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


__A							=logging.get_logger(__name__)

__A							={
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}




class        _SCREAMING_SNAKE_CASE	( _UpperCamelCase		):
	lowerCAmelCase__   =					"""switch_transformers"""
	lowerCAmelCase__   =					["""past_key_values"""]
	lowerCAmelCase__   =					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



	def __init__(       self		,    lowercase=32128		,    lowercase=768		,    lowercase=64		,    lowercase=2048		,    lowercase=64		,    lowercase=12		,    lowercase=3		,    lowercase=12		,    lowercase=3		,    lowercase=12		,    lowercase=8		,    lowercase=False		,    lowercase=0.0_1		,    lowercase="float32"		,    lowercase=False		,    lowercase=32		,    lowercase=128		,    lowercase=0.1		,    lowercase=1e-6		,    lowercase=0.0_0_1		,    lowercase=0.0_0_1		,    lowercase=1.0		,    lowercase="relu"		,    lowercase=True		,    lowercase=False		,    lowercase=True		,    lowercase=0		,    lowercase=1		,    **lowercase		,    )							->			Optional[Any]:
					lowerCamelCase_         =     vocab_size
					lowerCamelCase_         =     d_model
					lowerCamelCase_         =     d_kv
					lowerCamelCase_         =     d_ff

					lowerCamelCase_         =     num_sparse_encoder_layers

					lowerCamelCase_         =     num_layers
					lowerCamelCase_         =     (
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					lowerCamelCase_         =     num_sparse_decoder_layers

					# This tells us, each how many encoder layer we'll have to set a sparse layer.
					if self.num_sparse_encoder_layers > 0:
									lowerCamelCase_         =     self.num_layers // self.num_sparse_encoder_layers
					else:
									lowerCamelCase_         =     self.num_layers  # HACK: this will create 0 sparse layers

					# This tells us, each how many encoder layer we'll have to set a sparse layer.
					if self.num_sparse_decoder_layers > 0:
									lowerCamelCase_         =     self.num_decoder_layers // self.num_sparse_decoder_layers
					else:
									lowerCamelCase_         =     self.num_decoder_layers  # HACK: this will create 0 sparse layers

					lowerCamelCase_         =     num_heads
					lowerCamelCase_         =     num_experts
					lowerCamelCase_         =     expert_capacity
					lowerCamelCase_         =     router_bias
					lowerCamelCase_         =     router_jitter_noise
					if router_dtype not in ["float32", "float16", "bfloat16"]:
									raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'					)
					lowerCamelCase_         =     router_dtype

					lowerCamelCase_         =     router_ignore_padding_tokens
					lowerCamelCase_         =     relative_attention_num_buckets
					lowerCamelCase_         =     relative_attention_max_distance

					lowerCamelCase_         =     dropout_rate
					lowerCamelCase_         =     layer_norm_epsilon
					lowerCamelCase_         =     initializer_factor
					lowerCamelCase_         =     feed_forward_proj
					lowerCamelCase_         =     use_cache
					lowerCamelCase_         =     add_router_probs

					lowerCamelCase_         =     router_z_loss_coef
					lowerCamelCase_         =     router_aux_loss_coef

					lowerCamelCase_         =     self.feed_forward_proj.split("-"					)
					lowerCamelCase_         =     act_info[-1]
					lowerCamelCase_         =     act_info[0] == "gated"

					if len(lowercase					) > 1 and act_info[0] != "gated" or len(lowercase					) > 2:
									raise ValueError(
									    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
									    "'gated-gelu' or 'relu'"					)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
									lowerCamelCase_         =     "gelu_new"

					super().__init__(
					    pad_token_id=lowercase		,    eos_token_id=lowercase		,    is_encoder_decoder=lowercase		,    **lowercase		,    )









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""")

