
"""simple docstring"""




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		:       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')
a		:       int     						=       parser.parse_args()


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

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

# to channels last
a		:       Tuple     						=       pipe.unet.to(memory_format=torch.channels_last)
a		:       Optional[int]     						=       pipe.vae.to(memory_format=torch.channels_last)
a		:       str     						=       pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
    a		:       Tuple     						=       pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		:       Dict     						=       torch.randn(2, 4, 64, 64)
a		:       Dict     						=       torch.rand(1) * 9_99
a		:       str     						=       torch.randn(2, 77, 7_68)
a		:       str     						=       (sample, timestep, encoder_hidden_status)
try:
    a		:       Tuple     						=       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		:       List[Any]     						=       ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    a		:       List[Any]     						=       ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

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

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

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




"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


