


'''simple docstring'''




import warnings

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


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	str		=     '''mvp'''
     lowerCamelCase				:	str		=     ['''past_key_values''']
     lowerCamelCase				:	Tuple		=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      List[Any]=5_0_2_6_7    ,      UpperCAmelCase__       :      Union[str, Any]=1_0_2_4    ,      UpperCAmelCase__       :      Union[str, Any]=1_2    ,      UpperCAmelCase__       :      Optional[Any]=4_0_9_6    ,      UpperCAmelCase__       :      str=1_6    ,      UpperCAmelCase__       :      List[str]=1_2    ,      UpperCAmelCase__       :      int=4_0_9_6    ,      UpperCAmelCase__       :      List[Any]=1_6    ,      UpperCAmelCase__       :      List[Any]=0.0    ,      UpperCAmelCase__       :      Union[str, Any]=0.0    ,      UpperCAmelCase__       :      Any="gelu"    ,      UpperCAmelCase__       :      Optional[Any]=1_0_2_4    ,      UpperCAmelCase__       :      Union[str, Any]=0.1    ,      UpperCAmelCase__       :      List[Any]=0.0    ,      UpperCAmelCase__       :      Optional[int]=0.0    ,      UpperCAmelCase__       :      str=0.02    ,      UpperCAmelCase__       :      Optional[int]=0.0    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      Tuple=1    ,      UpperCAmelCase__       :      Optional[int]=0    ,      UpperCAmelCase__       :      int=2    ,      UpperCAmelCase__       :      List[str]=True    ,      UpperCAmelCase__       :      Any=2    ,      UpperCAmelCase__       :      List[str]=2    ,      UpperCAmelCase__       :      Optional[int]=False    ,      UpperCAmelCase__       :      Union[str, Any]=1_0_0    ,      UpperCAmelCase__       :      Tuple=8_0_0    ,      **UpperCAmelCase__       :      Optional[int]    ,      )				->  List[str]:
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			d_model
        lowerCAmelCase     =			encoder_ffn_dim
        lowerCAmelCase     =			encoder_layers
        lowerCAmelCase     =			encoder_attention_heads
        lowerCAmelCase     =			decoder_ffn_dim
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			decoder_attention_heads
        lowerCAmelCase     =			dropout
        lowerCAmelCase     =			attention_dropout
        lowerCAmelCase     =			activation_dropout
        lowerCAmelCase     =			activation_function
        lowerCAmelCase     =			init_std
        lowerCAmelCase     =			encoder_layerdrop
        lowerCAmelCase     =			decoder_layerdrop
        lowerCAmelCase     =			classifier_dropout
        lowerCAmelCase     =			use_cache
        lowerCAmelCase     =			encoder_layers
        lowerCAmelCase     =			scale_embedding  # scale factor will be sqrt(d_model) if True
        lowerCAmelCase     =			use_prompt
        lowerCAmelCase     =			prompt_length
        lowerCAmelCase     =			prompt_mid_dim

        super().__init__(
            pad_token_id=UpperCAmelCase__    ,      bos_token_id=UpperCAmelCase__    ,      eos_token_id=UpperCAmelCase__    ,      is_encoder_decoder=UpperCAmelCase__    ,      decoder_start_token_id=UpperCAmelCase__    ,      forced_eos_token_id=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )

        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			self.bos_token_id
           warnings.warn(
               F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
               'The config can simply be saved and uploaded again to be fixed.'    )









import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case             =     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''')
_snake_case             =     parser.parse_args()


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

_snake_case             =     '''path-to-your-trained-model'''
_snake_case             =     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   _snake_case             =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case             =     pipe.to(device)

# to channels last
_snake_case             =     pipe.unet.to(memory_format=torch.channels_last)
_snake_case             =     pipe.vae.to(memory_format=torch.channels_last)
_snake_case             =     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
   _snake_case             =     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_snake_case             =     torch.randn(2, 4, 64, 64)
_snake_case             =     torch.rand(1) * 9_99
_snake_case             =     torch.randn(2, 77, 7_68)
_snake_case             =     (sample, timestep, encoder_hidden_status)
try:
   _snake_case             =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   _snake_case             =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case             =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case             =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   _snake_case             =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case             =     6_66
_snake_case             =     torch.Generator(device).manual_seed(seed)
_snake_case             =     {'''generator''': generator}
if args.steps is not None:
   _snake_case             =     args.steps

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

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



