
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase						:      List[str]									= logging.get_logger(__name__)

lowercase						:      Optional[int]									= {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'beit'







	def __init__(      self	:str  ,     a	:Any=8_1_9_2  ,     a	:List[Any]=7_6_8  ,     a	:Optional[int]=1_2  ,     a	:Optional[Any]=1_2  ,     a	:Union[str, Any]=3_0_7_2  ,     a	:Optional[int]="gelu"  ,     a	:Optional[int]=0.0  ,     a	:Optional[Any]=0.0  ,     a	:str=0.02  ,     a	:List[Any]=1E-1_2  ,     a	:List[Any]=2_2_4  ,     a	:Optional[Any]=1_6  ,     a	:Dict=3  ,     a	:Tuple=False  ,     a	:Dict=False  ,     a	:Optional[int]=False  ,     a	:Optional[Any]=False  ,     a	:Any=0.1  ,     a	:str=0.1  ,     a	:str=True  ,     a	:Union[str, Any]=[3, 5, 7, 1_1]  ,     a	:Dict=[1, 2, 3, 6]  ,     a	:Union[str, Any]=True  ,     a	:int=0.4  ,     a	:List[Any]=2_5_6  ,     a	:List[Any]=1  ,     a	:int=False  ,     a	:List[str]=2_5_5  ,     **a	:List[Any]  ,     )	->				Union[str, Any]:
								super().__init__(**a		)

								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     Union[str, Any]         =      hidden_size
								__UpperCamelCase			:     Dict         =      num_hidden_layers
								__UpperCamelCase			:     Optional[Any]         =      num_attention_heads
								__UpperCamelCase			:     List[Any]         =      intermediate_size
								__UpperCamelCase			:     str         =      hidden_act
								__UpperCamelCase			:     List[str]         =      hidden_dropout_prob
								__UpperCamelCase			:     Any         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Dict         =      initializer_range
								__UpperCamelCase			:     Dict         =      layer_norm_eps

								__UpperCamelCase			:     Tuple         =      image_size
								__UpperCamelCase			:     Union[str, Any]         =      patch_size
								__UpperCamelCase			:     List[str]         =      num_channels
								__UpperCamelCase			:     int         =      use_mask_token
								__UpperCamelCase			:     Optional[Any]         =      use_absolute_position_embeddings
								__UpperCamelCase			:     str         =      use_relative_position_bias
								__UpperCamelCase			:     Dict         =      use_shared_relative_position_bias
								__UpperCamelCase			:     Optional[Any]         =      layer_scale_init_value
								__UpperCamelCase			:     int         =      drop_path_rate
								__UpperCamelCase			:     Any         =      use_mean_pooling
								# decode head attributes (semantic segmentation)
								__UpperCamelCase			:     Any         =      out_indices
								__UpperCamelCase			:     List[Any]         =      pool_scales
								# auxiliary head attributes (semantic segmentation)
								__UpperCamelCase			:     List[Any]         =      use_auxiliary_head
								__UpperCamelCase			:     int         =      auxiliary_loss_weight
								__UpperCamelCase			:     Any         =      auxiliary_channels
								__UpperCamelCase			:     Tuple         =      auxiliary_num_convs
								__UpperCamelCase			:     Tuple         =      auxiliary_concat_input
								__UpperCamelCase			:     Any         =      semantic_loss_ignore_index




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  version.parse('1.11')







	@property
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ]		)







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				float:
								return 1E-4

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


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

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

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

# optimize with ipex
lowercase						:      Any									= torch.randn(2, 4, 64, 64)
lowercase						:      Union[str, Any]									= torch.rand(1) * 999
lowercase						:      List[Any]									= torch.randn(2, 77, 768)
lowercase						:      List[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						:      str									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      Dict									= ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      str									= ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowercase						:      Dict									= ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

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

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

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