
import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      Any									= argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
lowercase						:      Tuple									= parser.parse_args()


lowercase						:      str									= 'cpu'
lowercase						:      List[Any]									= 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase						:      Any									= 'path-to-your-trained-model'
lowercase						:      List[Any]									= StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowercase						:      Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase						:      Dict									= pipe.to(device)

# to channels last
lowercase						:      int									= pipe.unet.to(memory_format=torch.channels_last)
lowercase						:      int									= pipe.vae.to(memory_format=torch.channels_last)
lowercase						:      Dict									= pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase						:      List[str]									= pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase						:      Any									= torch.randn(2, 4, 64, 64)
lowercase						:      Union[str, Any]									= torch.rand(1) * 999
lowercase						:      List[Any]									= torch.randn(2, 77, 768)
lowercase						:      List[Any]									= (sample, timestep, encoder_hidden_status)
try:
		lowercase						:      List[Any]									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		lowercase						:      str									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      Dict									= ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      str									= ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowercase						:      Dict									= ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase						:      Any									= 666
lowercase						:      int									= torch.Generator(device).manual_seed(seed)
lowercase						:      List[str]									= {'generator': generator}
if args.steps is not None:
		lowercase						:      List[str]									= args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		lowercase						:      Dict									= pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')