


'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCAmelCase    :     List[str]					=						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""")
lowerCAmelCase    :     Union[str, Any]					=						parser.parse_args()


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

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

# to channels last
lowerCAmelCase    :     Optional[int]					=						pipe.unet.to(memory_format=torch.channels_last)
lowerCAmelCase    :     Dict					=						pipe.vae.to(memory_format=torch.channels_last)
lowerCAmelCase    :     Optional[Any]					=						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
       lowerCAmelCase    :     Optional[int]					=						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowerCAmelCase    :     Dict					=						torch.randn(2, 4, 64, 64)
lowerCAmelCase    :     List[Any]					=						torch.rand(1) * 9_99
lowerCAmelCase    :     str					=						torch.randn(2, 77, 7_68)
lowerCAmelCase    :     Union[str, Any]					=						(sample, timestep, encoder_hidden_status)
try:
       lowerCAmelCase    :     List[str]					=						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
       lowerCAmelCase    :     List[str]					=						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase    :     int					=						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase    :     Dict					=						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
       lowerCAmelCase    :     Optional[Any]					=						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowerCAmelCase    :     Optional[int]					=						6_66
lowerCAmelCase    :     str					=						torch.Generator(device).manual_seed(seed)
lowerCAmelCase    :     Any					=						{"""generator""": generator}
if args.steps is not None:
       lowerCAmelCase    :     List[str]					=						args.steps

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

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





'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

            return {"attention_mask": attention_mask, "input_ids": input_ids}

