

"""simple docstring"""


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")





"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			__A						   =			None

__A						   =			logging.get_logger(__name__)
__A						   =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__A						   =			{
    "google/rembert": 256,
}

__A						   =			"▁"





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Any         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Tuple         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Dict         =  RemBertTokenizer

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Dict=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Optional[int]=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				int="[CLS]"     ,  UpperCamelCase__							:				Optional[Any]="[SEP]"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="[SEP]"     ,  UpperCamelCase__							:				int="<pad>"     ,  UpperCamelCase__							:				Any="[CLS]"     ,  UpperCamelCase__							:				str="[MASK]"     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else mask_token

						super().__init__(
						    UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Optional[int]          =			do_lower_case
						__lowerCAmelCase:       int          =			remove_space
						__lowerCAmelCase:       int          =			keep_accents
						__lowerCAmelCase:       str          =			vocab_file
						__lowerCAmelCase:       Tuple          =			False if not self.vocab_file else True

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[self.cls_token_id]
						if token_ids_a is None:
								return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None     ,  UpperCamelCase__							:				bool = False)->   List[int]:



						'''simple docstring'''


						if already_has_special_tokens:
								if token_ids_a is not None:
										raise ValueError(
										    "You should not supply a second sequence if the provided sequence of "
										    "ids is already formatted with special tokens for the model.")
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
								return [1] + ([0] * len(UpperCamelCase__)) + [1] + ([0] * len(UpperCamelCase__)) + [1]
						return [1] + ([0] * len(UpperCamelCase__)) + [1]

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Dict          =			[self.cls_token_id]

						if token_ids_a is None:
								return len(cls + token_ids_a + sep) * [0]
						return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						if not os.path.isdir(UpperCamelCase__):
								logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__))
								return
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(
						    UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

						if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__):
								copyfile(self.vocab_file     ,  UpperCamelCase__)

						return (out_vocab_file,)


