'''simple docstring'''




import qiskit

def _lowercase	(      lowerCamelCase__ = 2						)					->       qiskit.result.counts.Counts:





							"""simple docstring"""


							__UpperCAmelCase  :				Any       				=		qubits

							# Using Aer's simulator
							__UpperCAmelCase  :				Any       				=		qiskit.Aer.get_backend("aer_simulator"						)

							# Creating a Quantum Circuit acting on the q register
							__UpperCAmelCase  :				Any       				=		qiskit.QuantumCircuit(lowerCamelCase__							,    lowerCamelCase__						)

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

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

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

							# 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  :				Union[str, Any]       				=		qiskit.execute(lowerCamelCase__							,    lowerCamelCase__							,    shots=1000						)

							return job.result().get_counts(lowerCamelCase__						)


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







'''simple docstring'''




import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]						)






class 		__A       (__magic_name__ ,					__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					StableDiffusionLatentUpscalePipeline
       snake_case       :Optional[int]				    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       snake_case       :List[str]				    =					PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       snake_case       :Optional[Any]				    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       snake_case       :Optional[Any]				    =					frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       snake_case       :Any				    =					frozenset([]   )

       snake_case       :Optional[int]				    =					True






       @property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		1
              __UpperCAmelCase  :				Dict       				=		4
              __UpperCAmelCase  :				List[str]       				=		(16, 16)

              __UpperCAmelCase  :				Dict       				=		floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0      )      ).to(UpperCamelCase_      )
              return image






       def 			_snake_case						(      self      ):
              torch.manual_seed(0      )
              __UpperCAmelCase  :				List[str]       				=		UNetaDConditionModel(
                  act_fn="gelu"				,     attention_head_dim=8				,     norm_num_groups=UpperCamelCase_				,     block_out_channels=[32, 32, 64, 64]				,     time_cond_proj_dim=1_60				,     conv_in_kernel=1				,     conv_out_kernel=1				,     cross_attention_dim=32				,     down_block_types=(
                      "KDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                  )				,     in_channels=8				,     mid_block_type=UpperCamelCase_				,     only_cross_attention=UpperCamelCase_				,     out_channels=5				,     resnet_time_scale_shift="scale_shift"				,     time_embedding_type="fourier"				,     timestep_post_act="gelu"				,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")				,     )
              __UpperCAmelCase  :				int       				=		AutoencoderKL(
                  block_out_channels=[32, 32, 64, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=[
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                  ]				,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]				,     latent_channels=4				,     )
              __UpperCAmelCase  :				Optional[int]       				=		EulerDiscreteScheduler(prediction_type="sample"      )
              __UpperCAmelCase  :				int       				=		CLIPTextConfig(
                  bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     hidden_act="quick_gelu"				,     projection_dim=5_12				,     )
              __UpperCAmelCase  :				List[str]       				=		CLIPTextModel(UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		{
                  "unet": model.eval(),
                  "vae": vae.eval(),
                  "scheduler": scheduler,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
              }

              return components






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=0      ):
              if str(UpperCamelCase_      ).startswith("mps"      ):
                     __UpperCAmelCase  :				str       				=		torch.manual_seed(UpperCamelCase_      )
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		{
                  "prompt": "A painting of a squirrel eating a burger",
                  "image": self.dummy_image.cpu(),
                  "generator": generator,
                  "num_inference_steps": 2,
                  "output_type": "numpy",
              }
              return inputs






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		"cpu"

              __UpperCAmelCase  :				List[str]       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Tuple       				=		self.pipeline_class(**UpperCamelCase_      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				Any       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		pipe(**UpperCamelCase_      ).images
              __UpperCAmelCase  :				Any       				=		image[0, -3:, -3:, -1]

              self.assertEqual(image.shape				,     (1, 2_56, 2_56, 3)      )
              __UpperCAmelCase  :				Tuple       				=		np.array(
                  [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]      )
              __UpperCAmelCase  :				List[str]       				=		np.abs(image_slice.flatten() - expected_slice      ).max()
              self.assertLessEqual(UpperCamelCase_				,     1E-3      )






       def 			_snake_case						(      self      ):
              super().test_attention_slicing_forward_pass(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_cpu_offload_forward_pass(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_inference_batch_single_identical(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_local(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_optional_components(expected_max_difference=3E-3      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		[
                  "DDIMScheduler",
                  "DDPMScheduler",
                  "PNDMScheduler",
                  "HeunDiscreteScheduler",
                  "EulerAncestralDiscreteScheduler",
                  "KDPM2DiscreteScheduler",
                  "KDPM2AncestralDiscreteScheduler",
                  "DPMSolverSDEScheduler",
              ]
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Union[str, Any]       				=		self.pipeline_class(**UpperCamelCase_      )

              # make sure that PNDM does not need warm-up
              pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_      )

              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		2

              __UpperCAmelCase  :				List[str]       				=		[]
              for scheduler_enum in KarrasDiffusionSchedulers:
                     if scheduler_enum.name in skip_schedulers:
                            # no sigma schedulers are not supported
                            # no schedulers
                            continue

                     __UpperCAmelCase  :				Optional[int]       				=		getattr(UpperCamelCase_				,     scheduler_enum.name      )
                     __UpperCAmelCase  :				List[str]       				=		scheduler_cls.from_config(pipe.scheduler.config      )
                     __UpperCAmelCase  :				Optional[int]       				=		pipe(**UpperCamelCase_      )[0]
                     outputs.append(UpperCamelCase_      )

              assert check_same_shape(UpperCamelCase_      )






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






       def 			_snake_case						(      self      ):
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				str       				=		StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"				,     torch_dtype=torch.floataa      )
              pipe.to("cuda"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[int]       				=		"a photo of an astronaut high resolution, unreal engine, ultra realistic"

              __UpperCAmelCase  :				Any       				=		pipe(UpperCamelCase_				,     generator=UpperCamelCase_				,     output_type="latent"      ).images

              __UpperCAmelCase  :				int       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Optional[Any]       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"      )
              assert np.abs((expected_image - image).mean()      ) < 5E-2







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[Any]       				=		"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

              __UpperCAmelCase  :				str       				=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

              __UpperCAmelCase  :				Dict       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Tuple       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"      )
              assert np.abs((expected_image - image).max()      ) < 5E-2






