


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      int     		=		["image_processor", "tokenizer"]
   __A :      Optional[int]     		=		"Pix2StructImageProcessor"
   __A :      Union[str, Any]     		=		("T5Tokenizer", "T5TokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					int    =				False
     super().__init__(__A						,				__A )






   def __call__(		self						,				__A=None						,				__A = None						,				__A = True						,				__A = False						,				__A = None						,				__A = None						,				__A = 2048						,				__A = 0						,				__A = None						,				__A = None						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     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 and not self.image_processor.is_vqa:
       lowerCamelCase :					str    =				self.tokenizer
       lowerCamelCase :					List[str]    =				self.tokenizer(
           text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_token_type_ids=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
       return text_encoding

     if not self.image_processor.is_vqa:
       # add pixel_values
       lowerCamelCase :					List[str]    =				self.image_processor(
           __A						,				return_tensors=__A						,				max_patches=__A						,				**__A )
     else:
       # add pixel_values and bbox
       lowerCamelCase :					int    =				self.image_processor(
           __A						,				return_tensors=__A						,				max_patches=__A						,				header_text=__A						,				**__A )

     if text is not None and not self.image_processor.is_vqa:
       lowerCamelCase :					Tuple    =				self.tokenizer(
           text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_token_type_ids=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)

       if "attention_mask" in text_encoding:
         lowerCamelCase :					int    =				text_encoding.pop("attention_mask" )
       if "input_ids" in text_encoding:
         lowerCamelCase :					Optional[Any]    =				text_encoding.pop("input_ids" )
     else:
       lowerCamelCase :					Dict    =				None

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

     return encoding_image_processor






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,				**__A )






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,				**__A )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.tokenizer.model_input_names
     lowerCamelCase :					Union[str, Any]    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







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''')



