





from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

snake_case__   :				int						=	TypeVar('T', bound=bool)





class A_   (     Generic[T]  ):




   def __init__(self      :Dict       ,  _UpperCamelCase      :Optional[int]					)->   None:
         __A			  =							value
         __A			  =							None
         __A			  =							None




   @property
   def _lowerCAmelCase (self      :Optional[int]					)->   T:
         return self._value






   @staticmethod
   def _lowerCAmelCase (_UpperCamelCase      :Dict       ,  _UpperCamelCase      :List[Any]					)->   SkewNode[T] | None:
         if not roota:
               return roota

         if not roota:
               return roota

         if roota.value > roota.value:
               __A			  =							roota, roota

         __A			  =							roota
         __A			  =							roota.right
         __A			  =							roota.left
         __A			  =							SkewNode.merge(_lowerCamelCase       ,  _lowerCamelCase					)

         return result





class A_   (     Generic[T]  ):




   def __init__(self      :Union[str, Any]       ,  _UpperCamelCase      :Any = ()					)->   None:
         __A			  =							None
         if data:
               for item in data:
                     self.insert(_lowerCamelCase					)




   def __bool__(self      :List[Any]					)->   bool:
         return self._root is not None




   def __iter__(self      :str					)->   Iterator[T]:
         __A			  =							[]
         while self:
               result.append(self.pop()					)

         # Pushing items back to the heap not to clear it.
         for item in result:
               self.insert(_lowerCamelCase					)

         return iter(_lowerCamelCase					)




   def _lowerCAmelCase (self      :Optional[int]       ,  _UpperCamelCase      :List[Any]					)->   None:
         __A			  =							SkewNode.merge(self._root       ,  SkewNode(_lowerCamelCase					)					)




   def _lowerCAmelCase (self      :int					)->   T | None:
         __A			  =							self.top()
         __A			  =							(
             SkewNode.merge(self._root.left       ,  self._root.right					) if self._root else None
         )

         return result




   def _lowerCAmelCase (self      :Tuple					)->   T:
         if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''					)
         return self._root.value






   def _lowerCAmelCase (self      :Optional[int]					)->   None:
         __A			  =							None


if __name__ == "__main__":
       import doctest

       doctest.testmod()







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     a_       (			a__				,    a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  StableDiffusionModelEditingPipeline
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  TEXT_TO_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					int						=  TEXT_TO_IMAGE_BATCH_PARAMS
       __SCREAMING_SNAKE_CASE       :					Any						=  TEXT_TO_IMAGE_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  TEXT_TO_IMAGE_IMAGE_PARAMS






       def 	__lowerCAmelCase     (		self   )					->int:
           torch.manual_seed(0   )
           SCREAMING_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				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = DDIMScheduler()
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Union[str, 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   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = 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				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = CLIPTextModel(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

           SCREAMING_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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=0   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {
               '''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   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Any				      = image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				List[str]				      = np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]   )

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






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''french fries'''
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe(**_lowerCamelCase				,       negative_prompt=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = output.images
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image[0, -3:, -3:, -1]

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

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]   )

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






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				List[Any]				      = EulerAncestralDiscreteScheduler(
               beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       beta_schedule='''scaled_linear'''   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image[0, -3:, -3:, -1]

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

           SCREAMING_SNAKE_CASE				:				str				      = np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]   )

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






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = PNDMScheduler()
           SCREAMING_SNAKE_CASE				:				Tuple				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           # the pipeline does not expect pndm so test if it raises error.
           with self.assertRaises(_lowerCamelCase   ):
               SCREAMING_SNAKE_CASE				:				Dict				      = sd_pipe(**_lowerCamelCase   ).images






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().test_inference_batch_single_identical(expected_max_diff=5e-3   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )






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



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=0   )					->Dict:
           SCREAMING_SNAKE_CASE				:				str				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				List[str]				      = StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing()

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image[0, -3:, -3:, -1].flatten()

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

           SCREAMING_SNAKE_CASE				:				str				      = np.array(
               [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]   )

           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'''   )

           SCREAMING_SNAKE_CASE				:				Any				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				List[str]				      = 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   )					->str:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				Any				      = DDIMScheduler.from_pretrained(_lowerCamelCase				,       subfolder='''scheduler'''   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline.from_pretrained(
               _lowerCamelCase				,       scheduler=_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing(1   )
           pipe.enable_sequential_cpu_offload()

           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   )

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