
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__							(       __lowerCAmelCase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  CTRLTokenizer
	_A			=  False
	_A			=  False







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[int]:
								super().setUp()

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

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







	def      _lowerCamelCase       (      self	:Optional[int]  ,     **a	:int		)	->				Dict:
								kwargs.update(self.special_tokens_map		)
								return CTRLTokenizer.from_pretrained(self.tmpdirname  ,     **lowerCAmelCase_		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Dict		)	->				Optional[Any]:
								__UpperCamelCase			:     int         =      "adapt react readapt apt"
								__UpperCamelCase			:     List[Any]         =      "adapt react readapt apt"
								return input_text, output_text







	def      _lowerCamelCase       (      self	:Dict		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      CTRLTokenizer(self.vocab_file  ,     self.merges_file  ,     **self.special_tokens_map		)
								__UpperCamelCase			:     Union[str, Any]         =      "adapt react readapt apt"
								__UpperCamelCase			:     List[str]         =      "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
								__UpperCamelCase			:     Tuple         =      tokenizer.tokenize(lowerCAmelCase_		)
								self.assertListEqual(lowerCAmelCase_  ,     lowerCAmelCase_		)

								__UpperCamelCase			:     Tuple         =      tokens + [tokenizer.unk_token]

								__UpperCamelCase			:     int         =      [0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)  ,     lowerCAmelCase_		)

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      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')
lowercase						:      Tuple									= parser.parse_args()


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

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

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

# optimize with ipex
lowercase						:      Any									= torch.randn(2, 4, 64, 64)
lowercase						:      Union[str, Any]									= torch.rand(1) * 999
lowercase						:      List[Any]									= torch.randn(2, 77, 768)
lowercase						:      List[Any]									= (sample, timestep, encoder_hidden_status)
try:
		lowercase						:      List[Any]									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		lowercase						:      str									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      Dict									= ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      str									= ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowercase						:      Dict									= ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase						:      Any									= 666
lowercase						:      int									= torch.Generator(device).manual_seed(seed)
lowercase						:      List[str]									= {'generator': generator}
if args.steps is not None:
		lowercase						:      List[str]									= args.steps

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

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