

"""simple docstring"""






import qiskit
def    UpperCamelCase       (      _lowerCAmelCase  :    Optional[int] = 2			)					->      List[Any]:
	_UpperCAmelCase     :				Any          =       qubits

	# Using Aer's simulator
	_UpperCAmelCase     :				List[str]          =       qiskit.Aer.get_backend("""aer_simulator"""			)

	# Creating a Quantum Circuit acting on the q register
	_UpperCAmelCase     :				List[str]          =       qiskit.QuantumCircuit(_UpperCamelCase,	_UpperCamelCase			)

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

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

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

	# 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
	_UpperCAmelCase     :				Optional[int]          =       qiskit.execute(_UpperCamelCase,	_UpperCamelCase,	shots=1000			)

	return job.result().get_counts(_UpperCamelCase			)


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













'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Dict									=     StableDiffusionControlNetImgaImgPipeline
    __lowerCamelCase: Union[str, Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    __lowerCamelCase: Optional[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Any									=     IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
    __lowerCamelCase: Dict									=     IMAGE_TO_IMAGE_IMAGE_PARAMS




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     Tuple							     =  UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
       torch.manual_seed(0      )
       lowercase_						:     Optional[Any]							     =  ControlNetModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
       torch.manual_seed(0      )
       lowercase_						:     List[Any]							     =  DDIMScheduler(
           beta_start=0.0_0085	,  beta_end=0.012	,  beta_schedule="scaled_linear"	,  clip_sample=a	,  set_alpha_to_one=a	,  )
       torch.manual_seed(0      )
       lowercase_						:     Union[str, Any]							     =  AutoencoderKL(
           block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       torch.manual_seed(0      )
       lowercase_						:     str							     =  CLIPTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
       lowercase_						:     List[str]							     =  CLIPTextModel(a      )
       lowercase_						:     Any							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     Tuple							     =  {
           "unet": unet,
           "controlnet": controlnet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							List[str]	,  a	:							Any=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     int							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Optional[int]							     =  torch.Generator(device=a      ).manual_seed(a      )

       lowercase_						:     Optional[Any]							     =  2
       lowercase_						:     List[str]							     =  randn_tensor(
           (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a      )	,  )
       lowercase_						:     List[Any]							     =  floats_tensor(control_image.shape	,  rng=random.Random(a      )      ).to(a      )
       lowercase_						:     List[str]							     =  image.cpu().permute(0	,  2	,  3	,  1      )[0]
       lowercase_						:     Tuple							     =  Image.fromarray(np.uinta(a      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
       lowercase_						:     List[str]							     =  {
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
           "image": image,
           "control_image": control_image,
       }

       return inputs




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3      )




    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()	,  reason="XFormers attention is only available with CUDA and `xformers` installed"	,  )
    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )





    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self._test_inference_batch_single_identical(expected_max_diff=2e-3      )


class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Optional[Any]									=     StableDiffusionControlNetImgaImgPipeline
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    __lowerCamelCase: Optional[int]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Dict									=     frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     Union[str, Any]							     =  UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
       torch.manual_seed(0      )

       def init_weights(a	:							int      ):
          if isinstance(a	,  torch.nn.Convad      ):
             torch.nn.init.normal(m.weight      )
             m.bias.data.fill_(1.0      )

       lowercase_						:     Optional[int]							     =  ControlNetModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
       controlneta.controlnet_down_blocks.apply(a      )

       torch.manual_seed(0      )
       lowercase_						:     Tuple							     =  ControlNetModel(
           block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
       controlneta.controlnet_down_blocks.apply(a      )

       torch.manual_seed(0      )
       lowercase_						:     Optional[int]							     =  DDIMScheduler(
           beta_start=0.0_0085	,  beta_end=0.012	,  beta_schedule="scaled_linear"	,  clip_sample=a	,  set_alpha_to_one=a	,  )
       torch.manual_seed(0      )
       lowercase_						:     int							     =  AutoencoderKL(
           block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       torch.manual_seed(0      )
       lowercase_						:     List[str]							     =  CLIPTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
       lowercase_						:     Optional[int]							     =  CLIPTextModel(a      )
       lowercase_						:     Tuple							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     Union[str, Any]							     =  MultiControlNetModel([controlneta, controlneta]      )

       lowercase_						:     Dict							     =  {
           "unet": unet,
           "controlnet": controlnet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components




    def 					lowerCAmelCase__					(      self	:							List[Any]	,  a	:							List[str]	,  a	:							Optional[Any]=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     str							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Any							     =  torch.Generator(device=a      ).manual_seed(a      )

       lowercase_						:     Dict							     =  2

       lowercase_						:     Union[str, Any]							     =  [
           randn_tensor(
               (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a      )	,  ),
           randn_tensor(
               (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a      )	,  ),
       ]

       lowercase_						:     Dict							     =  floats_tensor(control_image[0].shape	,  rng=random.Random(a      )      ).to(a      )
       lowercase_						:     Tuple							     =  image.cpu().permute(0	,  2	,  3	,  1      )[0]
       lowercase_						:     Optional[int]							     =  Image.fromarray(np.uinta(a      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
       lowercase_						:     Optional[int]							     =  {
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
           "image": image,
           "control_image": control_image,
       }

       return inputs




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.get_dummy_components()
       lowercase_						:     Any							     =  self.pipeline_class(**a      )
       pipe.to(a      )

       lowercase_						:     str							     =  10.0
       lowercase_						:     Dict							     =  4

       lowercase_						:     Optional[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     str							     =  steps
       lowercase_						:     List[str]							     =  scale
       lowercase_						:     Dict							     =  pipe(**a      )[0]

       lowercase_						:     Optional[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Dict							     =  steps
       lowercase_						:     Optional[int]							     =  scale
       lowercase_						:     Optional[int]							     =  pipe(**a	,  control_guidance_start=0.1	,  control_guidance_end=0.2      )[0]

       lowercase_						:     List[str]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Any							     =  steps
       lowercase_						:     Optional[int]							     =  scale
       lowercase_						:     str							     =  pipe(**a	,  control_guidance_start=[0.1, 0.3]	,  control_guidance_end=[0.2, 0.7]      )[0]

       lowercase_						:     Union[str, Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     Union[str, Any]							     =  steps
       lowercase_						:     Optional[Any]							     =  scale
       lowercase_						:     Dict							     =  pipe(**a	,  control_guidance_start=0.4	,  control_guidance_end=[0.5, 0.8]      )[0]

       # make sure that all outputs are different
       assert np.sum(np.abs(output_a - output_a      )      ) > 1e-3
       assert np.sum(np.abs(output_a - output_a      )      ) > 1e-3
       assert np.sum(np.abs(output_a - output_a      )      ) > 1e-3




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3      )




    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()	,  reason="XFormers attention is only available with CUDA and `xformers` installed"	,  )
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       self._test_inference_batch_single_identical(expected_max_diff=2e-3      )





    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.get_dummy_components()
       lowercase_						:     List[str]							     =  self.pipeline_class(**a      )
       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )
       with tempfile.TemporaryDirectory() as tmpdir:
          try:
             # save_pretrained is not implemented for Multi-ControlNet
             pipe.save_pretrained(a      )
          except NotImplementedError:
             pass


@slow
@require_torch_gpu
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"      )

       lowercase_						:     int							     =  StableDiffusionControlNetImgaImgPipeline.from_pretrained(
           "runwayml/stable-diffusion-v1-5"	,  safety_checker=a	,  controlnet=a      )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Optional[int]							     =  torch.Generator(device="cpu"      ).manual_seed(0      )
       lowercase_						:     List[str]							     =  "evil space-punk bird"
       lowercase_						:     Union[str, Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"      ).resize((5_1_2, 5_1_2)      )
       lowercase_						:     List[str]							     =  load_image(
           "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"      ).resize((5_1_2, 5_1_2)      )

       lowercase_						:     int							     =  pipe(
           a	,  a	,  control_image=a	,  generator=a	,  output_type="np"	,  num_inference_steps=5_0	,  strength=0.6	,  )

       lowercase_						:     Any							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)

       lowercase_						:     Optional[Any]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"      )

       assert np.abs(expected_image - image      ).max() < 9e-2




