




'''simple docstring'''

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 __magic_name__(						__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Optional[Any]    =  StableDiffusionModelEditingPipeline
						UpperCAmelCase_		:					Tuple    =  TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase_		:					Dict    =  TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase_		:					Tuple    =  TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase_		:					Union[str, Any]    =  TEXT_TO_IMAGE_IMAGE_PARAMS






						def       __lowerCAmelCase(   self						:						Optional[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					,   )
									snake_case__          =						DDIMScheduler()
									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					,   )
									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					,   )
									snake_case__          =						CLIPTextModel(__UpperCamelCase       )
									snake_case__          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

									snake_case__          =						{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						int=0       ):



									'''simple docstring'''






									snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """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       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionModelEditingPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						sd_pipe(**__UpperCamelCase       ).images
									snake_case__          =						image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									snake_case__          =						np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]       )

									assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						StableDiffusionModelEditingPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						"""french fries"""
									snake_case__          =						sd_pipe(**__UpperCamelCase					,   negative_prompt=__UpperCamelCase       )
									snake_case__          =						output.images
									snake_case__          =						image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									snake_case__          =						np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]       )

									assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85					,   beta_end=0.0_12					,   beta_schedule="""scaled_linear"""       )
									snake_case__          =						StableDiffusionModelEditingPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						sd_pipe(**__UpperCamelCase       ).images
									snake_case__          =						image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									snake_case__          =						np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]       )

									assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						PNDMScheduler()
									snake_case__          =						StableDiffusionModelEditingPipeline(**__UpperCamelCase       )
									snake_case__          =						sd_pipe.to(__UpperCamelCase       )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(__UpperCamelCase       ):
												snake_case__          =						sd_pipe(**__UpperCamelCase       ).images






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






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







						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






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







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






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=0       ):



									'''simple docstring'''






									snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						"""CompVis/stable-diffusion-v1-4"""
									snake_case__          =						StableDiffusionModelEditingPipeline.from_pretrained(__UpperCamelCase					,   safety_checker=__UpperCamelCase       )
									pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )
									pipe.enable_attention_slicing()

									snake_case__          =						self.get_inputs()
									snake_case__          =						pipe(**__UpperCamelCase       ).images
									snake_case__          =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									snake_case__          =						np.array(
									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]       )

									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"""       )

									snake_case__          =						pipe(**__UpperCamelCase       ).images
									snake_case__          =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									assert np.abs(expected_slice - image_slice       ).max() > 1E-1







						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									snake_case__          =						"""CompVis/stable-diffusion-v1-4"""
									snake_case__          =						DDIMScheduler.from_pretrained(__UpperCamelCase					,   subfolder="""scheduler"""       )
									snake_case__          =						StableDiffusionModelEditingPipeline.from_pretrained(
									    __UpperCamelCase					,   scheduler=__UpperCamelCase					,   safety_checker=__UpperCamelCase       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )
									pipe.enable_attention_slicing(1       )
									pipe.enable_sequential_cpu_offload()

									snake_case__          =						self.get_inputs()
									snake_case__          =						pipe(**__UpperCamelCase       )

									snake_case__          =						torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 1_0**9


'''simple docstring'''

def      __magic_name__							(			A				)      ->		tuple[int, int]:
	# base case
	if len(A				) == 1:
		return a[0], a[0]
	else:
		# split A into half.
		snake_case							=					a[: len(A				) // 2]
		snake_case							=					a[len(A				) // 2 :]

		# 2 sub problems, 1/2 of original size.
		snake_case			,			snake_case							=					max_difference(A				)
		snake_case			,			snake_case							=					max_difference(A				)

		# get min of first and max of second
		# linear time
		snake_case							=					min(A				)
		snake_case							=					max(A				)

		# 3 cases, either (small1, big1),
		# (min_first, max_second), (small2, big2)
		# constant comparisons
		if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
			return smalla, biga
		elif biga - smalla > max_second - min_first:
			return smalla, biga
		else:
			return min_first, max_second


if __name__ == "__main__":
						import doctest

						doctest.testmod()





