

'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       ['image_processor', 'tokenizer']
 UpperCamelCase__     =       'BlipImageProcessor'
 UpperCamelCase__     =       'AutoTokenizer'







 def __init__(       self  , snake_case_  , snake_case_   ):
   lowercase									=False
   super().__init__(snake_case_  , snake_case_   )
   lowercase									=self.image_processor







 def __call__(       self  , snake_case_ = None  , snake_case_ = None  , snake_case_ = True  , snake_case_ = False  , snake_case_ = None  , snake_case_ = None  , snake_case_ = 0  , snake_case_ = None  , snake_case_ = None  , snake_case_ = False  , snake_case_ = False  , snake_case_ = False  , snake_case_ = False  , snake_case_ = False  , snake_case_ = True  , snake_case_ = None  , **snake_case_  , ):
   if images is None and text is None:
     raise ValueError('''You have to specify either images or text.'''   )

   # Get only text
   if images is None:
     lowercase									=self.tokenizer
     lowercase									=self.tokenizer(
         text=snake_case_  , add_special_tokens=snake_case_  , padding=snake_case_  , truncation=snake_case_  , max_length=snake_case_  , stride=snake_case_  , pad_to_multiple_of=snake_case_  , return_attention_mask=snake_case_  , return_overflowing_tokens=snake_case_  , return_special_tokens_mask=snake_case_  , return_offsets_mapping=snake_case_  , return_token_type_ids=snake_case_  , return_length=snake_case_  , verbose=snake_case_  , return_tensors=snake_case_  , **snake_case_  , )
     return text_encoding

   # add pixel_values
   lowercase									=self.image_processor(snake_case_  , return_tensors=snake_case_   )

   if text is not None:
     lowercase									=self.tokenizer(
         text=snake_case_  , add_special_tokens=snake_case_  , padding=snake_case_  , truncation=snake_case_  , max_length=snake_case_  , stride=snake_case_  , pad_to_multiple_of=snake_case_  , return_attention_mask=snake_case_  , return_overflowing_tokens=snake_case_  , return_special_tokens_mask=snake_case_  , return_offsets_mapping=snake_case_  , return_token_type_ids=snake_case_  , return_length=snake_case_  , verbose=snake_case_  , return_tensors=snake_case_  , **snake_case_  , )
   else:
     lowercase									=None

   if text_encoding is not None:
     encoding_image_processor.update(snake_case_   )

   return encoding_image_processor







 def 						_A(       self  , *snake_case_  , **snake_case_   ):
   return self.tokenizer.batch_decode(*snake_case_  , **snake_case_   )







 def 						_A(       self  , *snake_case_  , **snake_case_   ):
   return self.tokenizer.decode(*snake_case_  , **snake_case_   )







 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 						_A(       self   ):
   lowercase									=self.tokenizer.model_input_names
   lowercase									=self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





"""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')





