'''simple docstring'''


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case				:				Any       							=		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')
_snake_case				:				Tuple       							=		parser.parse_args()


_snake_case				:				int       							=		'cpu'
_snake_case				:				Optional[Any]       							=		'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

_snake_case				:				List[str]       							=		'path-to-your-trained-model'
_snake_case				:				int       							=		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
				_snake_case				:				List[Any]       							=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case				:				Dict       							=		pipe.to(device)

# to channels last
_snake_case				:				Optional[Any]       							=		pipe.unet.to(memory_format=torch.channels_last)
_snake_case				:				int       							=		pipe.vae.to(memory_format=torch.channels_last)
_snake_case				:				str       							=		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
				_snake_case				:				Dict       							=		pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
_snake_case				:				str       							=		666
_snake_case				:				str       							=		torch.Generator(device).manual_seed(seed)
_snake_case				:				Dict       							=		{'generator': generator}
if args.steps is not None:
				_snake_case				:				List[Any]       							=		args.steps

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

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







'''simple docstring'''


import copy
import random

from transformers import CLIPTokenizer

class       A   (		_a   ):







							def __init__(  self	:  str							,      *lowerCAmelCase_	:  int							,      **lowerCAmelCase_	:  List[str] )      ->							List[Any]:


														"""simple docstring"""


														super().__init__(*lowerCAmelCase_							,      **lowerCAmelCase_ )
														_a     					=    {}







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  Tuple							,      *lowerCAmelCase_	:  Any							,      **lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    super().add_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
														if num_added_tokens == 0:
																					raise ValueError(
																					    F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
																					    ''' `placeholder_token` that is not already in the tokenizer.''' )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict							,      *lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  Tuple=1							,      **lowerCAmelCase_	:  int )      ->							Any:


														"""simple docstring"""


														_a     					=    []
														if num_vec_per_token == 1:
																					self.try_adding_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
																					output.append(lowerCAmelCase_ )
														else:
																					_a     					=    []
																					for i in range(lowerCAmelCase_ ):
																												_a     					=    placeholder_token + F'_{i}'
																												self.try_adding_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
																												output.append(lowerCAmelCase_ )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
														for token in self.token_map:
																					if token in placeholder_token:
																												raise ValueError(
																												    F'The tokenizer already has placeholder token {token} that can get confused with'
																												    F' {placeholder_token}keep placeholder tokens independent' )
														_a     					=    output







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Tuple=False							,      lowerCAmelCase_	:  Optional[int]=1.0 )      ->							Tuple:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ):
																					_a     					=    []
																					for i in range(len(lowerCAmelCase_ ) ):
																												output.append(self.replace_placeholder_tokens_in_text(text[i]							,      vector_shuffle=lowerCAmelCase_ ) )
																					return output
														for placeholder_token in self.token_map:
																					if placeholder_token in text:
																												_a     					=    self.token_map[placeholder_token]
																												_a     					=    tokens[: 1 + int(len(lowerCAmelCase_ ) * prop_tokens_to_load )]
																												if vector_shuffle:
																																			_a     					=    copy.copy(lowerCAmelCase_ )
																																			random.shuffle(lowerCAmelCase_ )
																												_a     					=    text.replace(lowerCAmelCase_							,      ''' '''.join(lowerCAmelCase_ ) )
														return text







							def __call__(  self	:  List[str]							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Optional[int]=False							,      lowerCAmelCase_	:  Union[str, Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  int							,      *lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Any=False							,      lowerCAmelCase_	:  List[Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Dict:


														"""simple docstring"""


														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )

