




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        __lowerCAmelCase					(			tf.keras.layers.Layer					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None				)       -> Union[str, Any]:



							'''simple docstring'''
							super().__init__()
							__lowerCamelCase									=       pad_token_id
							__lowerCamelCase									=       max_length
							__lowerCamelCase									=       vocab
							__lowerCamelCase									=       merges
							__lowerCamelCase									=       BytePairTokenizer(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		sequence_length=_SCREAMING_SNAKE_CASE				)


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       [' '.join(_SCREAMING_SNAKE_CASE				) for m in tokenizer.bpe_ranks.keys()]
							__lowerCamelCase									=       tokenizer.get_vocab()
							return cls(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		*_SCREAMING_SNAKE_CASE     ,		**_SCREAMING_SNAKE_CASE				)


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     ,		*_SCREAMING_SNAKE_CASE     ,		**_SCREAMING_SNAKE_CASE				)
							return cls.from_tokenizer(_SCREAMING_SNAKE_CASE     ,		*_SCREAMING_SNAKE_CASE     ,		**_SCREAMING_SNAKE_CASE				)


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							return cls(**_SCREAMING_SNAKE_CASE				)


			def  lowercase_       ( self				)       -> List[str]:



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






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       self.tf_tokenizer(_SCREAMING_SNAKE_CASE				)
							__lowerCamelCase									=       tf.ones_like(_SCREAMING_SNAKE_CASE				)

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											__lowerCamelCase									=       max_length if max_length is not None else self.max_length

											if max_length is not None:
															__lowerCamelCase									=       pad_model_inputs(
															    _SCREAMING_SNAKE_CASE     ,		max_seq_length=_SCREAMING_SNAKE_CASE     ,		pad_value=self.pad_token_id				)

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







"""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')





