

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def 					_lowercase     (				a_				:					Sequence[float]					,a_				:					int					,a_				:					int				)				->	tuple[int | None, int | None, float]:




					'''simple docstring'''







					if not arr:
										return None, None, 0
					if low == high:
										return low, high, arr[low]

					__magic_name__							=    (low + high) // 2
					__magic_name__,	__magic_name__,	__magic_name__							=    max_subarray(a_					,a_					,a_				)
					__magic_name__,	__magic_name__,	__magic_name__							=    max_subarray(a_					,mid + 1					,a_				)
					__magic_name__,	__magic_name__,	__magic_name__							=    max_cross_sum(a_					,a_					,a_					,a_				)
					if left_sum >= right_sum and left_sum >= cross_sum:
										return left_low, left_high, left_sum
					elif right_sum >= left_sum and right_sum >= cross_sum:
										return right_low, right_high, right_sum
					return cross_left, cross_right, cross_sum




def 					_lowercase     (				a_				:					Sequence[float]					,a_				:					int					,a_				:					int					,a_				:					int				)				->	tuple[int, int, float]:




					'''simple docstring'''







					__magic_name__,	__magic_name__							=    float('-inf'				), -1
					__magic_name__,	__magic_name__							=    float('-inf'				), -1

					__magic_name__							=    0
					for i in range(a_					,low - 1					,-1				):
										summ += arr[i]
										if summ > left_sum:
															__magic_name__							=    summ
															__magic_name__							=    i

					__magic_name__							=    0
					for i in range(mid + 1					,high + 1				):
										summ += arr[i]
										if summ > right_sum:
															__magic_name__							=    summ
															__magic_name__							=    i

					return max_left, max_right, (left_sum + right_sum)




def 					_lowercase     (				a_				:					int				)				->	float:




					'''simple docstring'''







					__magic_name__							=    [randint(1					,a_				) for _ in range(a_				)]
					__magic_name__							=    time.time()
					max_subarray(a_					,0					,input_size - 1				)
					__magic_name__							=    time.time()
					return end - start




def 					_lowercase     (				)				->	None:




					'''simple docstring'''







					__magic_name__							=    [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
					__magic_name__							=    [time_max_subarray(a_				) for input_size in input_sizes]
					print('No of Inputs\t\tTime Taken'				)
					for input_size, runtime in zip(a_					,a_				):
										print(a_					,'\t\t'					,a_				)
					plt.plot(a_					,a_				)
					plt.xlabel('Number of Inputs'				)
					plt.ylabel('Time taken in seconds'				)
					plt.show()


if __name__ == "__main__":
						from doctest import testmod

						testmod()







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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    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=__UpperCamelCase  , )
						__magic_name__							=    DDIMScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_one=__UpperCamelCase  , )
						__magic_name__							=    DDIMInverseScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_zero=__UpperCamelCase  , )
						torch.manual_seed(0      )
						__magic_name__							=    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      )
						__magic_name__							=    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  , )
						__magic_name__							=    CLIPTextModel(__UpperCamelCase      )
						__magic_name__							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						__magic_name__							=    {
						    '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: Optional[int]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[Any]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    floats_tensor((1, 2, 4, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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: str  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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: List[Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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: Union[str, Any]      ):

						'''simple docstring'''





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

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    pipe(**__UpperCamelCase      )[0]

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

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

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    pipe_loaded(**__UpperCamelCase      )[0]

						__magic_name__							=    np.abs(output - output_loaded      ).max()
						self.assertLess(__UpperCamelCase  , 1E-4      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()

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

						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

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




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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]      ):

						'''simple docstring'''





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

						__magic_name__							=    raw_image.convert('RGB'      ).resize((7_68, 7_68)      )

						__magic_name__							=    raw_image







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

						__magic_name__							=    'a bowl of fruit'
						__magic_name__							=    'a bowl of pears'

						__magic_name__							=    pipe.generate_mask(
						    image=self.raw_image  , source_prompt=__UpperCamelCase  , target_prompt=__UpperCamelCase  , generator=__UpperCamelCase  , )

						__magic_name__							=    pipe.invert(
						    prompt=__UpperCamelCase  , image=self.raw_image  , inpaint_strength=0.7  , generator=__UpperCamelCase      ).latents

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

						__magic_name__							=    (
						    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: int      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

						__magic_name__							=    'a bowl of fruit'
						__magic_name__							=    'a bowl of pears'

						__magic_name__							=    pipe.generate_mask(
						    image=self.raw_image  , source_prompt=__UpperCamelCase  , target_prompt=__UpperCamelCase  , generator=__UpperCamelCase  , )

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

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

						__magic_name__							=    (
						    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




