



'''simple docstring'''





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





@skip_mps
class 							lowerCAmelCase_		(	A__  ,      A__  ,      A__  ,      unittest.TestCase						):
    __UpperCAmelCase							=	StableDiffusionModelEditingPipeline
    __UpperCAmelCase							=	TEXT_TO_IMAGE_PARAMS
    __UpperCAmelCase							=	TEXT_TO_IMAGE_BATCH_PARAMS
    __UpperCAmelCase							=	TEXT_TO_IMAGE_IMAGE_PARAMS
    __UpperCAmelCase							=	TEXT_TO_IMAGE_IMAGE_PARAMS





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       torch.manual_seed(0     )
       snake_case   :							Dict						  =UNetaDConditionModel(
           block_out_channels=(32, 64),    layers_per_block=2,    sample_size=32,    in_channels=4,    out_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),    cross_attention_dim=32,    )
       snake_case   :							List[str]						  =DDIMScheduler()
       torch.manual_seed(0     )
       snake_case   :							Optional[Any]						  =AutoencoderKL(
           block_out_channels=[32, 64],    in_channels=3,    out_channels=3,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],    latent_channels=4,    )
       torch.manual_seed(0     )
       snake_case   :							Union[str, 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=1_000,    )
       snake_case   :							List[str]						  =CLIPTextModel(__snake_case     )
       snake_case   :							List[str]						  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

       snake_case   :							List[Any]						  ={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    Any,    _snake_case    :    Tuple=0     ):



       '''simple docstring'''







       snake_case   :							int						  =torch.manual_seed(__snake_case     )
       snake_case   :							Dict						  ={
           '''prompt''': '''A field of roses''',
           '''generator''': generator,
           # Setting height and width to None to prevent OOMs on CPU.
           '''height''': None,
           '''width''': None,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case   :							int						  =self.get_dummy_components()
       snake_case   :							Optional[Any]						  =StableDiffusionModelEditingPipeline(**__snake_case     )
       snake_case   :							Any						  =sd_pipe.to(__snake_case     )
       sd_pipe.set_progress_bar_config(disable=__snake_case     )

       snake_case   :							int						  =self.get_dummy_inputs(__snake_case     )
       snake_case   :							Optional[Any]						  =sd_pipe(**__snake_case     ).images
       snake_case   :							Tuple						  =image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       snake_case   :							Optional[int]						  =np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case   :							Optional[Any]						  =self.get_dummy_components()
       snake_case   :							str						  =StableDiffusionModelEditingPipeline(**__snake_case     )
       snake_case   :							List[str]						  =sd_pipe.to(__snake_case     )
       sd_pipe.set_progress_bar_config(disable=__snake_case     )

       snake_case   :							int						  =self.get_dummy_inputs(__snake_case     )
       snake_case   :							Union[str, Any]						  ='''french fries'''
       snake_case   :							Optional[Any]						  =sd_pipe(**__snake_case,    negative_prompt=__snake_case     )
       snake_case   :							Tuple						  =output.images
       snake_case   :							Dict						  =image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       snake_case   :							Optional[Any]						  =np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Any						  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case   :							Any						  =self.get_dummy_components()
       snake_case   :							Any						  =EulerAncestralDiscreteScheduler(
           beta_start=0.0_0085,    beta_end=0.012,    beta_schedule='''scaled_linear'''     )
       snake_case   :							Any						  =StableDiffusionModelEditingPipeline(**__snake_case     )
       snake_case   :							Union[str, Any]						  =sd_pipe.to(__snake_case     )
       sd_pipe.set_progress_bar_config(disable=__snake_case     )

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

       assert image.shape == (1, 64, 64, 3)

       snake_case   :							List[str]						  =np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2





    def 					__snake_case       (    self    :    str     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case   :							Tuple						  =self.get_dummy_components()
       snake_case   :							int						  =PNDMScheduler()
       snake_case   :							Any						  =StableDiffusionModelEditingPipeline(**__snake_case     )
       snake_case   :							Union[str, Any]						  =sd_pipe.to(__snake_case     )
       sd_pipe.set_progress_bar_config(disable=__snake_case     )

       snake_case   :							Union[str, Any]						  =self.get_dummy_inputs(__snake_case     )
       # the pipeline does not expect pndm so test if it raises error.
       with self.assertRaises(__snake_case     ):
          snake_case   :							List[Any]						  =sd_pipe(**__snake_case     ).images





    def 					__snake_case       (    self    :    int     ):



       '''simple docstring'''







       super().test_inference_batch_single_identical(expected_max_diff=5E-3     )






    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       super().test_attention_slicing_forward_pass(expected_max_diff=5E-3     )





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





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    int,    _snake_case    :    Optional[int]=0     ):



       '''simple docstring'''







       snake_case   :							Any						  =torch.manual_seed(__snake_case     )
       snake_case   :							int						  ={
           '''prompt''': '''A field of roses''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  ='''CompVis/stable-diffusion-v1-4'''
       snake_case   :							Dict						  =StableDiffusionModelEditingPipeline.from_pretrained(__snake_case,    safety_checker=__snake_case     )
       pipe.to(__snake_case     )
       pipe.set_progress_bar_config(disable=__snake_case     )
       pipe.enable_attention_slicing()

       snake_case   :							Dict						  =self.get_inputs()
       snake_case   :							Optional[Any]						  =pipe(**__snake_case     ).images
       snake_case   :							str						  =image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)

       snake_case   :							int						  =np.array(
           [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]     )

       assert np.abs(expected_slice - image_slice     ).max() < 1E-2

       # make sure image changes after editing
       pipe.edit_model('''A pack of roses''',    '''A pack of blue roses'''     )

       snake_case   :							Dict						  =pipe(**__snake_case     ).images
       snake_case   :							Union[str, Any]						  =image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(expected_slice - image_slice     ).max() > 1E-1






    def 					__snake_case       (    self    :    str     ):



       '''simple docstring'''







       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case   :							Any						  ='''CompVis/stable-diffusion-v1-4'''
       snake_case   :							int						  =DDIMScheduler.from_pretrained(__snake_case,    subfolder='''scheduler'''     )
       snake_case   :							int						  =StableDiffusionModelEditingPipeline.from_pretrained(
           __snake_case,    scheduler=__snake_case,    safety_checker=__snake_case     )
       snake_case   :							Tuple						  =pipe.to(__snake_case     )
       pipe.set_progress_bar_config(disable=__snake_case     )
       pipe.enable_attention_slicing(1     )
       pipe.enable_sequential_cpu_offload()

       snake_case   :							List[str]						  =self.get_inputs()
       snake_case   :							int						  =pipe(**__snake_case     )

       snake_case   :							Union[str, Any]						  =torch.cuda.max_memory_allocated()
       # make sure that less than 4.4 GB is allocated
       assert mem_bytes < 4.4 * 10**9






'''simple docstring'''





import doctest

import projectq
from projectq.ops import H, Measure
def 		_a   (  lowerCamelCase_							):
   snake_case   :							str						  =quantum_engine.allocate_qubit()
   H | qubit
   Measure | qubit
   return int(lowerCamelCase_							)


if __name__ == "__main__":
      doctest.testmod()

      # initialises a new quantum backend
      A			:   Dict			=      projectq.MainEngine()

      # Generate a list of 10 random numbers
      A			:   Dict			=      [get_random_number(quantum_engine) for _ in range(10)]

      # Flushes the quantum engine from memory
      quantum_engine.flush()

      print("""Random numbers""", random_numbers_list)





