




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


__A								=       logging.get_logger(__name__)

__A								=       {
    '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					(			_lowerCamelCase					):

			"""simple docstring"""







			snake_case_  						=			'''beit'''


			def __init__( self     ,		lowerCamelCase__=8_192     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=12     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=224     ,		lowerCamelCase__=16     ,		lowerCamelCase__=3     ,		lowerCamelCase__=False     ,		lowerCamelCase__=False     ,		lowerCamelCase__=False     ,		lowerCamelCase__=False     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=True     ,		lowerCamelCase__=[3, 5, 7, 11]     ,		lowerCamelCase__=[1, 2, 3, 6]     ,		lowerCamelCase__=True     ,		lowerCamelCase__=0.4     ,		lowerCamelCase__=256     ,		lowerCamelCase__=1     ,		lowerCamelCase__=False     ,		lowerCamelCase__=255     ,		**lowerCamelCase__     ,		)       -> Any:



							'''simple docstring'''
							super().__init__(**_SCREAMING_SNAKE_CASE				)

							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps

							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       use_mask_token
							__lowerCamelCase									=       use_absolute_position_embeddings
							__lowerCamelCase									=       use_relative_position_bias
							__lowerCamelCase									=       use_shared_relative_position_bias
							__lowerCamelCase									=       layer_scale_init_value
							__lowerCamelCase									=       drop_path_rate
							__lowerCamelCase									=       use_mean_pooling
							# decode head attributes (semantic segmentation)
							__lowerCamelCase									=       out_indices
							__lowerCamelCase									=       pool_scales
							# auxiliary head attributes (semantic segmentation)
							__lowerCamelCase									=       use_auxiliary_head
							__lowerCamelCase									=       auxiliary_loss_weight
							__lowerCamelCase									=       auxiliary_channels
							__lowerCamelCase									=       auxiliary_num_convs
							__lowerCamelCase									=       auxiliary_concat_input
							__lowerCamelCase									=       semantic_loss_ignore_index







class        __lowerCAmelCase					(			_lowerCamelCase					):

			"""simple docstring"""







			snake_case_  						=			version.parse('''1.11'''					)


			@property
			def  lowercase_       ( self				)       -> Mapping[str, Mapping[int, str]]:



							'''simple docstring'''
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]				)






			@property
			def  lowercase_       ( self				)       -> float:



							'''simple docstring'''
							return 1e-4







"""simple docstring"""

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[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_						:    Tuple				=      parser.parse_args()


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

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

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

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

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

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

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





