





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 UpperCAmelCase_							(   snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase   ):

  '''simple docstring'''





  __lowerCAmelCase      :	Dict  	=      StableDiffusionModelEditingPipeline
  __lowerCAmelCase      :	Tuple  	=      TEXT_TO_IMAGE_PARAMS
  __lowerCAmelCase      :	str  	=      TEXT_TO_IMAGE_BATCH_PARAMS
  __lowerCAmelCase      :	Tuple  	=      TEXT_TO_IMAGE_IMAGE_PARAMS
  __lowerCAmelCase      :	Any  	=      TEXT_TO_IMAGE_IMAGE_PARAMS


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       torch.manual_seed(0   )
       A_					      =					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   ,)
       A_					      =					DDIMScheduler()
       torch.manual_seed(0   )
       A_					      =					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   )
       A_					      =					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   ,)
       A_					      =					CLIPTextModel(__snake_case   )
       A_					      =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       A_					      =					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case=0   ):



       """simple docstring"""





       A_					      =					torch.manual_seed(__snake_case   )
       A_					      =					{
           '''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 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A_					      =					self.get_dummy_components()
       A_					      =					StableDiffusionModelEditingPipeline(**__snake_case   )
       A_					      =					sd_pipe.to(__snake_case   )
       sd_pipe.set_progress_bar_config(disable=__snake_case   )

       A_					      =					self.get_dummy_inputs(__snake_case   )
       A_					      =					sd_pipe(**__snake_case   ).images
       A_					      =					image[0, -3:, -3:, -1]
       assert image.shape == (1, 6_4, 6_4, 3)

       A_					      =					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 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A_					      =					self.get_dummy_components()
       A_					      =					StableDiffusionModelEditingPipeline(**__snake_case   )
       A_					      =					sd_pipe.to(__snake_case   )
       sd_pipe.set_progress_bar_config(disable=__snake_case   )

       A_					      =					self.get_dummy_inputs(__snake_case   )
       A_					      =					'''french fries'''
       A_					      =					sd_pipe(**__snake_case   ,negative_prompt=__snake_case   )
       A_					      =					output.images
       A_					      =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       A_					      =					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 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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

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

       assert image.shape == (1, 6_4, 6_4, 3)

       A_					      =					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 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A_					      =					self.get_dummy_components()
       A_					      =					PNDMScheduler()
       A_					      =					StableDiffusionModelEditingPipeline(**__snake_case   )
       A_					      =					sd_pipe.to(__snake_case   )
       sd_pipe.set_progress_bar_config(disable=__snake_case   )

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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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





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

  '''simple docstring'''





  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ,__snake_case=0   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					'''CompVis/stable-diffusion-v1-4'''
       A_					      =					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()

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

       assert image.shape == (1, 5_1_2, 5_1_2, 3)

       A_					      =					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'''   )

       A_					      =					pipe(**__snake_case   ).images
       A_					      =					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_1_2, 5_1_2, 3)

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




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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

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

       A_					      =					self.get_inputs()
       A_					      =					pipe(**__snake_case   )

       A_					      =					torch.cuda.max_memory_allocated()
       # make sure that less than 4.4 GB is allocated
       assert mem_bytes < 4.4 * 1_0**9










from __future__ import annotations

def     UpperCAmelCase_						(						_UpperCAmelCase       :list							)  ->							int | float:
     '''simple docstring'''


     A_					      =					sorted(_UpperCAmelCase							)
     A_					      =					len(_UpperCAmelCase							)
     A_					      =					length >> 1
     return (
         (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
         if length % 2 == 0
         else sorted_list[mid_index]
     )

def     UpperCAmelCase_						(						)  ->							Optional[Any]:
     '''simple docstring'''


     import doctest

     doctest.testmod()


if __name__ == "__main__":
      main()



