

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				(	__snake_case  ,							__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      int          =					StableDiffusionModelEditingPipeline
      lowerCamelCase_	:      Optional[Any]          =					TEXT_TO_IMAGE_PARAMS
      lowerCamelCase_	:      Union[str, Any]          =					TEXT_TO_IMAGE_BATCH_PARAMS
      lowerCamelCase_	:      Dict          =					TEXT_TO_IMAGE_IMAGE_PARAMS
      lowerCamelCase_	:      Optional[Any]          =					TEXT_TO_IMAGE_IMAGE_PARAMS







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        torch.manual_seed(0   )
        snake_case_       						=      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_       						=      DDIMScheduler()
        torch.manual_seed(0   )
        snake_case_       						=      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_       						=      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=1000							,  )
        snake_case_       						=      CLIPTextModel(lowerCamelCase   )
        snake_case_       						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase=0   )						->       Dict:
        snake_case_       						=      torch.manual_seed(lowerCamelCase   )
        snake_case_       						=      {
            """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 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case_       						=      self.get_dummy_components()
        snake_case_       						=      StableDiffusionModelEditingPipeline(**lowerCamelCase   )
        snake_case_       						=      sd_pipe.to(lowerCamelCase   )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase   )

        snake_case_       						=      self.get_dummy_inputs(lowerCamelCase   )
        snake_case_       						=      sd_pipe(**lowerCamelCase   ).images
        snake_case_       						=      image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case_       						=      self.get_dummy_components()
        snake_case_       						=      StableDiffusionModelEditingPipeline(**lowerCamelCase   )
        snake_case_       						=      sd_pipe.to(lowerCamelCase   )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase   )

        snake_case_       						=      self.get_dummy_inputs(lowerCamelCase   )
        snake_case_       						=      """french fries"""
        snake_case_       						=      sd_pipe(**lowerCamelCase							,  negative_prompt=lowerCamelCase   )
        snake_case_       						=      output.images
        snake_case_       						=      image[0, -3:, -3:, -1]

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

        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case_       						=      self.get_dummy_components()
        snake_case_       						=      EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085							,  beta_end=0.012							,  beta_schedule="""scaled_linear"""   )
        snake_case_       						=      StableDiffusionModelEditingPipeline(**lowerCamelCase   )
        snake_case_       						=      sd_pipe.to(lowerCamelCase   )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase   )

        snake_case_       						=      self.get_dummy_inputs(lowerCamelCase   )
        snake_case_       						=      sd_pipe(**lowerCamelCase   ).images
        snake_case_       						=      image[0, -3:, -3:, -1]

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

        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case_       						=      self.get_dummy_components()
        snake_case_       						=      PNDMScheduler()
        snake_case_       						=      StableDiffusionModelEditingPipeline(**lowerCamelCase   )
        snake_case_       						=      sd_pipe.to(lowerCamelCase   )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase   )

        snake_case_       						=      self.get_dummy_inputs(lowerCamelCase   )
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowerCamelCase   ):
          snake_case_       						=      sd_pipe(**lowerCamelCase   ).images







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        super().test_inference_batch_single_identical(expected_max_diff=5e-3   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )







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







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase=0   )						->       Dict:
        snake_case_       						=      torch.manual_seed(lowerCamelCase   )
        snake_case_       						=      {
            """prompt""": """A field of roses""",
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      """CompVis/stable-diffusion-v1-4"""
        snake_case_       						=      StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase							,  safety_checker=lowerCamelCase   )
        pipe.to(lowerCamelCase   )
        pipe.set_progress_bar_config(disable=lowerCamelCase   )
        pipe.enable_attention_slicing()

        snake_case_       						=      self.get_inputs()
        snake_case_       						=      pipe(**lowerCamelCase   ).images
        snake_case_       						=      image[0, -3:, -3:, -1].flatten()

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

        snake_case_       						=      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_       						=      pipe(**lowerCamelCase   ).images
        snake_case_       						=      image[0, -3:, -3:, -1].flatten()

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

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







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        snake_case_       						=      """CompVis/stable-diffusion-v1-4"""
        snake_case_       						=      DDIMScheduler.from_pretrained(lowerCamelCase							,  subfolder="""scheduler"""   )
        snake_case_       						=      StableDiffusionModelEditingPipeline.from_pretrained(
            lowerCamelCase							,  scheduler=lowerCamelCase							,  safety_checker=lowerCamelCase   )
        snake_case_       						=      pipe.to(lowerCamelCase   )
        pipe.set_progress_bar_config(disable=lowerCamelCase   )
        pipe.enable_attention_slicing(1   )
        pipe.enable_sequential_cpu_offload()

        snake_case_       						=      self.get_inputs()
        snake_case_       						=      pipe(**lowerCamelCase   )

        snake_case_       						=      torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 10**9


from typing import Callable



def    UpperCamelCase(					lowercase_							)	->				List[str]:





  '''simple docstring'''





  def wrapper(lowercase_							):
    snake_case_       						=      function_with_docstring.__doc__
    return target_function

  return wrapper