
"""simple docstring"""




import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_=2,	SCREAMING_SNAKE_CASE_=True,	SCREAMING_SNAKE_CASE_=False     ):
					SCREAMING_SNAKE_CASE           =      Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_,	split_batches=SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      torch.nn.Linear(2,	4     )
					SCREAMING_SNAKE_CASE           =      torch.optim.AdamW(model.parameters(),	lr=1.0     )
					SCREAMING_SNAKE_CASE           =      torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE_,	max_lr=0.01,	steps_per_epoch=2,	epochs=1     )
					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      accelerator.prepare(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					# Optimizer has stepped
					scheduler.step()
					if step_scheduler_with_optimizer or (num_processes == 1):
										assert (
										    scheduler.scheduler.last_epoch == num_processes
										), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
					else:
										assert (
										    scheduler.scheduler.last_epoch != num_processes
										), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_=2,	SCREAMING_SNAKE_CASE_=True,	SCREAMING_SNAKE_CASE_=False     ):
					SCREAMING_SNAKE_CASE           =      Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_,	split_batches=SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      torch.nn.Linear(2,	4     )
					SCREAMING_SNAKE_CASE           =      torch.optim.AdamW(model.parameters(),	lr=1.0     )
					SCREAMING_SNAKE_CASE           =      torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE_,	lr_lambda=lambda SCREAMING_SNAKE_CASE_     : 1 - n / 1_0     )
					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      accelerator.prepare(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					# Optimizer has stepped
					SCREAMING_SNAKE_CASE           =      False
					scheduler.step()
					SCREAMING_SNAKE_CASE           =      1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
					assert (
					    scheduler.get_last_lr()[0] == expected_lr
					), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

					# Optimizer has not stepped
					SCREAMING_SNAKE_CASE           =      True
					scheduler.step()
					if not step_scheduler_with_optimizer:
										SCREAMING_SNAKE_CASE           =      1 - 2 / 1_0
					assert (
					    scheduler.get_last_lr()[0] == expected_lr
					), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 2     ):
					from transformers import get_linear_schedule_with_warmup

					SCREAMING_SNAKE_CASE           =      [1, 2, 4]
					for num_steps in steps:
										SCREAMING_SNAKE_CASE           =      GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE_,	adjust_scheduler=num_steps > 1     )
										SCREAMING_SNAKE_CASE           =      Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE_     )
										SCREAMING_SNAKE_CASE           =      torch.nn.Linear(2,	4     )
										SCREAMING_SNAKE_CASE           =      torch.optim.AdamW(model.parameters(),	lr=10.0     )
										SCREAMING_SNAKE_CASE           =      get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE_,	num_warmup_steps=0,	num_training_steps=2_0     )

										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      accelerator.prepare(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

										for i in range(1_0 * num_steps     ):
															with accelerator.accumulate(SCREAMING_SNAKE_CASE_     ):
																				optimizer.step()
																				scheduler.step()

															if i == (1_0 * num_steps - 2):
																				assert (
																				    scheduler.get_last_lr()[0] != 0
																				), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
										assert (
										    scheduler.get_last_lr()[0] == 0
										), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
										GradientState._reset_state()




@require_cpu
class 	UpperCamelCase      (			unittest.TestCase ):

				"""simple docstring"""



				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									debug_launcher(partial(_A						,      num_processes=1     )						,      num_processes=1     )
									debug_launcher(partial(_A						,      num_processes=1						,      split_batches=_A     )						,      num_processes=1     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									debug_launcher(partial(_A						,      num_processes=1     )						,      num_processes=1     )
									debug_launcher(partial(_A						,      num_processes=1						,      split_batches=_A     )						,      num_processes=1     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									debug_launcher(partial(_A						,      num_processes=1						,      step_scheduler_with_optimizer=_A     )						,      num_processes=1     )


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									debug_launcher(partial(_A						,      num_processes=1						,      step_scheduler_with_optimizer=_A     )						,      num_processes=1     )


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									AcceleratorState._reset_state(_A     )
									debug_launcher(_A     )
									debug_launcher(partial(_A						,      num_processes=1						,      split_batches=_A     )						,      num_processes=1     )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									AcceleratorState._reset_state(_A     )
									debug_launcher(_A     )
									debug_launcher(partial(_A						,      num_processes=1						,      split_batches=_A     )						,      num_processes=1     )


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									AcceleratorState._reset_state(_A     )
									debug_launcher(partial(_A						,      step_scheduler_with_optimizer=_A     )     )


				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									AcceleratorState._reset_state(_A     )
									debug_launcher(partial(_A						,      step_scheduler_with_optimizer=_A     )     )




				@require_huggingface_suite
				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									AcceleratorState._reset_state(_A     )
									debug_launcher(partial(_A						,      num_processes=1     )     )
									debug_launcher(_A     )






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCamelCase__            =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class      a__       (   snake_case__ ):
						_a				:       Tuple   =       ["""safety_checker""", """feature_extractor"""]






						def __init__(  self						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A = True						,     ):




											"""simple docstring"""



											super().__init__()
											if hasattr(scheduler.config						,     "steps_offset"     ) and scheduler.config.steps_offset != 1:
																__lowerCAmelCase							  =			(
																    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
																    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
																    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
																    " file"
																)
																deprecate("steps_offset!=1"						,     "1.0.0"						,     _A						,     standard_warn=_A     )
																__lowerCAmelCase							  =			dict(scheduler.config     )
																__lowerCAmelCase							  =			1
																__lowerCAmelCase							  =			FrozenDict(_A     )

											self.register_modules(
											    vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     unet=_A						,     scheduler=_A						,     safety_checker=_A						,     feature_extractor=_A						,     )
											self.register_to_config(requires_safety_checker=_A     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return {k: getattr(self						,     _A     ) for k in self.config.keys() if not k.startswith("_"     )}






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A = "auto"     ):




											"""simple docstring"""



											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowerCAmelCase							  =			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.enable_attention_slicing(_A     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionInpaintPipelineLegacy(**self.components     )(
											    prompt=_A						,     image=_A						,     mask_image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     **_A						,     ):




											"""simple docstring"""



											return StableDiffusionImgaImgPipeline(**self.components     )(
											    prompt=_A						,     image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = 5_1_2						,     _A = 5_1_2						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionPipeline(**self.components     )(
											    prompt=_A						,     height=_A						,     width=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     latents=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )




