


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      __lowerCAmelCase  (			a			,	a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							StableDiffusionDiffEditPipeline
  _SCREAMING_SNAKE_CASE		     =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
  _SCREAMING_SNAKE_CASE		     =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
  _SCREAMING_SNAKE_CASE		     =							frozenset(
      []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _SCREAMING_SNAKE_CASE		     =							frozenset([]       )



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




     """simple docstring"""
     torch.manual_seed(0	)
     snake_case_            =       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     ,					attention_head_dim=(2, 4)     ,					use_linear_projection=_lowerCAmelCase     ,					)
     snake_case_            =       DDIMScheduler(
         beta_start=0.00_085     ,					beta_end=0.012     ,					beta_schedule="scaled_linear"     ,					clip_sample=_lowerCAmelCase     ,					set_alpha_to_one=_lowerCAmelCase     ,					)
     snake_case_            =       DDIMInverseScheduler(
         beta_start=0.00_085     ,					beta_end=0.012     ,					beta_schedule="scaled_linear"     ,					clip_sample=_lowerCAmelCase     ,					set_alpha_to_zero=_lowerCAmelCase     ,					)
     torch.manual_seed(0	)
     snake_case_            =       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     ,					sample_size=1_2_8     ,					)
     torch.manual_seed(0	)
     snake_case_            =       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     ,					hidden_act="gelu"     ,					projection_dim=5_1_2     ,					)
     snake_case_            =       CLIPTextModel(_lowerCAmelCase	)
     snake_case_            =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

     snake_case_            =       {
         "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        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int=0	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       floats_tensor((1, 1_6, 1_6)     ,					rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
     snake_case_            =       floats_tensor((1, 2, 4, 1_6, 1_6)     ,					rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
     if str(_lowerCAmelCase	).startswith("mps"	):
        snake_case_            =       torch.manual_seed(_lowerCAmelCase	)
     else:
        snake_case_            =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)
     snake_case_            =       {
         "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        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Dict=0	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       floats_tensor((1, 3, 3_2, 3_2)     ,					rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
     snake_case_            =       image.cpu().permute(0     ,					2     ,					3     ,					1	)[0]
     snake_case_            =       Image.fromarray(np.uinta(_lowerCAmelCase	)	).convert("RGB"	)
     if str(_lowerCAmelCase	).startswith("mps"	):
        snake_case_            =       torch.manual_seed(_lowerCAmelCase	)
     else:
        snake_case_            =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)
     snake_case_            =       {
         "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        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							Union[str, Any]=0	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       floats_tensor((1, 3, 3_2, 3_2)     ,					rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
     snake_case_            =       image.cpu().permute(0     ,					2     ,					3     ,					1	)[0]
     snake_case_            =       Image.fromarray(np.uinta(_lowerCAmelCase	)	).convert("RGB"	)
     if str(_lowerCAmelCase	).startswith("mps"	):
        snake_case_            =       torch.manual_seed(_lowerCAmelCase	)
     else:
        snake_case_            =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)
     snake_case_            =       {
         "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        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Any:




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

     snake_case_            =       self.get_dummy_components()
     snake_case_            =       self.pipeline_class(**_lowerCAmelCase	)
     pipe.to(_lowerCAmelCase	)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

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

     snake_case_            =       self.get_dummy_inputs(_lowerCAmelCase	)
     snake_case_            =       pipe(**_lowerCAmelCase	)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.pipeline_class.from_pretrained(_lowerCAmelCase	)
        pipe_loaded.to(_lowerCAmelCase	)
        pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase	)

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

     snake_case_            =       self.get_dummy_inputs(_lowerCAmelCase	)
     snake_case_            =       pipe_loaded(**_lowerCAmelCase	)[0]

     snake_case_            =       np.abs(output - output_loaded	).max()
     self.assertLess(_lowerCAmelCase     ,					1e-4	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			int:




     """simple docstring"""
     snake_case_            =       "cpu"

     snake_case_            =       self.get_dummy_components()
     snake_case_            =       self.pipeline_class(**_lowerCAmelCase	)
     pipe.to(_lowerCAmelCase	)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

     snake_case_            =       self.get_dummy_mask_inputs(_lowerCAmelCase	)
     snake_case_            =       pipe.generate_mask(**_lowerCAmelCase	)
     snake_case_            =       mask[0, -3:, -3:]

     self.assertEqual(mask.shape     ,					(1, 1_6, 1_6)	)
     snake_case_            =       np.array([0] * 9	)
     snake_case_            =       np.abs(mask_slice.flatten() - expected_slice	).max()
     self.assertLessEqual(_lowerCAmelCase     ,					1e-3	)
     self.assertEqual(mask[0, -3, -4]     ,					0	)



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       "cpu"

     snake_case_            =       self.get_dummy_components()
     snake_case_            =       self.pipeline_class(**_lowerCAmelCase	)
     pipe.to(_lowerCAmelCase	)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

     snake_case_            =       self.get_dummy_inversion_inputs(_lowerCAmelCase	)
     snake_case_            =       pipe.invert(**_lowerCAmelCase	).images
     snake_case_            =       image[0, -1, -3:, -3:]

     self.assertEqual(image.shape     ,					(2, 3_2, 3_2, 3)	)
     snake_case_            =       np.array(
         [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]     ,					)
     snake_case_            =       np.abs(image_slice.flatten() - expected_slice	).max()
     self.assertLessEqual(_lowerCAmelCase     ,					1e-3	)



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




     """simple docstring"""
     super().test_inference_batch_single_identical(expected_max_diff=5e-3	)



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




     """simple docstring"""
     snake_case_            =       "cpu"

     snake_case_            =       self.get_dummy_components()

     snake_case_            =       {"beta_start": 0.00_085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
     snake_case_            =       DPMSolverMultistepScheduler(**_lowerCAmelCase	)
     snake_case_            =       DPMSolverMultistepInverseScheduler(**_lowerCAmelCase	)

     snake_case_            =       self.pipeline_class(**_lowerCAmelCase	)
     pipe.to(_lowerCAmelCase	)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

     snake_case_            =       self.get_dummy_inversion_inputs(_lowerCAmelCase	)
     snake_case_            =       pipe.invert(**_lowerCAmelCase	).images
     snake_case_            =       image[0, -1, -3:, -3:]

     self.assertEqual(image.shape     ,					(2, 3_2, 3_2, 3)	)
     snake_case_            =       np.array(
         [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]     ,					)
     snake_case_            =       np.abs(image_slice.flatten() - expected_slice	).max()
     self.assertLessEqual(_lowerCAmelCase     ,					1e-3	)






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






  """simple docstring"""





  def        lowerCAmelCase__	(  self							:							List[str]	) ->			Any:




     """simple docstring"""
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Union[str, Any]	) ->			int:




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

     snake_case_            =       raw_image.convert("RGB"	).resize((7_6_8, 7_6_8)	)

     snake_case_            =       raw_image



  def        lowerCAmelCase__	(  self							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       torch.manual_seed(0	)

     snake_case_            =       StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"     ,					safety_checker=_lowerCAmelCase     ,					torch_dtype=torch.floataa	)
     snake_case_            =       DDIMScheduler.from_config(pipe.scheduler.config	)
     snake_case_            =       DDIMInverseScheduler.from_config(pipe.scheduler.config	)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

     snake_case_            =       "a bowl of fruit"
     snake_case_            =       "a bowl of pears"

     snake_case_            =       pipe.generate_mask(
         image=self.raw_image     ,					source_prompt=_lowerCAmelCase     ,					target_prompt=_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					)

     snake_case_            =       pipe.invert(
         prompt=_lowerCAmelCase     ,					image=self.raw_image     ,					inpaint_strength=0.7     ,					generator=_lowerCAmelCase	).latents

     snake_case_            =       pipe(
         prompt=_lowerCAmelCase     ,					mask_image=_lowerCAmelCase     ,					image_latents=_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					negative_prompt=_lowerCAmelCase     ,					inpaint_strength=0.7     ,					output_type="numpy"     ,					).images[0]

     snake_case_            =       (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"	).resize((7_6_8, 7_6_8)	)	)
         / 2_5_5
     )
     assert np.abs((expected_image - image).max()	) < 5e-1



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




     """simple docstring"""
     snake_case_            =       torch.manual_seed(0	)

     snake_case_            =       StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"     ,					safety_checker=_lowerCAmelCase     ,					torch_dtype=torch.floataa	)
     snake_case_            =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
     snake_case_            =       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config	)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

     snake_case_            =       "a bowl of fruit"
     snake_case_            =       "a bowl of pears"

     snake_case_            =       pipe.generate_mask(
         image=self.raw_image     ,					source_prompt=_lowerCAmelCase     ,					target_prompt=_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					)

     snake_case_            =       pipe.invert(
         prompt=_lowerCAmelCase     ,					image=self.raw_image     ,					inpaint_strength=0.7     ,					generator=_lowerCAmelCase     ,					num_inference_steps=2_5     ,					).latents

     snake_case_            =       pipe(
         prompt=_lowerCAmelCase     ,					mask_image=_lowerCAmelCase     ,					image_latents=_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					negative_prompt=_lowerCAmelCase     ,					inpaint_strength=0.7     ,					num_inference_steps=2_5     ,					output_type="numpy"     ,					).images[0]

     snake_case_            =       (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"	).resize((7_6_8, 7_6_8)	)	)
         / 2_5_5
     )
     assert np.abs((expected_image - image).max()	) < 5e-1








from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




