from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class   snake_case_       (		_lowerCamelCase       ):
   __A	:		Union[str, Any]         =				42
   __A	:		Union[str, Any]         =				42
   __A	:		Tuple         =				None



class   snake_case_       (		_lowerCamelCase  ,_lowerCamelCase       ):
   __A	:		str         =				2






   @register_to_config
   def __init__(      self				:     int	,						lowercase_				:     List[Any] = 0.02	,						lowercase_				:     str = 1_00	,						lowercase_				:     Tuple = 1.0_07	,						lowercase_				:     Optional[int] = 80	,						lowercase_				:     Dict = 0.05	,						lowercase_				:     Any = 50	,						)					->						List[str]:
       lowercase__ :   List[str]    					=				sigma_max

       # setable values
       lowercase__ :   str    					=				None
       lowercase__ :   Union[str, Any]    					=				None
       lowercase__ :   Dict    					=				None  # sigma(t_i)






   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     List[str]	,						lowercase_				:     Union[str, Any] = None  )					->						Optional[Any]:
       return sample






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     List[str]	,						lowercase_				:     Any = None  )					->						Union[str, Any]:
       lowercase__ :   Any    					=				num_inference_steps
       lowercase__ :   Optional[int]    					=				np.arange(0	,						self.num_inference_steps  )[::-1].copy()
       lowercase__ :   Tuple    					=				torch.from_numpy(lowerCAmelCase_  ).to(lowerCAmelCase_  )
       lowercase__ :   Optional[int]    					=				[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowercase__ :   List[Any]    					=				torch.tensor(lowerCAmelCase_	,						dtype=torch.floataa	,						device=lowerCAmelCase_  )






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






       if self.config.s_min <= sigma <= self.config.s_max:
           lowercase__ :   int    					=				min(self.config.s_churn / self.num_inference_steps	,						2**0.5 - 1  )
       else:
           lowercase__ :   Tuple    					=				0

       # sample eps ~ N(0, S_noise^2 * I)
       lowercase__ :   Tuple    					=				self.config.s_noise * randn_tensor(sample.shape	,						generator=lowerCAmelCase_  ).to(sample.device  )
       lowercase__ :   str    					=				sigma + gamma * sigma
       lowercase__ :   Optional[Any]    					=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     Optional[int]	,						lowercase_				:     List[str]	,						lowercase_				:     List[Any]	,						lowercase_				:     List[Any]	,						lowercase_				:     Tuple = True	,						)					->						Union[str, Any]:
       lowercase__ :   Any    					=				sample_hat + sigma_hat * model_output
       lowercase__ :   List[Any]    					=				(sample_hat - pred_original_sample) / sigma_hat
       lowercase__ :   int    					=				sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
           return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=lowerCAmelCase_	,						derivative=lowerCAmelCase_	,						pred_original_sample=lowerCAmelCase_  )






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     int	,						lowercase_				:     int	,						lowercase_				:     str	,						lowercase_				:     Optional[int]	,						lowercase_				:     Tuple	,						lowercase_				:     Optional[int]	,						lowercase_				:     Any = True	,						)					->						Tuple:
       lowercase__ :   Dict    					=				sample_prev + sigma_prev * model_output
       lowercase__ :   Tuple    					=				(sample_prev - pred_original_sample) / sigma_prev
       lowercase__ :   Any    					=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
           return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=lowerCAmelCase_	,						derivative=lowerCAmelCase_	,						pred_original_sample=lowerCAmelCase_  )







   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     str  )					->						Optional[Any]:
       raise NotImplementedError()





'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase    :							Optional[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")
lowercase    :							Tuple      =					parser.parse_args()


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

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

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

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

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

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

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

