


'''simple docstring'''
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__magic_name__ : Optional[Any]					=logging.getLogger(__name__)






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__(			self					:       Any	)				->       Dict:
										__magic_name__							=			False
						def        __A       (			self					:       Tuple						,					_lowerCamelCase					:       Dict						,					_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       Optional[Any]						,					_lowerCamelCase					:       Optional[int]	)				->       Optional[Any]:
										if not self.initialized:
														__magic_name__							=			RagRetriever(
														    _lowerCamelCase						,					question_encoder_tokenizer=_lowerCamelCase						,					generator_tokenizer=_lowerCamelCase						,					index=_lowerCamelCase						,					init_retrieval=_lowerCamelCase						,					)
														__magic_name__							=			True
						def        __A       (			self					:       int	)				->       Dict:
										self.retriever.index.init_index()

						def        __A       (			self					:       int						,					_lowerCamelCase					:       List[str]						,					_lowerCamelCase					:       int	)				->       int:
										__magic_name__							=			self.retriever._main_retrieve(_lowerCamelCase						,					_lowerCamelCase	)
										return doc_ids, retrieved_doc_embeds






class 		UpperCamelCase_		(       A ):





						"""simple docstring"""




						def __init__(			self					:       Dict						,					_lowerCamelCase					:       Dict						,					_lowerCamelCase					:       Dict						,					_lowerCamelCase					:       int						,					_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       int=None	)				->       List[Any]:
										if index is not None and index.is_initialized() and len(_lowerCamelCase	) > 0:
														raise ValueError(
														    "When using Ray for distributed fine-tuning, "
														    "you\'ll need to provide the paths instead, "
														    "as the dataset and the index are loaded "
														    "separately. More info in examples/rag/use_own_knowledge_dataset.py "	)
										super().__init__(
										    _lowerCamelCase						,					question_encoder_tokenizer=_lowerCamelCase						,					generator_tokenizer=_lowerCamelCase						,					index=_lowerCamelCase						,					init_retrieval=_lowerCamelCase						,					)
										__magic_name__							=			retrieval_workers
										if len(self.retrieval_workers	) > 0:
														ray.get(
														    [
														        worker.create_rag_retriever.remote(_lowerCamelCase						,					_lowerCamelCase						,					_lowerCamelCase						,					_lowerCamelCase	)
														        for worker in self.retrieval_workers
														    ]	)
						def        __A       (			self					:       Union[str, Any]	)				->       Any:
										logger.info("initializing retrieval"	)

										if len(self.retrieval_workers	) > 0:
														ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]	)
										else:
														# Non-distributed training. Load index into this same process.
														self.index.init_index()
						def        __A       (			self					:       str						,					_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       str	)				->       int:
										if len(self.retrieval_workers	) > 0:
														# Select a random retrieval actor.
														__magic_name__							=			self.retrieval_workers[random.randint(0						,					len(self.retrieval_workers	) - 1	)]
														__magic_name__							=			ray.get(random_worker.retrieve.remote(_lowerCamelCase						,					_lowerCamelCase	)	)
										else:
														__magic_name__							=			self._main_retrieve(_lowerCamelCase						,					_lowerCamelCase	)
										return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCamelCase	)
						@classmethod
						def        __A       (			cls					:       Optional[int]						,					_lowerCamelCase					:       Union[str, Any]						,					_lowerCamelCase					:       Optional[int]=None						,					**_lowerCamelCase					:       Optional[Any]	)				->       Optional[int]:
										return super(_lowerCamelCase						,					cls	).get_tokenizers(_lowerCamelCase						,					_lowerCamelCase						,					**_lowerCamelCase	)

						@classmethod
						def        __A       (			cls					:       Any						,					_lowerCamelCase					:       Tuple						,					_lowerCamelCase					:       Tuple						,					_lowerCamelCase					:       List[Any]=None						,					**_lowerCamelCase					:       Union[str, Any]	)				->       Tuple:
										__magic_name__							=			kwargs.pop("config"						,					_lowerCamelCase	) or RagConfig.from_pretrained(_lowerCamelCase						,					**_lowerCamelCase	)
										__magic_name__							=			RagTokenizer.from_pretrained(_lowerCamelCase						,					config=_lowerCamelCase	)
										__magic_name__							=			rag_tokenizer.question_encoder
										__magic_name__							=			rag_tokenizer.generator
										if indexed_dataset is not None:
														__magic_name__							=			'custom'
														__magic_name__							=			CustomHFIndex(config.retrieval_vector_size						,					_lowerCamelCase	)
										else:
														__magic_name__							=			cls._build_index(_lowerCamelCase	)
										return cls(
										    _lowerCamelCase						,					question_encoder_tokenizer=_lowerCamelCase						,					generator_tokenizer=_lowerCamelCase						,					retrieval_workers=_lowerCamelCase						,					index=_lowerCamelCase						,					)









"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__			=		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__			=		parser.parse_args()


lowercase__			=		"""cpu"""
lowercase__			=		"""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

lowercase__			=		"""path-to-your-trained-model"""
lowercase__			=		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
			lowercase__			=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__			=		pipe.to(device)

# to channels last
lowercase__			=		pipe.unet.to(memory_format=torch.channels_last)
lowercase__			=		pipe.vae.to(memory_format=torch.channels_last)
lowercase__			=		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
			lowercase__			=		pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase__			=		666
lowercase__			=		torch.Generator(device).manual_seed(seed)
lowercase__			=		{"""generator""": generator}
if args.steps is not None:
			lowercase__			=		args.steps

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

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