






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   UpperCamelCase  (       snake_case__     :   Optional[int]  ):




     '''simple docstring'''






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




class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= StableDiffusionLatentUpscalePipeline
       lowerCamelCase							:				Dict 				= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       lowerCamelCase							:				int 				= PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       lowerCamelCase							:				Tuple 				= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       lowerCamelCase							:				str 				= frozenset(
           [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       lowerCamelCase							:				List[Any] 				= frozenset([])

       lowerCamelCase							:				List[Any] 				= True



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:int          =						1
            __snake_case			:Dict          =						4
            __snake_case			:List[str]          =						(16, 16)

            __snake_case			:List[str]          =						floats_tensor((batch_size, num_channels) + sizes			,						rng=random.Random(0    )    ).to(a__    )
            return image



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            torch.manual_seed(0    )
            __snake_case			:List[Any]          =						UNetaDConditionModel(
                act_fn="""gelu"""			,						attention_head_dim=8			,						norm_num_groups=a__			,						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=a__			,						only_cross_attention=a__			,						out_channels=5			,						resnet_time_scale_shift="""scale_shift"""			,						time_embedding_type="""fourier"""			,						timestep_post_act="""gelu"""			,						up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")			,						)
            __snake_case			:Dict          =						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			,						)
            __snake_case			:Dict          =						EulerDiscreteScheduler(prediction_type="""sample"""    )
            __snake_case			:Any          =						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			,						)
            __snake_case			:str          =						CLIPTextModel(a__    )
            __snake_case			:Tuple          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

            __snake_case			:Optional[int]          =						{
                """unet""": model.eval(),
                """vae""": vae.eval(),
                """scheduler""": scheduler,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
            }

            return components



       def 					__lowercase						(							self			,						a__			,						a__=0    ) ->      List[Any]:






            '''simple docstring'''





            if str(a__    ).startswith("""mps"""    ):
                 __snake_case			:Dict          =						torch.manual_seed(a__    )
            else:
                 __snake_case			:Dict          =						torch.Generator(device=a__    ).manual_seed(a__    )
            __snake_case			:Union[str, 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 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						"""cpu"""

            __snake_case			:Dict          =						self.get_dummy_components()
            __snake_case			:List[str]          =						self.pipeline_class(**a__    )
            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )

            __snake_case			:Optional[int]          =						self.get_dummy_inputs(a__    )
            __snake_case			:List[Any]          =						pipe(**a__    ).images
            __snake_case			:List[Any]          =						image[0, -3:, -3:, -1]

            self.assertEqual(image.shape			,						(1, 2_56, 2_56, 3)    )
            __snake_case			:Dict          =						np.array(
                [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]    )
            __snake_case			:int          =						np.abs(image_slice.flatten() - expected_slice    ).max()
            self.assertLessEqual(a__			,						1e-3    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            super().test_attention_slicing_forward_pass(expected_max_diff=7e-3    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().test_cpu_offload_forward_pass(expected_max_diff=3e-3    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().test_inference_batch_single_identical(expected_max_diff=7e-3    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            super().test_save_load_local(expected_max_difference=3e-3    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            super().test_save_load_optional_components(expected_max_difference=3e-3    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Dict          =						[
                """DDIMScheduler""",
                """DDPMScheduler""",
                """PNDMScheduler""",
                """HeunDiscreteScheduler""",
                """EulerAncestralDiscreteScheduler""",
                """KDPM2DiscreteScheduler""",
                """KDPM2AncestralDiscreteScheduler""",
                """DPMSolverSDEScheduler""",
            ]
            __snake_case			:List[str]          =						self.get_dummy_components()
            __snake_case			:Optional[Any]          =						self.pipeline_class(**a__    )

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

            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )
            __snake_case			:Dict          =						self.get_dummy_inputs(a__    )
            __snake_case			:List[str]          =						2

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

                 __snake_case			:List[str]          =						getattr(a__			,						scheduler_enum.name    )
                 __snake_case			:Union[str, Any]          =						scheduler_cls.from_config(pipe.scheduler.config    )
                 __snake_case			:Union[str, Any]          =						pipe(**a__    )[0]
                 outputs.append(a__    )

            assert check_same_shape(a__    )




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

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:int          =						torch.manual_seed(33    )

            __snake_case			:Optional[int]          =						StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""			,						torch_dtype=torch.floataa    )
            pipe.to("""cuda"""    )

            __snake_case			:Optional[int]          =						StableDiffusionLatentUpscalePipeline.from_pretrained(
                """stabilityai/sd-x2-latent-upscaler"""			,						torch_dtype=torch.floataa    )
            upscaler.to("""cuda"""    )

            __snake_case			:Dict          =						"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

            __snake_case			:Any          =						pipe(a__			,						generator=a__			,						output_type="""latent"""    ).images

            __snake_case			:Any          =						upscaler(
                prompt=a__			,						image=a__			,						num_inference_steps=20			,						guidance_scale=0			,						generator=a__			,						output_type="""np"""			,						).images[0]

            __snake_case			:Dict          =						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 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						torch.manual_seed(33    )

            __snake_case			:Any          =						StableDiffusionLatentUpscalePipeline.from_pretrained(
                """stabilityai/sd-x2-latent-upscaler"""			,						torch_dtype=torch.floataa    )
            upscaler.to("""cuda"""    )

            __snake_case			:Dict          =						"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

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

            __snake_case			:Any          =						upscaler(
                prompt=a__			,						image=a__			,						num_inference_steps=20			,						guidance_scale=0			,						generator=a__			,						output_type="""np"""			,						).images[0]

            __snake_case			:Optional[Any]          =						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










import numpy as np
import qiskit






def   UpperCamelCase  (       snake_case__     :   int = 8	,snake_case__     :   int | None = None  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						np.random.default_rng(seed=snake_case__  )

     # Roughly 25% of the qubits will contribute to the key.
     # So we take more than we need.
     __snake_case			:Union[str, Any]          =						6 * key_len
     # Measurement basis for Alice's qubits.
     __snake_case			:int          =						rng.integers(2	,size=snake_case__  )
     # The set of states Alice will prepare.
     __snake_case			:Dict          =						rng.integers(2	,size=snake_case__  )
     # Measurement basis for Bob's qubits.
     __snake_case			:str          =						rng.integers(2	,size=snake_case__  )

     # Quantum Circuit to simulate BB84
     __snake_case			:Optional[int]          =						qiskit.QuantumCircuit(snake_case__	,name="""BB84"""  )

     # Alice prepares her qubits according to rules above.
     for index, _ in enumerate(snake_case__  ):
          if alice_state[index] == 1:
               bbaa_circ.x(snake_case__  )
          if alice_basis[index] == 1:
               bbaa_circ.h(snake_case__  )
     bbaa_circ.barrier()

     # Bob measures the received qubits according to rules above.
     for index, _ in enumerate(snake_case__  ):
          if bob_basis[index] == 1:
               bbaa_circ.h(snake_case__  )

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     __snake_case			:Optional[int]          =						qiskit.Aer.get_backend("""aer_simulator"""  )
     # We only need to run one shot because the key is unique.
     # Multiple shots will produce the same key.
     __snake_case			:List[Any]          =						qiskit.execute(snake_case__	,snake_case__	,shots=1	,seed_simulator=snake_case__  )
     # Returns the result of measurement.
     __snake_case			:Tuple          =						job.result().get_counts(snake_case__  ).most_frequent()

     # Extracting the generated key from the simulation results.
     # Only keep measurement results where Alice and Bob chose the same basis.
     __snake_case			:Any          =						"""""".join(
         [
             result_bit
             for alice_basis_bit, bob_basis_bit, result_bit in zip(
                 snake_case__	,snake_case__	,snake_case__  )
             if alice_basis_bit == bob_basis_bit
         ]  )

     # Get final key. Pad with 0 if too short, otherwise truncate.
     __snake_case			:Dict          =						gen_key[:key_len] if len(snake_case__  ) >= key_len else gen_key.ljust(snake_case__	,"""0"""  )
     return key


if __name__ == "__main__":
     print(f'''The generated key is : {bbaa(8, seed=0)}''')
     from doctest import testmod

     testmod()


