import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






   '''simple docstring'''






   a__			:		Any      			=   IFInpaintingPipeline
   a__			:		List[str]      			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
   a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a__			:		Optional[Any]      			=   PipelineTesterMixin.required_optional_params - {"""latents"""}







   def     UpperCamelCase__     (			self) ->			Any:
       return self._get_dummy_components()







   def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0) ->			Dict:





       if str(UpperCamelCase_).startswith('''mps'''):
           __UpperCamelCase  :List[str]    						=	torch.manual_seed(UpperCamelCase_)
       else:
           __UpperCamelCase  :List[str]    						=	torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)

       __UpperCamelCase  :str    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(UpperCamelCase_)).to(UpperCamelCase_)
       __UpperCamelCase  :Union[str, Any]    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(UpperCamelCase_)).to(UpperCamelCase_)

       __UpperCamelCase  :List[str]    						=	{
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'mask_image': mask_image,
           'generator': generator,
           'num_inference_steps': 2,
           'output_type': 'numpy',
       }

       return inputs







   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
   def     UpperCamelCase__     (			self) ->			str:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







   def     UpperCamelCase__     (			self) ->			List[Any]:
       self._test_save_load_optional_components()







   @unittest.skipIf(torch_device != '''cuda'''				,				reason='''float16 requires CUDA''')
   def     UpperCamelCase__     (			self) ->			Optional[Any]:
       super().test_save_load_floataa(expected_max_diff=1E-1)







   def     UpperCamelCase__     (			self) ->			Any:
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)







   def     UpperCamelCase__     (			self) ->			Optional[int]:
       self._test_save_load_local()







   def     UpperCamelCase__     (			self) ->			Optional[Any]:
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2				,				)






import qiskit



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	qiskit.Aer.get_backend('''aer_simulator'''			)

    __UpperCamelCase  :Tuple    						=	qiskit.QuantumCircuit(4						,    2			)
    # encode inputs in qubits 0 and 1
    if bita == 1:
        qc_ha.x(0			)
    if bita == 1:
        qc_ha.x(1			)
    qc_ha.barrier()

    # use cnots to write XOR of the inputs on qubit2
    qc_ha.cx(0						,    2			)
    qc_ha.cx(1						,    2			)

    # use ccx / toffoli gate to write AND of the inputs on qubit3
    qc_ha.ccx(0						,    1						,    3			)
    qc_ha.barrier()

    # extract outputs
    qc_ha.measure(2						,    0			)  # extract XOR value
    qc_ha.measure(3						,    1			)  # extract AND value

    # Execute the circuit on the qasm simulator
    __UpperCamelCase  :Optional[Any]    						=	qiskit.execute(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    shots=1_000			)

    # Return the histogram data of the results of the experiment
    return job.result().get_counts(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      half_adder(1, 1)
    print(F'Half Adder Output Qubit Counts: {counts}')





