


'''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_xlnet import XLNetTokenizer
else:
						__magic_name__ : List[Any]					=None


__magic_name__ : Tuple					=logging.get_logger(__name__)

__magic_name__ : Optional[int]					={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__magic_name__ : List[str]					={
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

__magic_name__ : List[Any]					={
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

__magic_name__ : Any					='▁'

# Segments (not really needed)
__magic_name__ : Any					=0
__magic_name__ : int					=1
__magic_name__ : List[Any]					=2
__magic_name__ : Dict					=3
__magic_name__ : List[Any]					=4






class 		UpperCamelCase_		(       A ):





						"""simple docstring"""




						UpperCAmelCase__						:							Tuple       =  VOCAB_FILES_NAMES
						UpperCAmelCase__						:							str       =  PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase__						:							Dict       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase__						:							Any       =  '''left'''
						UpperCAmelCase__						:							List[Any]       =  XLNetTokenizer
						def __init__(			self					:       Optional[Any]						,					_lowerCamelCase					:       Tuple=None						,					_lowerCamelCase					:       str=None						,					_lowerCamelCase					:       List[str]=False						,					_lowerCamelCase					:       Tuple=True						,					_lowerCamelCase					:       List[Any]=False						,					_lowerCamelCase					:       Any="<s>"						,					_lowerCamelCase					:       str="</s>"						,					_lowerCamelCase					:       List[str]="<unk>"						,					_lowerCamelCase					:       Optional[int]="<sep>"						,					_lowerCamelCase					:       Any="<pad>"						,					_lowerCamelCase					:       List[Any]="<cls>"						,					_lowerCamelCase					:       List[str]="<mask>"						,					_lowerCamelCase					:       Union[str, Any]=["<eop>", "<eod>"]						,					**_lowerCamelCase					:       Optional[Any]						,					)				->       Optional[int]:
										# Mask token behave like a normal word, i.e. include the space before it
										__magic_name__							=			AddedToken(_lowerCamelCase						,					lstrip=_lowerCamelCase						,					rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase						,					_lowerCamelCase	) else mask_token

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

										__magic_name__							=			3
										__magic_name__							=			do_lower_case
										__magic_name__							=			remove_space
										__magic_name__							=			keep_accents
										__magic_name__							=			vocab_file
										__magic_name__							=			False if not self.vocab_file else True
						def        __A       (			self					:       Optional[Any]						,					_lowerCamelCase					:       Dict						,					_lowerCamelCase					:       Any = None	)				->       Tuple:
										__magic_name__							=			[self.sep_token_id]
										__magic_name__							=			[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls
						def        __A       (			self					:       Union[str, Any]						,					_lowerCamelCase					:       Tuple						,					_lowerCamelCase					:       List[Any] = None	)				->       Tuple:
										__magic_name__							=			[self.sep_token_id]
										__magic_name__							=			[2]

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

						def        __A       (			self					:       int						,					_lowerCamelCase					:       Union[str, Any]						,					_lowerCamelCase					:       str = None	)				->       Any:
										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
														    "tokenizer."	)

										if not os.path.isdir(_lowerCamelCase	):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
														return
										__magic_name__							=			os.path.join(
										    _lowerCamelCase						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

										return (out_vocab_file,)









"""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""")