



import gc
import tempfile
import unittest

import numpy as np
import torch

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


a_      :      int    =     False
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



 pass





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

 """simple docstring"""



 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  a__												= VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		,				torch_dtype=torch.floataa      )
  pipe.to(SCREAMING_SNAKE_CASE      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= load_image(
      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )

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

  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(SCREAMING_SNAKE_CASE      )
   a__												= VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE		,				torch_dtype=torch.floataa      )
  pipe.to(SCREAMING_SNAKE_CASE      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= generator.manual_seed(0      )
  a__												= pipe.dual_guided(
      prompt='''first prompt'''		,				image=SCREAMING_SNAKE_CASE		,				text_to_image_strength=0.75		,				generator=SCREAMING_SNAKE_CASE		,				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      ) ->			Any:
  a__												= VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		,				torch_dtype=torch.floataa      )
  pipe.to(SCREAMING_SNAKE_CASE      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= '''cyberpunk 2077'''
  a__												= load_image(
      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
  a__												= torch.manual_seed(0      )
  a__												= pipe.dual_guided(
      prompt=SCREAMING_SNAKE_CASE		,				image=SCREAMING_SNAKE_CASE		,				text_to_image_strength=0.75		,				generator=SCREAMING_SNAKE_CASE		,				guidance_scale=7.5		,				num_inference_steps=5_0		,				output_type='''numpy'''		,				).images

  a__												= 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)
  a__												= np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]      )

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

  a__												= '''A painting of a squirrel eating a burger '''
  a__												= torch.manual_seed(0      )
  a__												= pipe.text_to_image(
      prompt=SCREAMING_SNAKE_CASE		,				generator=SCREAMING_SNAKE_CASE		,				guidance_scale=7.5		,				num_inference_steps=5_0		,				output_type='''numpy'''      ).images

  a__												= 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)
  a__												= np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]      )

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

  a__												= pipe.image_variation(SCREAMING_SNAKE_CASE		,				generator=SCREAMING_SNAKE_CASE		,				output_type='''numpy'''      ).images

  a__												= 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)
  a__												= np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]      )

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










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def     __a					(		__UpperCAmelCase       ):
 if "model" in orig_key:
  a__												= orig_key.replace('''model.'''							,  ''''''       )
 if "norm1" in orig_key:
  a__												= orig_key.replace('''norm1'''							,  '''attention.output.LayerNorm'''       )
 if "norm2" in orig_key:
  a__												= orig_key.replace('''norm2'''							,  '''output.LayerNorm'''       )
 if "norm" in orig_key:
  a__												= orig_key.replace('''norm'''							,  '''LayerNorm'''       )
 if "transformer" in orig_key:
  a__												= orig_key.split('''.'''       )[0].split('''_'''       )[-1]
  a__												= orig_key.replace(f"transformer_{layer_num}"							,  f"encoder.layer.{layer_num}"       )
 if "mha.attn" in orig_key:
  a__												= orig_key.replace('''mha.attn'''							,  '''attention.self'''       )
 if "mha" in orig_key:
  a__												= orig_key.replace('''mha'''							,  '''attention'''       )
 if "W_q" in orig_key:
  a__												= orig_key.replace('''W_q'''							,  '''self.query'''       )
 if "W_k" in orig_key:
  a__												= orig_key.replace('''W_k'''							,  '''self.key'''       )
 if "W_v" in orig_key:
  a__												= orig_key.replace('''W_v'''							,  '''self.value'''       )
 if "ff1" in orig_key:
  a__												= orig_key.replace('''ff1'''							,  '''intermediate.dense'''       )
 if "ff2" in orig_key:
  a__												= orig_key.replace('''ff2'''							,  '''output.dense'''       )
 if "ff" in orig_key:
  a__												= orig_key.replace('''ff'''							,  '''output.dense'''       )
 if "mlm_class" in orig_key:
  a__												= orig_key.replace('''mlm.mlm_class'''							,  '''cls.predictions.decoder'''       )
 if "mlm" in orig_key:
  a__												= orig_key.replace('''mlm'''							,  '''cls.predictions.transform'''       )
 if "cls" not in orig_key:
  a__												= '''yoso.''' + orig_key

 return orig_key






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 for key in orig_state_dict.copy().keys():
  a__												= orig_state_dict.pop(__UpperCAmelCase       )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   a__												= val

 a__												= orig_state_dict['''cls.predictions.decoder.bias''']
 a__												= torch.arange(__UpperCAmelCase       ).expand((1, -1)       ) + 2

 return orig_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )['''model_state_dict''']
 a__												= YosoConfig.from_json_file(__UpperCAmelCase       )
 a__												= YosoForMaskedLM(__UpperCAmelCase       )

 a__												= convert_checkpoint_helper(config.max_position_embeddings							,  __UpperCAmelCase       )

 print(model.load_state_dict(__UpperCAmelCase       )       )
 model.eval()
 model.save_pretrained(__UpperCAmelCase       )

 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
     a_      :      int    =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_      :      Any    =     parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





