
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase_ 				=			TypeVar("""T""")





class     __UpperCamelCase (							Generic[T]       ):






     """simple docstring"""







     def __init__(				self							:							List[Any]     ,						_A							:							list[T]     ,						_A							:							Callable[[T, T], T] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any | T					=		None

       __SCREAMING_SNAKE_CASE      :	int					=		len(_A )
       __SCREAMING_SNAKE_CASE      :	list[T]					=		[any_type for _ in range(self.N )] + arr
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		fnc
       self.build()



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       for p in range(self.N - 1     ,						0     ,						-1 ):
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.fn(self.st[p * 2]     ,						self.st[p * 2 + 1] )



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							int     ,						_A							:							T ):







       """simple docstring"""




       p += self.N
       __SCREAMING_SNAKE_CASE      :	Tuple					=		v
       while p > 1:
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		p // 2
         __SCREAMING_SNAKE_CASE      :	Any					=		self.fn(self.st[p * 2]     ,						self.st[p * 2 + 1] )




     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							int     ,						_A							:							int ):  # noqa: E741







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		l + self.N, r + self.N

       __SCREAMING_SNAKE_CASE      :	T | None					=		None
       while l <= r:
         if l % 2 == 1:
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.st[l] if res is None else self.fn(_A     ,						self.st[l] )
         if r % 2 == 0:
           __SCREAMING_SNAKE_CASE      :	int					=		self.st[r] if res is None else self.fn(_A     ,						self.st[r] )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Any					=		(l + 1) // 2, (r - 1) // 2
       return res


if __name__ == "__main__":
   from functools import reduce

   lowercase_ 				=			[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

   lowercase_ 				=			{
       0: 7,
       1: 2,
       2: 6,
       3: -14,
       4: 5,
       5: 4,
       6: 7,
       7: -10,
       8: 9,
       9: 10,
       10: 12,
       11: 1,
   }

   lowercase_ 				=			SegmentTree(test_array, min)
   lowercase_ 				=			SegmentTree(test_array, max)
   lowercase_ 				=			SegmentTree(test_array, lambda a, b: a + b)


   def 							a__					(							):





     """simple docstring"""
     for i in range(len(snake_case							)							):
       for j in range(snake_case       ,			len(snake_case							)							):
         __SCREAMING_SNAKE_CASE      :	Any					=		reduce(snake_case       ,			test_array[i : j + 1]							)
         __SCREAMING_SNAKE_CASE      :	Tuple					=		reduce(snake_case       ,			test_array[i : j + 1]							)
         __SCREAMING_SNAKE_CASE      :	str					=		reduce(lambda snake_case       ,			snake_case							: a + b       ,			test_array[i : j + 1]							)
         assert min_range == min_segment_tree.query(snake_case       ,			snake_case							)
         assert max_range == max_segment_tree.query(snake_case       ,			snake_case							)
         assert sum_range == sum_segment_tree.query(snake_case       ,			snake_case							)

   test_all_segments()

   for index, value in test_updates.items():
      lowercase_ 				=			value
      min_segment_tree.update(index, value)
      max_segment_tree.update(index, value)
      sum_segment_tree.update(index, value)
      test_all_segments()



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




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=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=lowerCAmelCase				,  )
        _lowercase      			=DDIMScheduler(
            beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='scaled_linear'				,  clip_sample=lowerCAmelCase				,  set_alpha_to_one=lowerCAmelCase				,  )
        _lowercase      			=DDIMInverseScheduler(
            beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='scaled_linear'				,  clip_sample=lowerCAmelCase				,  set_alpha_to_zero=lowerCAmelCase				,  )
        torch.manual_seed(0   )
        _lowercase      			=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=128				,  )
        torch.manual_seed(0   )
        _lowercase      			=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=1_000				,  hidden_act='gelu'				,  projection_dim=512				,  )
        _lowercase      			=CLIPTextModel(lowerCAmelCase   )
        _lowercase      			=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

        _lowercase      			={
            '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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 16, 16)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=floats_tensor((1, 2, 4, 16, 16)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            '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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   )[0]
        _lowercase      			=Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('RGB'   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            '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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=floats_tensor((1, 3, 32, 32)				,  rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        _lowercase      			=image.cpu().permute(0				,  2				,  3				,  1   )[0]
        _lowercase      			=Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('RGB'   )
        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            '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     A__	(							self   )    ->					Any:
        '''simple docstring'''






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

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=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}   )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe(**lowerCAmelCase   )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(lowerCAmelCase   )
              _lowercase      			=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.'''				,  )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe_loaded(**lowerCAmelCase   )[0]

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




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

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




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

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




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()

        _lowercase      			={'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
        _lowercase      			=DPMSolverMultistepScheduler(**lowerCAmelCase   )
        _lowercase      			=DPMSolverMultistepInverseScheduler(**lowerCAmelCase   )

        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

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





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




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






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




  @classmethod
  def     A__	(							cls   )    ->					int:
        '''simple docstring'''






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

        _lowercase      			=raw_image.convert('RGB'   ).resize((768, 768)   )

        _lowercase      			=raw_image




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(0   )

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

        _lowercase      			='a bowl of fruit'
        _lowercase      			='a bowl of pears'

        _lowercase      			=pipe.generate_mask(
            image=self.raw_image				,  source_prompt=lowerCAmelCase				,  target_prompt=lowerCAmelCase				,  generator=lowerCAmelCase				,  )

        _lowercase      			=pipe.invert(
            prompt=lowerCAmelCase				,  image=self.raw_image				,  inpaint_strength=0.7				,  generator=lowerCAmelCase   ).latents

        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  mask_image=lowerCAmelCase				,  image_latents=lowerCAmelCase				,  generator=lowerCAmelCase				,  negative_prompt=lowerCAmelCase				,  inpaint_strength=0.7				,  output_type='numpy'				,  ).images[0]

        _lowercase      			=(
            np.array(
                load_image(
                    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                    '/diffedit/pears.png'   ).resize((768, 768)   )   )
            / 255
        )
        assert np.abs((expected_image - image).max()   ) < 5e-1




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(0   )

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

        _lowercase      			='a bowl of fruit'
        _lowercase      			='a bowl of pears'

        _lowercase      			=pipe.generate_mask(
            image=self.raw_image				,  source_prompt=lowerCAmelCase				,  target_prompt=lowerCAmelCase				,  generator=lowerCAmelCase				,  )

        _lowercase      			=pipe.invert(
            prompt=lowerCAmelCase				,  image=self.raw_image				,  inpaint_strength=0.7				,  generator=lowerCAmelCase				,  num_inference_steps=25				,  ).latents

        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  mask_image=lowerCAmelCase				,  image_latents=lowerCAmelCase				,  generator=lowerCAmelCase				,  negative_prompt=lowerCAmelCase				,  inpaint_strength=0.7				,  num_inference_steps=25				,  output_type='numpy'				,  ).images[0]

        _lowercase      			=(
            np.array(
                load_image(
                    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                    '/diffedit/pears.png'   ).resize((768, 768)   )   )
            / 255
        )
        assert np.abs((expected_image - image).max()   ) < 5e-1


