
import logging
import os
import threading
import time


try:
   import warnings
except ImportError:
   __lowerCamelCase				=			None

try:
   import msvcrt
except ImportError:
   __lowerCamelCase				=			None

try:
   import fcntl
except ImportError:
   __lowerCamelCase				=			None


# Backward compatibility
# ------------------------------------------------
try:
   TimeoutError
except NameError:
   __lowerCamelCase				=			OSError


# Data
# ------------------------------------------------
__lowerCamelCase				=			[
    """Timeout""",
    """BaseFileLock""",
    """WindowsFileLock""",
    """UnixFileLock""",
    """SoftFileLock""",
    """FileLock""",
]

__lowerCamelCase				=			"""3.0.12"""


__lowerCamelCase				=			None


def      UpperCamelCase   (       ):
       global _logger
       snake_case			:  Optional[Any]     							= _logger or logging.getLogger(__name__       )
       return _logger






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   List[Any]       ,		snake_case__						:   Dict					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= lock_file
          return None



   def __str__(self						:   str					)						->      Dict:


          '''simple docstring'''


          snake_case			:  str     							= f"""The file lock '{self.lock_file}' could not be acquired."""
          return temp






class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   Union[str, Any]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Any     							= lock
          return None



   def __enter__(self						:   List[str]					)						->      Dict:


          '''simple docstring'''


          return self.lock



   def __exit__(self						:   List[Any]       ,		snake_case__						:   List[str]       ,		snake_case__						:   int       ,		snake_case__						:   Union[str, Any]					)						->      str:


          '''simple docstring'''


          self.lock.release()
          return None






class     UpperCAmelCase						:



   def __init__(self						:   List[Any]       ,		snake_case__						:   Dict       ,		snake_case__						:   Any=-1       ,		snake_case__						:   Union[str, Any]=None					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  int     							= max_filename_length if max_filename_length is not None else 2_55
          # Hash the filename if it's too long
          snake_case			:  Any     							= self.hash_filename_if_too_long(snake_case__       ,		snake_case__					)
          # The path to the lock file.
          snake_case			:  Union[str, Any]     							= lock_file

          # The file descriptor for the *_lock_file* as it is returned by the
          # os.open() function.
          # This file lock is only NOT None, if the object currently holds the
          # lock.
          snake_case			:  int     							= None

          # The default timeout value.
          snake_case			:  Optional[int]     							= timeout

          # We use this lock primarily for the lock counter.
          snake_case			:  List[str]     							= threading.Lock()

          # The lock counter is used for implementing the nested locking
          # mechanism. Whenever the lock is acquired, the counter is increased and
          # the lock is only released, when this value is 0 again.
          snake_case			:  Tuple     							= 0
          return None



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Tuple:


          '''simple docstring'''


          return self._lock_file



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


          '''simple docstring'''


          return self._timeout



   @timeout.setter
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		snake_case__						:   List[str]					)						->      str:


          '''simple docstring'''


          snake_case			:  List[Any]     							= float(snake_case__					)
          return None



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


          '''simple docstring'''


          raise NotImplementedError()



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Any:


          '''simple docstring'''


          raise NotImplementedError()



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          return self._lock_file_fd is not None



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Tuple=None       ,		snake_case__						:   Optional[Any]=0.05					)						->      List[Any]:


          '''simple docstring'''


          if timeout is None:
                 snake_case			:  Optional[Any]     							= self.timeout

          # Increment the number right at the beginning.
          # We can still undo it, if something fails.
          with self._thread_lock:
                 self._lock_counter += 1

          snake_case			:  List[Any]     							= id(self					)
          snake_case			:  Dict     							= self._lock_file
          snake_case			:  Any     							= time.time()
          try:
                 while True:
                        with self._thread_lock:
                               if not self.is_locked:
                                      logger().debug(f"""Attempting to acquire lock {lock_id} on {lock_filename}"""					)
                                      self._acquire()

                        if self.is_locked:
                               logger().debug(f"""Lock {lock_id} acquired on {lock_filename}"""					)
                               break
                        elif timeout >= 0 and time.time() - start_time > timeout:
                               logger().debug(f"""Timeout on acquiring lock {lock_id} on {lock_filename}"""					)
                               raise Timeout(self._lock_file					)
                        else:
                               logger().debug(
                                   f"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."""					)
                               time.sleep(snake_case__					)
          except:  # noqa
                 # Something did go wrong, so decrement the counter.
                 with self._thread_lock:
                        snake_case			:  Dict     							= max(0       ,		self._lock_counter - 1					)

                 raise
          return _Acquire_ReturnProxy(lock=self					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   Tuple=False					)						->      Optional[Any]:


          '''simple docstring'''


          with self._thread_lock:
                 if self.is_locked:
                        self._lock_counter -= 1

                        if self._lock_counter == 0 or force:
                               snake_case			:  List[Any]     							= id(self					)
                               snake_case			:  Optional[Any]     							= self._lock_file

                               logger().debug(f"""Attempting to release lock {lock_id} on {lock_filename}"""					)
                               self._release()
                               snake_case			:  Dict     							= 0
                               logger().debug(f"""Lock {lock_id} released on {lock_filename}"""					)

          return None



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


          '''simple docstring'''


          self.acquire()
          return self



   def __exit__(self						:   Dict       ,		snake_case__						:   Any       ,		snake_case__						:   List[str]       ,		snake_case__						:   int					)						->      int:


          '''simple docstring'''


          self.release()
          return None



   def __del__(self						:   Dict					)						->      int:


          '''simple docstring'''


          self.release(force=snake_case__					)
          return None



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   str       ,		snake_case__						:   int					)						->      str:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= os.path.basename(snake_case__					)
          if len(snake_case__					) > max_length and max_length > 0:
                 snake_case			:  int     							= os.path.dirname(snake_case__					)
                 snake_case			:  Optional[int]     							= str(hash(snake_case__					)					)
                 snake_case			:  Tuple     							= filename[: max_length - len(snake_case__					) - 8] + "..." + hashed_filename + ".lock"
                 return os.path.join(snake_case__       ,		snake_case__					)
          else:
                 return path






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   str       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Dict=-1       ,		snake_case__						:   Any=None					)						->      Union[str, Any]:


          '''simple docstring'''


          from .file_utils import relative_to_absolute_path

          super().__init__(snake_case__       ,		timeout=snake_case__       ,		max_filename_length=snake_case__					)
          snake_case			:  Optional[Any]     							= "\\\\?\\" + relative_to_absolute_path(self.lock_file					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= os.O_RDWR | os.O_CREAT | os.O_TRUNC

          try:
                 snake_case			:  List[Any]     							= os.open(self._lock_file       ,		snake_case__					)
          except OSError:
                 pass
          else:
                 try:
                        msvcrt.locking(snake_case__       ,		msvcrt.LK_NBLCK       ,		1					)
                 except OSError:
                        os.close(snake_case__					)
                 else:
                        snake_case			:  Tuple     							= fd
          return None



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self._lock_file_fd
          snake_case			:  Tuple     							= None
          msvcrt.locking(snake_case__       ,		msvcrt.LK_UNLCK       ,		1					)
          os.close(snake_case__					)

          try:
                 os.remove(self._lock_file					)
          # Probably another instance of the application
          # that acquired the file lock.
          except OSError:
                 pass
          return None






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   Any       ,		snake_case__						:   List[Any]       ,		snake_case__						:   int=-1       ,		snake_case__						:   Union[str, Any]=None					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Any     							= os.statvfs(os.path.dirname(snake_case__					)					).f_namemax
          super().__init__(snake_case__       ,		timeout=snake_case__       ,		max_filename_length=snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Dict     							= os.O_RDWR | os.O_CREAT | os.O_TRUNC
          snake_case			:  int     							= os.open(self._lock_file       ,		snake_case__					)

          try:
                 fcntl.flock(snake_case__       ,		fcntl.LOCK_EX | fcntl.LOCK_NB					)
          except OSError:
                 os.close(snake_case__					)
          else:
                 snake_case			:  List[Any]     							= fd
          return None



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= self._lock_file_fd
          snake_case			:  List[Any]     							= None
          fcntl.flock(snake_case__       ,		fcntl.LOCK_UN					)
          os.close(snake_case__					)
          return None






class     UpperCAmelCase						(		A_					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Tuple     							= os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
          try:
                 snake_case			:  Tuple     							= os.open(self._lock_file       ,		snake_case__					)
          except OSError:
                 pass
          else:
                 snake_case			:  Tuple     							= fd
          return None



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      int:


          '''simple docstring'''


          os.close(self._lock_file_fd					)
          snake_case			:  str     							= None

          try:
                 os.remove(self._lock_file					)
          # The file is already deleted and that's what we want.
          except OSError:
                 pass
          return None



__lowerCamelCase				=			None

if msvcrt:
   __lowerCamelCase				=			WindowsFileLock
elif fcntl:
   __lowerCamelCase				=			UnixFileLock
else:
   __lowerCamelCase				=			SoftFileLock

   if warnings is not None:
      warnings.warn("""only soft file lock is available""")







import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						Dict			=	StableDiffusionDiffEditPipeline
   A__   :						str			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
   A__   :						List[Any]			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
   A__   :						Optional[Any]			=	frozenset(
       []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   A__   :						int			=	frozenset([]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[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       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=snake_case__       ,		)
          snake_case			:  str     							= DDIMScheduler(
              beta_start=0.00085       ,		beta_end=0.012       ,		beta_schedule="scaled_linear"       ,		clip_sample=snake_case__       ,		set_alpha_to_one=snake_case__       ,		)
          snake_case			:  List[Any]     							= DDIMInverseScheduler(
              beta_start=0.00085       ,		beta_end=0.012       ,		beta_schedule="scaled_linear"       ,		clip_sample=snake_case__       ,		set_alpha_to_zero=snake_case__       ,		)
          torch.manual_seed(0					)
          snake_case			:  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       ,		sample_size=1_28       ,		)
          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=10_00       ,		hidden_act="gelu"       ,		projection_dim=5_12       ,		)
          snake_case			:  str     							= CLIPTextModel(snake_case__					)
          snake_case			:  Optional[Any]     							= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

          snake_case			:  List[str]     							= {
              "unet": unet,
              "scheduler": scheduler,
              "inverse_scheduler": inverse_scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }

          return components



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   Dict       ,		snake_case__						:   Union[str, Any]=0					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= floats_tensor((1, 16, 16)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          snake_case			:  List[Any]     							= floats_tensor((1, 2, 4, 16, 16)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  Any     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  Optional[Any]     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  Union[str, Any]     							= {
              "prompt": "a dog and a newt",
              "mask_image": mask,
              "image_latents": latents,
              "generator": generator,
              "num_inference_steps": 2,
              "inpaint_strength": 1.0,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }

          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Optional[int]=0					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Tuple     							= floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          snake_case			:  Optional[Any]     							= image.cpu().permute(0       ,		2       ,		3       ,		1					)[0]
          snake_case			:  Any     							= Image.fromarray(np.uinta(snake_case__					)					).convert("RGB"					)
          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  int     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  Optional[int]     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  Any     							= {
              "image": image,
              "source_prompt": "a cat and a frog",
              "target_prompt": "a dog and a newt",
              "generator": generator,
              "num_inference_steps": 2,
              "num_maps_per_mask": 2,
              "mask_encode_strength": 1.0,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }

          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Tuple=0					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)
          snake_case			:  Tuple     							= image.cpu().permute(0       ,		2       ,		3       ,		1					)[0]
          snake_case			:  List[Any]     							= Image.fromarray(np.uinta(snake_case__					)					).convert("RGB"					)
          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  str     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  int     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  List[str]     							= {
              "image": image,
              "prompt": "a cat and a frog",
              "generator": generator,
              "num_inference_steps": 2,
              "inpaint_strength": 1.0,
              "guidance_scale": 6.0,
              "decode_latents": True,
              "output_type": "numpy",
          }
          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      str:


          '''simple docstring'''


          if not hasattr(self.pipeline_class       ,		"_optional_components"					):
                 return

          snake_case			:  int     							= self.get_dummy_components()
          snake_case			:  Optional[int]     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          # set all optional components to None and update pipeline config accordingly
          for optional_component in pipe._optional_components:
                 setattr(snake_case__       ,		snake_case__       ,		snake_case__					)
          pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}					)

          snake_case			:  Dict     							= self.get_dummy_inputs(snake_case__					)
          snake_case			:  int     							= pipe(**snake_case__					)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(snake_case__					)
                 snake_case			:  Any     							= self.pipeline_class.from_pretrained(snake_case__					)
                 pipe_loaded.to(snake_case__					)
                 pipe_loaded.set_progress_bar_config(disable=snake_case__					)

          for optional_component in pipe._optional_components:
                 self.assertTrue(
                     getattr(snake_case__       ,		snake_case__					) is None       ,		f"""`{optional_component}` did not stay set to None after loading."""       ,		)

          snake_case			:  Optional[int]     							= self.get_dummy_inputs(snake_case__					)
          snake_case			:  int     							= pipe_loaded(**snake_case__					)[0]

          snake_case			:  int     							= np.abs(output - output_loaded					).max()
          self.assertLess(snake_case__       ,		1e-4					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      int:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= "cpu"

          snake_case			:  int     							= self.get_dummy_components()
          snake_case			:  Any     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Any     							= self.get_dummy_mask_inputs(snake_case__					)
          snake_case			:  Union[str, Any]     							= pipe.generate_mask(**snake_case__					)
          snake_case			:  List[Any]     							= mask[0, -3:, -3:]

          self.assertEqual(mask.shape       ,		(1, 16, 16)					)
          snake_case			:  int     							= np.array([0] * 9					)
          snake_case			:  str     							= np.abs(mask_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)
          self.assertEqual(mask[0, -3, -4]       ,		0					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      str:


          '''simple docstring'''


          snake_case			:  Any     							= "cpu"

          snake_case			:  List[Any]     							= self.get_dummy_components()
          snake_case			:  Optional[int]     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  List[str]     							= self.get_dummy_inversion_inputs(snake_case__					)
          snake_case			:  List[str]     							= pipe.invert(**snake_case__					).images
          snake_case			:  Optional[Any]     							= image[0, -1, -3:, -3:]

          self.assertEqual(image.shape       ,		(2, 32, 32, 3)					)
          snake_case			:  int     							= np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]       ,		)
          snake_case			:  Optional[Any]     							= np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Any:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Any:


          '''simple docstring'''


          snake_case			:  Any     							= "cpu"

          snake_case			:  int     							= self.get_dummy_components()

          snake_case			:  Tuple     							= {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
          snake_case			:  int     							= DPMSolverMultistepScheduler(**snake_case__					)
          snake_case			:  Optional[Any]     							= DPMSolverMultistepInverseScheduler(**snake_case__					)

          snake_case			:  Any     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Tuple     							= self.get_dummy_inversion_inputs(snake_case__					)
          snake_case			:  Optional[int]     							= pipe.invert(**snake_case__					).images
          snake_case			:  Optional[Any]     							= image[0, -1, -3:, -3:]

          self.assertEqual(image.shape       ,		(2, 32, 32, 3)					)
          snake_case			:  int     							= np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]       ,		)
          snake_case			:  Any     							= np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)






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



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      int:


          '''simple docstring'''


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



   @classmethod
   def 							_SCREAMING_SNAKE_CASE  (cls						:   Optional[int]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Tuple     							= load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"					)

          snake_case			:  Tuple     							= raw_image.convert("RGB"					).resize((7_68, 7_68)					)

          snake_case			:  Tuple     							= raw_image



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


          '''simple docstring'''


          snake_case			:  Optional[int]     							= torch.manual_seed(0					)

          snake_case			:  List[str]     							= StableDiffusionDiffEditPipeline.from_pretrained(
              "stabilityai/stable-diffusion-2-1"       ,		safety_checker=snake_case__       ,		torch_dtype=torch.floataa					)
          snake_case			:  int     							= DDIMScheduler.from_config(pipe.scheduler.config					)
          snake_case			:  Dict     							= DDIMInverseScheduler.from_config(pipe.scheduler.config					)
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Union[str, Any]     							= "a bowl of fruit"
          snake_case			:  int     							= "a bowl of pears"

          snake_case			:  List[Any]     							= pipe.generate_mask(
              image=self.raw_image       ,		source_prompt=snake_case__       ,		target_prompt=snake_case__       ,		generator=snake_case__       ,		)

          snake_case			:  Optional[int]     							= pipe.invert(
              prompt=snake_case__       ,		image=self.raw_image       ,		inpaint_strength=0.7       ,		generator=snake_case__					).latents

          snake_case			:  List[str]     							= pipe(
              prompt=snake_case__       ,		mask_image=snake_case__       ,		image_latents=snake_case__       ,		generator=snake_case__       ,		negative_prompt=snake_case__       ,		inpaint_strength=0.7       ,		output_type="numpy"       ,		).images[0]

          snake_case			:  Dict     							= (
              np.array(
                  load_image(
                      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                      "/diffedit/pears.png"					).resize((7_68, 7_68)					)					)
              / 2_55
          )
          assert np.abs((expected_image - image).max()					) < 5e-1



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


          '''simple docstring'''


          snake_case			:  int     							= torch.manual_seed(0					)

          snake_case			:  Tuple     							= StableDiffusionDiffEditPipeline.from_pretrained(
              "stabilityai/stable-diffusion-2-1"       ,		safety_checker=snake_case__       ,		torch_dtype=torch.floataa					)
          snake_case			:  Union[str, Any]     							= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
          snake_case			:  Optional[int]     							= DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config					)
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  str     							= "a bowl of fruit"
          snake_case			:  Optional[int]     							= "a bowl of pears"

          snake_case			:  Optional[Any]     							= pipe.generate_mask(
              image=self.raw_image       ,		source_prompt=snake_case__       ,		target_prompt=snake_case__       ,		generator=snake_case__       ,		)

          snake_case			:  List[str]     							= pipe.invert(
              prompt=snake_case__       ,		image=self.raw_image       ,		inpaint_strength=0.7       ,		generator=snake_case__       ,		num_inference_steps=25       ,		).latents

          snake_case			:  Dict     							= pipe(
              prompt=snake_case__       ,		mask_image=snake_case__       ,		image_latents=snake_case__       ,		generator=snake_case__       ,		negative_prompt=snake_case__       ,		inpaint_strength=0.7       ,		num_inference_steps=25       ,		output_type="numpy"       ,		).images[0]

          snake_case			:  int     							= (
              np.array(
                  load_image(
                      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                      "/diffedit/pears.png"					).resize((7_68, 7_68)					)					)
              / 2_55
          )
          assert np.abs((expected_image - image).max()					) < 5e-1





