





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 			__lowerCAmelCase				(					snake_case_      ,						snake_case_      ,						snake_case_      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							StableDiffusionModelEditingPipeline
	_A       	=							TEXT_TO_IMAGE_PARAMS
	_A       	=							TEXT_TO_IMAGE_BATCH_PARAMS
	_A       	=							TEXT_TO_IMAGE_IMAGE_PARAMS
	_A       	=							TEXT_TO_IMAGE_IMAGE_PARAMS

	def       snake_case__(	self:	Any ):
			torch.manual_seed(0 )
			lowercase__				:			List[str]            =		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,				)
			lowercase__				:			List[Any]            =		DDIMScheduler()
			torch.manual_seed(0 )
			lowercase__				:			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 )
			lowercase__				:			Tuple            =		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,				)
			lowercase__				:			List[str]            =		CLIPTextModel(lowerCamelCase_ )
			lowercase__				:			int            =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

			lowercase__				:			Any            =		{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'safety_checker': None,
			    'feature_extractor': None,
			}
			return components

	def       snake_case__(	self:	Any,				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Optional[Any]=0 ):
			lowercase__				:			int            =		torch.manual_seed(lowerCamelCase_ )
			lowercase__				:			str            =		{
			    '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       snake_case__(	self:	List[Any] ):
			lowercase__				:			Optional[int]            =		'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowercase__				:			List[str]            =		self.get_dummy_components()
			lowercase__				:			Optional[Any]            =		StableDiffusionModelEditingPipeline(**lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		sd_pipe.to(lowerCamelCase_ )
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

			lowercase__				:			Union[str, Any]            =		self.get_dummy_inputs(lowerCamelCase_ )
			lowercase__				:			Dict            =		sd_pipe(**lowerCamelCase_ ).images
			lowercase__				:			Optional[Any]            =		image[0, -3:, -3:, -1]
			assert image.shape == (1, 64, 64, 3)

			lowercase__				:			Optional[int]            =		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       snake_case__(	self:	Dict ):
			lowercase__				:			Dict            =		'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowercase__				:			str            =		self.get_dummy_components()
			lowercase__				:			Optional[int]            =		StableDiffusionModelEditingPipeline(**lowerCamelCase_ )
			lowercase__				:			Any            =		sd_pipe.to(lowerCamelCase_ )
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

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

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

			lowercase__				:			Optional[int]            =		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       snake_case__(	self:	Dict ):
			lowercase__				:			Any            =		'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowercase__				:			Dict            =		self.get_dummy_components()
			lowercase__				:			Any            =		EulerAncestralDiscreteScheduler(
			    beta_start=0.0_0_0_8_5,				beta_end=0.0_1_2,				beta_schedule='scaled_linear' )
			lowercase__				:			Any            =		StableDiffusionModelEditingPipeline(**lowerCamelCase_ )
			lowercase__				:			Any            =		sd_pipe.to(lowerCamelCase_ )
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

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

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

			lowercase__				:			Any            =		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       snake_case__(	self:	Tuple ):
			lowercase__				:			str            =		'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowercase__				:			Union[str, Any]            =		self.get_dummy_components()
			lowercase__				:			List[str]            =		PNDMScheduler()
			lowercase__				:			int            =		StableDiffusionModelEditingPipeline(**lowerCamelCase_ )
			lowercase__				:			Optional[int]            =		sd_pipe.to(lowerCamelCase_ )
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		self.get_dummy_inputs(lowerCamelCase_ )
			# the pipeline does not expect pndm so test if it raises error.
			with self.assertRaises(lowerCamelCase_ ):
					lowercase__				:			str            =		sd_pipe(**lowerCamelCase_ ).images

	def       snake_case__(	self:	Tuple ):
			super().test_inference_batch_single_identical(expected_max_diff=5E-3 )





	def       snake_case__(	self:	List[Any] ):
			super().test_attention_slicing_forward_pass(expected_max_diff=5E-3 )


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


	'''simple docstring'''





	def       snake_case__(	self:	Optional[Any] ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	def       snake_case__(	self:	Tuple,				lowerCamelCase_:	Union[str, Any]=0 ):
			lowercase__				:			Union[str, Any]            =		torch.manual_seed(lowerCamelCase_ )
			lowercase__				:			Tuple            =		{
			    'prompt': 'A field of roses',
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs

	def       snake_case__(	self:	str ):
			lowercase__				:			int            =		'CompVis/stable-diffusion-v1-4'
			lowercase__				:			List[str]            =		StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_,				safety_checker=lowerCamelCase_ )
			pipe.to(lowerCamelCase_ )
			pipe.set_progress_bar_config(disable=lowerCamelCase_ )
			pipe.enable_attention_slicing()

			lowercase__				:			Dict            =		self.get_inputs()
			lowercase__				:			Union[str, Any]            =		pipe(**lowerCamelCase_ ).images
			lowercase__				:			Optional[int]            =		image[0, -3:, -3:, -1].flatten()

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

			lowercase__				:			Optional[Any]            =		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' )

			lowercase__				:			List[Any]            =		pipe(**lowerCamelCase_ ).images
			lowercase__				:			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       snake_case__(	self:	Optional[Any] ):
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowercase__				:			Optional[Any]            =		'CompVis/stable-diffusion-v1-4'
			lowercase__				:			Any            =		DDIMScheduler.from_pretrained(lowerCamelCase_,				subfolder='scheduler' )
			lowercase__				:			Union[str, Any]            =		StableDiffusionModelEditingPipeline.from_pretrained(
			    lowerCamelCase_,				scheduler=lowerCamelCase_,				safety_checker=lowerCamelCase_ )
			lowercase__				:			Tuple            =		pipe.to(lowerCamelCase_ )
			pipe.set_progress_bar_config(disable=lowerCamelCase_ )
			pipe.enable_attention_slicing(1 )
			pipe.enable_sequential_cpu_offload()

			lowercase__				:			Optional[Any]            =		self.get_inputs()
			lowercase__				:			Optional[Any]            =		pipe(**lowerCamelCase_ )

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









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


