
'''simple docstring'''
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A__   :		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')
A__   :		Any 		=parser.parse_args()


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

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

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

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

# compute
A__   :		Any 		=666
A__   :		Optional[Any] 		=torch.Generator(device).manual_seed(seed)
A__   :		Optional[int] 		={'generator': generator}
if args.steps is not None:
     A__   :		Optional[int] 		=args.steps

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

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





'''simple docstring'''
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A__   :		Dict 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''input_values''', '''padding_mask''']
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    int = 1				,   lowerCamelCase			:    int = 2_40_00				,   lowerCamelCase			:    float = 0.0				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    float = None				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   super().__init__(feature_size=lowerCamelCase				,   sampling_rate=lowerCamelCase				,   padding_value=lowerCamelCase				,   **lowerCamelCase       )
   __A		:     str												=		chunk_length_s
   __A		:     str												=		overlap
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   if self.chunk_length_s is None:
     return None
   else:
     return int(self.chunk_length_s * self.sampling_rate       )
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   if self.chunk_length_s is None or self.overlap is None:
     return None
   else:
     return max(1				,   int((1.0 - self.overlap) * self.chunk_length       )       )






 def __call__(   self			:    List[str]				,   lowerCamelCase			:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,   lowerCamelCase			:    Optional[Union[bool, str, PaddingStrategy]] = None				,   lowerCamelCase			:    Optional[bool] = False				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    Optional[int] = None				,   ):



   """simple docstring"""





   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
           f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
           f" {self.sampling_rate} and not {sampling_rate}."       )
   else:
     logger.warning(
         """It is strongly recommended to pass the `sampling_rate` argument to this function. """
         """Failing to do so can result in silent errors that might be hard to debug."""       )

   if padding and truncation:
     raise ValueError("""Both padding and truncation were set. Make sure you only set one."""       )
   elif padding is None:
     # by default let's pad the inputs
     __A		:     List[Any]												=		True

   __A		:     List[str]												=		bool(
       isinstance(lowerCamelCase				,   (list, tuple)       ) and (isinstance(raw_audio[0]				,   (np.ndarray, tuple, list)       ))       )

   if is_batched:
     __A		:     Optional[Any]												=		[np.asarray(lowerCamelCase				,   dtype=np.floataa       ).T for audio in raw_audio]
   elif not is_batched and not isinstance(lowerCamelCase				,   np.ndarray       ):
     __A		:     Union[str, Any]												=		np.asarray(lowerCamelCase				,   dtype=np.floataa       )
   elif isinstance(lowerCamelCase				,   np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
     __A		:     Optional[Any]												=		raw_audio.astype(np.floataa       )

   # always return batch
   if not is_batched:
     __A		:     Tuple												=		[np.asarray(lowerCamelCase       ).T]

   # verify inputs are valid
   for idx, example in enumerate(lowerCamelCase       ):
     if example.ndim > 2:
       raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}"       )
     if self.feature_size == 1 and example.ndim != 1:
       raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels"       )
     if self.feature_size == 2 and example.shape[-1] != 2:
       raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels"       )

   __A		:     Optional[Any]												=		None
   __A		:     Dict												=		BatchFeature({"""input_values""": raw_audio}       )
   if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
     if truncation:
       __A		:     Optional[Any]												=		min(array.shape[0] for array in raw_audio       )
       __A		:     Dict												=		int(np.floor(max_length / self.chunk_stride       )       )
       __A		:     Union[str, Any]												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
     elif padding:
       __A		:     int												=		max(array.shape[0] for array in raw_audio       )
       __A		:     Optional[Any]												=		int(np.ceil(max_length / self.chunk_stride       )       )
       __A		:     Any												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
       __A		:     Optional[int]												=		"""max_length"""
     else:
       __A		:     Optional[Any]												=		input_values

        # normal padding on batch
   if padded_inputs is None:
     __A		:     Tuple												=		self.pad(
         lowerCamelCase				,   max_length=lowerCamelCase				,   truncation=lowerCamelCase				,   padding=lowerCamelCase				,   return_attention_mask=lowerCamelCase				,   )
     if padding:
       __A		:     List[Any]												=		padded_inputs.pop("""attention_mask"""       )

   __A		:     List[Any]												=		[]
   for example in padded_inputs.pop("""input_values"""       ):
     if self.feature_size == 1:
       __A		:     Optional[Any]												=		example[..., None]
     input_values.append(example.T       )

   __A		:     str												=		input_values
   if return_tensors is not None:
     __A		:     Any												=		padded_inputs.convert_to_tensors(lowerCamelCase       )

   return padded_inputs



