






'''simple docstring'''







from manim import *




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Union[str, Any]												=	Rectangle(height=0.5  ,		width=0.5						)
             UpperCAmelCase__	:   Tuple												=	Rectangle(height=0.46  ,		width=0.46						).set_stroke(width=0						)
             UpperCAmelCase__	:   Tuple												=	Rectangle(height=0.25  ,		width=0.25						)

             UpperCAmelCase__	:   str												=	[mem.copy() for i in range(6						)]
             UpperCAmelCase__	:   List[str]												=	[mem.copy() for i in range(6						)]
             UpperCAmelCase__	:   Tuple												=	VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   Dict												=	VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   Dict												=	VGroup(_UpperCAmelCase  ,		_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   Dict												=	Text('''CPU'''  ,		font_size=24						)
             UpperCAmelCase__	:   Any												=	Group(_UpperCAmelCase  ,		_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0.5  ,		aligned_edge=_UpperCAmelCase						)
             cpu.move_to([-2.5, -0.5, 0]						)
             self.add(_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	[mem.copy() for i in range(4						)]
             UpperCAmelCase__	:   List[Any]												=	VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   str												=	Text('''GPU'''  ,		font_size=24						)
             UpperCAmelCase__	:   Optional[int]												=	Group(_UpperCAmelCase  ,		_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0.5  ,		aligned_edge=_UpperCAmelCase						)
             gpu.move_to([-1, -1, 0]						)
             self.add(_UpperCAmelCase						)

             UpperCAmelCase__	:   Dict												=	[mem.copy() for i in range(6						)]
             UpperCAmelCase__	:   Optional[Any]												=	VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)

             UpperCAmelCase__	:   Union[str, Any]												=	Text('''Model'''  ,		font_size=24						)
             UpperCAmelCase__	:   Union[str, Any]												=	Group(_UpperCAmelCase  ,		_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0.5  ,		aligned_edge=_UpperCAmelCase						)
             model.move_to([3, -1.0, 0]						)
             self.add(_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	[]
             UpperCAmelCase__	:   Any												=	[]

             for i, rect in enumerate(_UpperCAmelCase						):
                    rect.set_stroke(_UpperCAmelCase						)

                    UpperCAmelCase__	:   str												=	Rectangle(height=0.46 / 4  ,		width=0.46 / 3						).set_stroke(width=0.0						).set_fill(_UpperCAmelCase  ,		opacity=0.7						)

                    if i == 0:
                           cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						)  ,		buff=0.02  ,		direction=_UpperCAmelCase						)
                           cpu_target.set_x(cpu_target.get_x() + 0.1						)
                    elif i == 3:
                           cpu_target.next_to(model_cpu_arr[0]  ,		direction=_UpperCAmelCase  ,		buff=0.0						)
                    else:
                           cpu_target.next_to(model_cpu_arr[i - 1]  ,		direction=_UpperCAmelCase  ,		buff=0.0						)
                    self.add(_UpperCAmelCase						)
                    model_cpu_arr.append(_UpperCAmelCase						)

             self.add(*_UpperCAmelCase  ,		*_UpperCAmelCase						)

             UpperCAmelCase__	:   List[str]												=	[meta_mem.copy() for i in range(6						)]
             UpperCAmelCase__	:   Union[str, Any]												=	[meta_mem.copy() for i in range(6						)]
             UpperCAmelCase__	:   Union[str, Any]												=	VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   List[Any]												=	VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   int												=	VGroup(_UpperCAmelCase  ,		_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0						)
             UpperCAmelCase__	:   Optional[int]												=	Text('''Disk'''  ,		font_size=24						)
             UpperCAmelCase__	:   Dict												=	Group(_UpperCAmelCase  ,		_UpperCAmelCase						).arrange(_UpperCAmelCase  ,		buff=0.5  ,		aligned_edge=_UpperCAmelCase						)
             disk.move_to([-4.0, -1.25, 0]						)
             self.add(_UpperCAmelCase  ,		_UpperCAmelCase						)

             UpperCAmelCase__	:   Union[str, Any]												=	[]

             for i in range(6						):
                    UpperCAmelCase__	:   str												=	fill.copy().set_fill(_UpperCAmelCase  ,		opacity=0.8						)
                    target.move_to(disk_left_col_base[i]						).scale(0.5						)
                    cpu_disk_arr.append(_UpperCAmelCase						)

             self.add(*_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	Square(side_length=2.2						)
             key.move_to([-5, 2, 0]						)

             UpperCAmelCase__	:   int												=	MarkupText(
                 F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""  ,		font_size=18  ,		)

             key_text.move_to([-5, 2.4, 0]						)

             self.add(_UpperCAmelCase  ,		_UpperCAmelCase						)

             UpperCAmelCase__	:   Dict												=	MarkupText(
                 F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""  ,		font_size=18  ,		)

             blue_text.next_to(_UpperCAmelCase  ,		DOWN * 2.4  ,		aligned_edge=key_text.get_left()						)
             self.add(_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	MarkupText(F"""The offloaded weights are all sent to the CPU."""  ,		font_size=24						)
             step_a.move_to([2, 2, 0]						)

             self.play(Write(_UpperCAmelCase  ,		run_time=3						)						)

             for i in range(6						):
                    UpperCAmelCase__	:   Dict												=	cpu_disk_arr[i]
                    UpperCAmelCase__	:   List[str]												=	rect.copy().set_fill(_UpperCAmelCase  ,		opacity=0.8						).scale(2.0						)
                    cpa.generate_target()
                    cpa.target.move_to(model_base[i]						)

                    if i == 0:
                           rect.set_fill(_UpperCAmelCase  ,		opacity=0.8						)
                           rect.generate_target()
                           rect.target.move_to(cpu_left_col_base[0]						).scale(2.0						)

                           self.remove(
                               *_UpperCAmelCase  ,		*_UpperCAmelCase  ,		)

                    else:
                           rect.generate_target()
                           rect.target.move_to(cpu_left_col_base[i]						).scale(2.0						)
                    self.play(MoveToTarget(_UpperCAmelCase						)  ,		MoveToTarget(_UpperCAmelCase						)  ,		model_base[i].animate.set_stroke(_UpperCAmelCase						)						)
             self.play(FadeOut(_UpperCAmelCase						)						)

             UpperCAmelCase__	:   List[str]												=	MarkupText(
                 F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""  ,		font_size=24  ,		)
             step_a.move_to([2, 2, 0]						)

             self.play(Write(_UpperCAmelCase  ,		run_time=3						)						)

             UpperCAmelCase__	:   Optional[int]												=	[]
             UpperCAmelCase__	:   Optional[Any]												=	[]
             for i in range(6						):
                    UpperCAmelCase__	:   List[Any]												=	Arrow(start=_UpperCAmelCase  ,		end=_UpperCAmelCase  ,		color=_UpperCAmelCase  ,		buff=0.5						)
                    a.next_to(model_base[i].get_left()  ,		_UpperCAmelCase  ,		buff=0.2						)
                    arrows.append(_UpperCAmelCase						)
                    animations.append(Write(_UpperCAmelCase						)						)
             self.play(*_UpperCAmelCase						)
             self.wait()







'''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 					__UpperCAmelCase (		UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				unittest.TestCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					StableDiffusionModelEditingPipeline
      SCREAMING_SNAKE_CASE				:    str				       =					TEXT_TO_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					TEXT_TO_IMAGE_BATCH_PARAMS
      SCREAMING_SNAKE_CASE				:    Dict				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def 							lowerCamelCase     (					self						):
             torch.manual_seed(0						)
             UpperCAmelCase__	:   List[Any]												=	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  ,		)
             UpperCAmelCase__	:   List[str]												=	DDIMScheduler()
             torch.manual_seed(0						)
             UpperCAmelCase__	:   str												=	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						)
             UpperCAmelCase__	:   Optional[int]												=	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  ,		)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextModel(_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

             UpperCAmelCase__	:   List[str]												=	{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components




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

             UpperCAmelCase__	:   Any												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]
             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   int												=	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						):
             UpperCAmelCase__	:   int												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	'''french fries'''
             UpperCAmelCase__	:   Any												=	sd_pipe(**_UpperCAmelCase  ,		negative_prompt=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	output.images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   str												=	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						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   str												=	EulerAncestralDiscreteScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''						)
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[Any]												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   int												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1]

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

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

             UpperCAmelCase__	:   Tuple												=	self.get_dummy_inputs(_UpperCAmelCase						)
             # the pipeline does not expect pndm so test if it raises error.
             with self.assertRaises(_UpperCAmelCase						):
                    UpperCAmelCase__	:   List[Any]												=	sd_pipe(**_UpperCAmelCase						).images




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




      def 							lowerCamelCase     (					self						):
             super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




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

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




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




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

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

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

             UpperCAmelCase__	:   List[str]												=	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'''						)

             UpperCAmelCase__	:   Optional[int]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[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 							lowerCamelCase     (					self						):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase__	:   Dict												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   int												=	DDIMScheduler.from_pretrained(_UpperCAmelCase  ,		subfolder='''scheduler'''						)
             UpperCAmelCase__	:   int												=	StableDiffusionModelEditingPipeline.from_pretrained(
                 _UpperCAmelCase  ,		scheduler=_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             UpperCAmelCase__	:   Tuple												=	pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing(1						)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase__	:   Any												=	self.get_inputs()
             UpperCAmelCase__	:   Any												=	pipe(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	torch.cuda.max_memory_allocated()
             # make sure that less than 4.4 GB is allocated
             assert mem_bytes < 4.4 * 10**9