


import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


a					:				Any       =      get_logger(__name__)






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->Dict:


 '''simple docstring'''
 if text.startswith(__lowerCamelCase							):
  return text[len(__lowerCamelCase							) :]
 return text  # or whatever






def  _UpperCAmelCase						( __lowerCamelCase							)		->Optional[Any]:


 '''simple docstring'''
 return {remove_prefix(__lowerCamelCase			,  '''model.'''							): v for k, v in sd.items()}






def  _UpperCAmelCase						( __lowerCamelCase							)		->Union[str, Any]:


 '''simple docstring'''
 snake_case :			Tuple							=   {}
 for k in state_dicts[0].keys():
  snake_case :			Optional[Any]							=   [sd[k] for sd in state_dicts]
  snake_case :			List[str]							=   sum(__lowerCamelCase							) / len(__lowerCamelCase							)
  assert isinstance(__lowerCamelCase			,  torch.Tensor							)
  snake_case :			Optional[Any]							=   new_t
 return new_sd






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->None:


 '''simple docstring'''
 snake_case :			Optional[Any]							=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)
 if os.path.isfile(__lowerCamelCase							):
  snake_case :			List[Any]							=   [pl_ckpt_path]
 else:
  assert os.path.isdir(__lowerCamelCase							)
  snake_case :			List[Any]							=   list(Path(__lowerCamelCase							).glob('''*.ckpt'''							)							)
  assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

 if len(__lowerCamelCase							) > 1:
  logger.info(f'''averaging the weights of {ckpt_files}'''							)

 snake_case :			str							=   [sanitize(torch.load(__lowerCamelCase			,  map_location='''cpu'''							)['''state_dict''']							) for x in ckpt_files]
 snake_case :			List[Any]							=   average_state_dicts(__lowerCamelCase							)

 snake_case	,       snake_case :			List[Any]							=   hf_model.load_state_dict(__lowerCamelCase			,  strict=__lowerCamelCase							)
 assert not missing, f'''missing keys: {missing}'''
 hf_model.save_pretrained(__lowerCamelCase							)
 try:
  snake_case :			Union[str, Any]							=   AutoTokenizer.from_pretrained(__lowerCamelCase							)
  tok.save_pretrained(__lowerCamelCase							)
 except Exception:
  pass
  # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)










import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a					:				Optional[int]       =      False

@nightly
@require_torch_gpu
class   lowerCamelCase						(	unittest.TestCase  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

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






