



"""simple docstring"""





from pathlib import Path

import fire
from tqdm import tqdm
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any="ro"					, __UpperCamelCase      :			Union[str, Any]="en"					, __UpperCamelCase      :			Union[str, Any]="wmt16"					, __UpperCamelCase      :			int=None    ):


						'''simple docstring'''






						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("""run pip install datasets"""    )
						snake_case_   : Tuple						=			F'{src_lang}-{tgt_lang}'
						print(F'Converting {dataset}-{pair}'    )
						snake_case_   : str						=			datasets.load_dataset(__UpperCamelCase					, __UpperCamelCase    )
						if save_dir is None:
												snake_case_   : List[Any]						=			F'{dataset}-{pair}'
						snake_case_   : List[str]						=			Path(__UpperCamelCase    )
						save_dir.mkdir(exist_ok=__UpperCamelCase    )

						for split in ds.keys():
												print(F'Splitting {split} with {ds[split].num_rows} records'    )

												# to save to val.source, val.target like summary datasets
												snake_case_   : Union[str, Any]						=			"""val""" if split == """validation""" else split
												snake_case_   : List[str]						=			save_dir.joinpath(F'{fn}.source'    )
												snake_case_   : Optional[Any]						=			save_dir.joinpath(F'{fn}.target'    )
												snake_case_   : List[str]						=			src_path.open("""w+"""    )
												snake_case_   : Dict						=			tgt_path.open("""w+"""    )

												# reader is the bottleneck so writing one record at a time doesn't slow things down
												for x in tqdm(ds[split]    ):
																		snake_case_   : int						=			x["""translation"""]
																		src_fp.write(ex[src_lang] + """\n"""    )
																		tgt_fp.write(ex[tgt_lang] + """\n"""    )

						print(F'Saved {dataset} dataset to {save_dir}'    )


if __name__ == "__main__":
					fire.Fire(download_wmt_dataset)






"""simple docstring"""





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__lowerCAmelCase       :					Optional[int]      					=					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''')
__lowerCAmelCase       :					Optional[Any]      					=					parser.parse_args()


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

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

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

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

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

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

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

