

"""simple docstring"""




import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a		=logging.getLogger(__name__)






def        SCREAMING_SNAKE_CASE__	(  )  ->					Tuple:
	__lowerCamelCase    :    Dict 	=			argparse.ArgumentParser(
	    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'      )
	parser.add_argument('--file_path'     ,			type=lowerCamelCase__     ,			default='data/dump.txt'     ,			help='The path to the data.'      )
	parser.add_argument('--tokenizer_type'     ,			type=lowerCamelCase__     ,			default='bert'     ,			choices=['bert', 'roberta', 'gpt2']      )
	parser.add_argument('--tokenizer_name'     ,			type=lowerCamelCase__     ,			default='bert-base-uncased'     ,			help='The tokenizer to use.'      )
	parser.add_argument('--dump_file'     ,			type=lowerCamelCase__     ,			default='data/dump'     ,			help='The dump file prefix.'      )
	__lowerCamelCase    :    int 	=			parser.parse_args()

	logger.info(F"Loading Tokenizer ({args.tokenizer_name})"      )
	if args.tokenizer_type == "bert":
		__lowerCamelCase    :    int 	=			BertTokenizer.from_pretrained(args.tokenizer_name      )
		__lowerCamelCase    :    str 	=			tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
		__lowerCamelCase    :    List[str] 	=			tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		__lowerCamelCase    :    Optional[Any] 	=			RobertaTokenizer.from_pretrained(args.tokenizer_name      )
		__lowerCamelCase    :    Optional[int] 	=			tokenizer.special_tokens_map['''cls_token''']  # `<s>`
		__lowerCamelCase    :    str 	=			tokenizer.special_tokens_map['''sep_token''']  # `</s>`
	elif args.tokenizer_type == "gpt2":
		__lowerCamelCase    :    List[str] 	=			GPTaTokenizer.from_pretrained(args.tokenizer_name      )
		__lowerCamelCase    :    Optional[Any] 	=			tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

	logger.info(F"Loading text from {args.file_path}"      )
	with open(args.file_path     ,			'r'     ,			encoding='utf8'      ) as fp:
		__lowerCamelCase    :    List[Any] 	=			fp.readlines()

	logger.info('Start encoding'      )
	logger.info(F"{len(lowerCamelCase__      )} examples to process."      )

	__lowerCamelCase    :    Dict 	=			[]
	__lowerCamelCase    :    Optional[Any] 	=			0
	__lowerCamelCase    :    Tuple 	=			1_0_0_0_0
	__lowerCamelCase    :    Any 	=			time.time()
	for text in data:
		__lowerCamelCase    :    Union[str, Any] 	=			F"{bos} {text.strip()} {sep}"
		__lowerCamelCase    :    Optional[int] 	=			tokenizer.encode(lowerCamelCase__     ,			add_special_tokens=lowerCamelCase__      )
		rslt.append(lowerCamelCase__      )

		iter += 1
		if iter % interval == 0:
			__lowerCamelCase    :    List[str] 	=			time.time()
			logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"      )
			__lowerCamelCase    :    Any 	=			time.time()
	logger.info('Finished binarization'      )
	logger.info(F"{len(lowerCamelCase__      )} examples processed."      )

	__lowerCamelCase    :    Any 	=			F"{args.dump_file}.{args.tokenizer_name}.pickle"
	__lowerCamelCase    :    int 	=			tokenizer.vocab_size
	if vocab_size < (1 << 1_6):
		__lowerCamelCase    :    List[Any] 	=			[np.uintaa(lowerCamelCase__      ) for d in rslt]
	else:
		__lowerCamelCase    :    int 	=			[np.intaa(lowerCamelCase__      ) for d in rslt]
	random.shuffle(rslt_      )
	logger.info(F"Dump to {dp_file}"      )
	with open(lowerCamelCase__     ,			'wb'      ) as handle:
		pickle.dump(rslt_     ,			lowerCamelCase__     ,			protocol=pickle.HIGHEST_PROTOCOL      )


if __name__ == "__main__":
					main()







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=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""")
a		=parser.parse_args()


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

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

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

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

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

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

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




