_of_qubits - i - 1)
		counter -= 1
		for j in range(_a):
			quantum_circuit.cp(np.pi / 2 ** (counter - j)							,			_a							,			_a)

	for k in range(number_of_qubits // 2):
		quantum_circuit.swap(_a							,			number_of_qubits - k - 1)

	# measure all the qubits
	quantum_circuit.measure(_a							,			_a)
	# simulate with 10000 shots
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Aer.get_backend("qasm_simulator")
	SCREAMING_SNAKE_CASE   :      int				     =       execute(_a							,			_a							,			shots=10000)

	return job.result().get_counts(_a)


if __name__ == "__main__":
			print(
			    F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
			)


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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







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

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      str				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_head_dim
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads * attention_head_dim

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_channels

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.nn.GroupNorm(num_groups=a      ,      num_channels=a      ,      eps=1e-6      ,      affine=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       nn.Linear(a      ,      a	)

				# 3. Define transformers blocks
				SCREAMING_SNAKE_CASE   :      List[str]				     =       nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            a      ,      a      ,      a      ,      dropout=a      ,      cross_attention_dim=a      ,      activation_fn=a      ,      attention_bias=a      ,      double_self_attention=a      ,      norm_elementwise_affine=a      ,      )
				        for d in range(a	)
				    ]	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Linear(a      ,      a	)






			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Optional[int]=None      ,      a							: List[str]=None      ,      a							: List[str]=None      ,      a							: Union[str, Any]=1      ,      a							: Any=None      ,      a							: bool = True      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       hidden_states.shape
				SCREAMING_SNAKE_CASE   :      Any				     =       batch_frames // num_frames

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_states[None, :].reshape(a      ,      a      ,      a      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_states.permute(0      ,      2      ,      1      ,      3      ,      4	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.norm(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_states.permute(0      ,      3      ,      4      ,      2      ,      1	).reshape(batch_size * height * width      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.proj_in(a	)

				# 2. Blocks
				for block in self.transformer_blocks:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       block(
					    a      ,      encoder_hidden_states=a      ,      timestep=a      ,      cross_attention_kwargs=a      ,      class_labels=a      ,      )

				# 3. Output
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.proj_out(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       (
				    hidden_states[None, None, :]
				    .reshape(a      ,      a      ,      a      ,      a      ,      a	)
				    .permute(0      ,      3      ,      4      ,      1      ,      2	)
				    .contiguous()
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_states.reshape(a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=a	)