



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE     				=						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")
SCREAMING_SNAKE_CASE     				=						parser.parse_args()


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

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

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

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

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

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

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










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    List[str]									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    str									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Union[str, Any]									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    int									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    str									=  DistilBertTokenizer



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase="[UNK]"    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase="[PAD]"    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase="[MASK]"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	do_lower_case=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	sep_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	cls_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	tokenize_chinese_chars=lowerCAmelCase    ,	strip_accents=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
							if (
							    normalizer_state.get("lowercase"    ,	lowerCAmelCase     ) != do_lower_case
							    or normalizer_state.get("strip_accents"    ,	lowerCAmelCase     ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars"    ,	lowerCAmelCase     ) != tokenize_chinese_chars
							):
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	normalizer_state.pop("type"     )     )
									UpperCAmelCase_						=				do_lower_case
									UpperCAmelCase_						=				strip_accents
									UpperCAmelCase_						=				tokenize_chinese_chars
									UpperCAmelCase_						=				normalizer_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

