





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 	__lowerCAmelCase      (       _UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    Union[str, Any]  		=					CTRLTokenizer
   _UpperCamelCase			:    Union[str, Any]  		=					False
   _UpperCamelCase			:    Tuple  		=					False





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a__						:    Optional[int]   		=	["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
      a__						:    Tuple   		=	dict(zip(snake_case       ,      range(len(snake_case   )   )   )   )
      a__						:    List[Any]   		=	["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
      a__						:    int   		=	{"unk_token": "<unk>"}

      a__						:    Union[str, Any]   		=	os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["vocab_file"]   )
      a__						:    str   		=	os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["merges_file"]   )
      with open(self.vocab_file       ,      "w"       ,      encoding="utf-8"   ) as fp:
         fp.write(json.dumps(snake_case   ) + "\n"   )
      with open(self.merges_file       ,      "w"       ,      encoding="utf-8"   ) as fp:
         fp.write("\n".join(snake_case   )   )





   def 						_snake_case			(			self       ,      **snake_case   )					->	List[Any]:
      """simple docstring"""





      kwargs.update(self.special_tokens_map   )
      return CTRLTokenizer.from_pretrained(self.tmpdirname       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	"adapt react readapt apt"
      a__						:    int   		=	"adapt react readapt apt"
      return input_text, output_text





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    str   		=	CTRLTokenizer(self.vocab_file       ,      self.merges_file       ,      **self.special_tokens_map   )
      a__						:    Any   		=	"adapt react readapt apt"
      a__						:    Union[str, Any]   		=	"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
      a__						:    int   		=	tokenizer.tokenize(snake_case   )
      self.assertListEqual(snake_case       ,      snake_case   )

      a__						:    Optional[Any]   		=	tokens + [tokenizer.unk_token]

      a__						:    Tuple   		=	[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )       ,      snake_case   )








import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      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""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

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

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

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

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

