




import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def  SCREAMING_SNAKE_CASE							(       )      ->					int:
     a     =				argparse.ArgumentParser()
     parser.add_argument(
         "-m"		, "--pretrained_model_name_or_path"		, type=__UpperCamelCase		, default=__UpperCamelCase		, required=__UpperCamelCase		, help="Path to pretrained model or model identifier from huggingface.co/models."		, )
     parser.add_argument(
         "-c"		, "--caption"		, type=__UpperCamelCase		, default="robotic cat with wings"		, help="Text used to generate images."		, )
     parser.add_argument(
         "-n"		, "--images_num"		, type=__UpperCamelCase		, default=4		, help="How much images to generate."		, )
     parser.add_argument(
         "-s"		, "--seed"		, type=__UpperCamelCase		, default=42		, help="Seed for random process."		, )
     parser.add_argument(
         "-ci"		, "--cuda_id"		, type=__UpperCamelCase		, default=0		, help="cuda_id."		, )
     a     =				parser.parse_args()
     return args

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Dict:
     if not len(__UpperCamelCase) == rows * cols:
          raise ValueError("The specified number of rows and columns are not correct.")

     a  ,				a     =				imgs[0].size
     a     =				Image.new("RGB"		, size=(cols * w, rows * h))
     a  ,				a     =				grid.size

     for i, img in enumerate(__UpperCamelCase):
          grid.paste(__UpperCamelCase		, box=(i % cols * w, i // cols * h))
     return grid

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase="robotic cat with wings"		, __UpperCamelCase=7.5		, __UpperCamelCase=50		, __UpperCamelCase=1		, __UpperCamelCase=42		, )      ->					List[Any]:
     a     =				torch.Generator(pipeline.device).manual_seed(__UpperCamelCase)
     a     =				pipeline(
         __UpperCamelCase		, guidance_scale=__UpperCamelCase		, num_inference_steps=__UpperCamelCase		, generator=__UpperCamelCase		, num_images_per_prompt=__UpperCamelCase		, ).images
     a     =				int(math.sqrt(__UpperCamelCase))
     a     =				image_grid(__UpperCamelCase		, rows=_rows		, cols=num_images_per_prompt // _rows)
     return grid, images


lowercase__				:      List[str]	   = parse_args()
# Load models and create wrapper for stable diffusion
lowercase__				:      Tuple	   = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
lowercase__				:      List[str]	   = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
lowercase__				:      Optional[int]	   = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
lowercase__				:      Optional[Any]	   = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

lowercase__				:      Tuple	   = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase__				:      List[Any]	   = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
   lowercase__				:      int	   = load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, "unet", unet)
else:
   lowercase__				:      Union[str, Any]	   = unet.to(torch.device("cuda", args.cuda_id))
lowercase__				:      List[str]	   = pipeline.to(unet.device)
lowercase__   ,    lowercase__				:      List[Any]	   = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
lowercase__				:      Optional[Any]	   = os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
   image.save(os.path.join(dirname, "{}.png".format(idx + 1)))







import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(UpperCamelCase__				)       , """Tatoeba directory does not exist."""				)
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          return TatoebaConverter(save_dir=A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.resolver.convert_models(["heb-eng"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				self.resolver.write_model_card("opus-mt-he-en"  ,		dry_run=A			)
          assert mmeta["long_pair"] == "heb-eng"

