


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase					:       Optional[Any]			    =     logging.get_logger(__name__)

UpperCamelCase					:       str			    =     {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'gpt_neox'

  def __init__( self			:  int				,				lowerCamelCase__			:  int=50_432				,				lowerCamelCase__			:  Optional[int]=6_144				,				lowerCamelCase__			:  Optional[Any]=44				,				lowerCamelCase__			:  Tuple=64				,				lowerCamelCase__			:  Union[str, Any]=24_576				,				lowerCamelCase__			:  List[str]="gelu"				,				lowerCamelCase__			:  Union[str, Any]=0.25				,				lowerCamelCase__			:  Optional[int]=10_000				,				lowerCamelCase__			:  Union[str, Any]=0.0				,				lowerCamelCase__			:  List[Any]=0.0				,				lowerCamelCase__			:  int=0.1				,				lowerCamelCase__			:  Tuple=2_048				,				lowerCamelCase__			:  str=0.02				,				lowerCamelCase__			:  Any=1E-5				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Optional[Any]=0				,				lowerCamelCase__			:  Tuple=2				,				lowerCamelCase__			:  Optional[Any]=False				,				lowerCamelCase__			:  str=True				,				lowerCamelCase__			:  Optional[int]=None				,				**lowerCamelCase__			:  Tuple				,				):
        super().__init__(bos_token_id=lowerCamelCase__				,				eos_token_id=lowerCamelCase__				,				**lowerCamelCase__       )
        a__	:  int     =   vocab_size
        a__	:  str     =   max_position_embeddings
        a__	:  Optional[int]     =   hidden_size
        a__	:  List[str]     =   num_hidden_layers
        a__	:  Tuple     =   num_attention_heads
        a__	:  int     =   intermediate_size
        a__	:  Optional[int]     =   hidden_act
        a__	:  str     =   rotary_pct
        a__	:  Optional[Any]     =   rotary_emb_base
        a__	:  Any     =   attention_dropout
        a__	:  Dict     =   hidden_dropout
        a__	:  Optional[int]     =   classifier_dropout
        a__	:  Dict     =   initializer_range
        a__	:  int     =   layer_norm_eps
        a__	:  Tuple     =   use_cache
        a__	:  Optional[Any]     =   tie_word_embeddings
        a__	:  List[str]     =   use_parallel_residual
        a__	:  str     =   rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
              raise ValueError(
                  "The hidden size is not divisble by the number of attention heads! Make sure to update them!"       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        if self.rope_scaling is None:
              return

        if not isinstance(self.rope_scaling				,				lowerCamelCase__       ) or len(self.rope_scaling       ) != 2:
              raise ValueError(
                  "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                  f'''got {self.rope_scaling}'''       )
        a__	:  Dict     =   self.rope_scaling.get("type"				,				lowerCamelCase__       )
        a__	:  List[str]     =   self.rope_scaling.get("factor"				,				lowerCamelCase__       )
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
              raise ValueError(
                  f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''       )
        if rope_scaling_factor is None or not isinstance(lowerCamelCase__				,				lowerCamelCase__       ) or rope_scaling_factor <= 1.0:
              raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )








import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[str]         =    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__							:      Tuple         =    parser.parse_args()


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

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

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

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

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

