






'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase__			=      False


class 							_UpperCAmelCase    (		unittest.TestCase      ):
    pass


@slow
@require_torch_gpu
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     Optional[int]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
       lowercase_						:     List[Any]							     =  torch.manual_seed(0      )
       lowercase_						:     Optional[int]							     =  pipe(
           image=a	,  generator=a	,  guidance_scale=7.5	,  num_inference_steps=5_0	,  output_type="numpy"	,  ).images

       lowercase_						:     Tuple							     =  image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       lowercase_						:     Any							     =  np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2












'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__			=      ['small', 'medium', 'large']

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument('--dialogpt_path', default='.', type=str)
 UpperCamelCase__			=      parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  UpperCamelCase__			=      os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
  UpperCamelCase__			=      f"""./DialoGPT-{MODEL}"""
  convert_dialogpt_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
  )




