





'''simple docstring'''



import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class    __SCREAMING_SNAKE_CASE						:



     '''simple docstring'''






     @property
     def 						UpperCamelCase(       self						):
       return self.get_dummy_input()

     @property
     def 						UpperCamelCase(       self						):



       if self.block_type == "down":
         return (4, 32, 16, 16)
       elif self.block_type == "mid":
         return (4, 32, 32, 32)
       elif self.block_type == "up":
         return (4, 32, 64, 64)

       raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''						)

     def 						UpperCamelCase(       self    , lowerCamelCase=True    , lowerCamelCase=False    , lowerCamelCase=False    , lowerCamelCase=False    , ):
       _snake_case  		=					4
       _snake_case  		=					32
       _snake_case  		=					(32, 32)

       _snake_case  		=					torch.manual_seed(0						)
       _snake_case  		=					torch.device(lowerCamelCase						)
       _snake_case  		=					(batch_size, num_channels) + sizes
       _snake_case  		=					randn_tensor(lowerCamelCase    , generator=lowerCamelCase    , device=lowerCamelCase						)
       _snake_case  		=					{"hidden_states": hidden_states}

       if include_temb:
         _snake_case  		=					128
         _snake_case  		=					randn_tensor((batch_size, temb_channels)    , generator=lowerCamelCase    , device=lowerCamelCase						)

       if include_res_hidden_states_tuple:
         _snake_case  		=					torch.manual_seed(1						)
         _snake_case  		=					(randn_tensor(lowerCamelCase    , generator=lowerCamelCase    , device=lowerCamelCase						),)

       if include_encoder_hidden_states:
         _snake_case  		=					floats_tensor((batch_size, 32, 32)						).to(lowerCamelCase						)

       if include_skip_sample:
         _snake_case  		=					randn_tensor(((batch_size, 3) + sizes)    , generator=lowerCamelCase    , device=lowerCamelCase						)

       return dummy_input

     def 						UpperCamelCase(       self						):
       _snake_case  		=					{
           "in_channels": 32,
           "out_channels": 32,
           "temb_channels": 128,
       }
       if self.block_type == "up":
         _snake_case  		=					32

       if self.block_type == "mid":
         init_dict.pop("out_channels"						)

       _snake_case  		=					self.dummy_input
       return init_dict, inputs_dict

     def 						UpperCamelCase(       self    , lowerCamelCase						):
       _snake_case		,		_snake_case  		=					self.prepare_init_args_and_inputs_for_common()
       _snake_case  		=					self.block_class(**lowerCamelCase						)
       unet_block.to(lowerCamelCase						)
       unet_block.eval()

       with torch.no_grad():
         _snake_case  		=					unet_block(**lowerCamelCase						)

       if isinstance(lowerCamelCase    , lowerCamelCase						):
         _snake_case  		=					output[0]

       self.assertEqual(output.shape    , self.output_shape						)

       _snake_case  		=					output[0, -1, -3:, -3:]
       _snake_case  		=					torch.tensor(lowerCamelCase						).to(lowerCamelCase						)
       assert torch_all_close(output_slice.flatten()    , lowerCamelCase    , atol=5e-3						)






     @unittest.skipIf(torch_device == "mps"    , "Training is not supported in mps"						)
     def 						UpperCamelCase(       self						):
       _snake_case		,		_snake_case  		=					self.prepare_init_args_and_inputs_for_common()
       _snake_case  		=					self.block_class(**lowerCamelCase						)
       model.to(lowerCamelCase						)
       model.train()
       _snake_case  		=					model(**lowerCamelCase						)

       if isinstance(lowerCamelCase    , lowerCamelCase						):
         _snake_case  		=					output[0]

       _snake_case  		=					torch.device(lowerCamelCase						)
       _snake_case  		=					randn_tensor(output.shape    , device=lowerCamelCase						)
       _snake_case  		=					torch.nn.functional.mse_loss(lowerCamelCase    , lowerCamelCase						)
       loss.backward()








import qiskit

def lowercase    (  SCREAMING_SNAKE_CASE = 2 )			->							qiskit.result.counts.Counts:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						qubits

			# Using Aer's simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.Aer.get_backend('aer_simulator' )

			# Creating a Quantum Circuit acting on the q register
			SCREAMING_SNAKE_CASE_								=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Adding a H gate on qubit 0 (now q0 in superposition)
			circuit.h(0 )

			for i in range(1       ,    SCREAMING_SNAKE_CASE ):
						# Adding CX (CNOT) gate
						circuit.cx(i - 1       ,    SCREAMING_SNAKE_CASE )

			# Mapping the quantum measurement to the classical bits
			circuit.measure(list(range(SCREAMING_SNAKE_CASE ) )       ,    list(range(SCREAMING_SNAKE_CASE ) ) )

			# Now measuring any one qubit would affect other qubits to collapse
			# their super position and have same state as the measured one.

			# Executing the circuit on the simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.execute(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    shots=10_00 )

			return job.result().get_counts(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				print(f"""Total count for various states are: {quantum_entanglement(3)}""")
