code: 
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  
  @skip_mps
  class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
         _lowerCamelCase							:							List[Any]       =			StableDiffusionModelEditingPipeline
         _lowerCamelCase							:							Union[str, Any]       =			TEXT_TO_IMAGE_PARAMS
         _lowerCamelCase							:							List[Any]       =			TEXT_TO_IMAGE_BATCH_PARAMS
         _lowerCamelCase							:							List[str]       =			TEXT_TO_IMAGE_IMAGE_PARAMS
         _lowerCamelCase							:							Tuple       =			TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					UNetaDConditionModel(
                block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=4							,out_channels=4							,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,cross_attention_dim=32							,)
            _UpperCAmelCase   	=					DDIMScheduler()
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					AutoencoderKL(
                block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,latent_channels=4							,)
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					CLIPTextConfig(
                bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1e-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=1000							,)
            _UpperCAmelCase   	=					CLIPTextModel(lowerCamelCase_		)
            _UpperCAmelCase   	=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
  
            _UpperCAmelCase   	=					{
                "unet": unet,
                "scheduler": scheduler,
                "vae": vae,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "safety_checker": None,
                "feature_extractor": None,
            }
            return components
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_=0		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					torch.manual_seed(lowerCamelCase_		)
            _UpperCAmelCase   	=					{
                "prompt": "A field of roses",
                "generator": generator,
                # Setting height and width to None to prevent OOMs on CPU.
                "height": None,
                "width": None,
                "num_inference_steps": 2,
                "guidance_scale": 6.0,
                "output_type": "numpy",
            }
            return inputs
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"cpu"  # ensure determinism for the device-dependent torch.Generator
            _UpperCAmelCase   	=					self.get_dummy_components()
            _UpperCAmelCase   	=					StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
            _UpperCAmelCase   	=					sd_pipe.to(lowerCamelCase_		)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.get_dummy_inputs(lowerCamelCase_		)
            _UpperCAmelCase   	=					sd_pipe(**lowerCamelCase_		).images
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
            assert image.shape == (1, 64, 64, 3)
  
            _UpperCAmelCase   	=					np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"cpu"  # ensure determinism for the device-dependent torch.Generator
            _UpperCAmelCase   	=					self.get_dummy_components()
            _UpperCAmelCase   	=					StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
            _UpperCAmelCase   	=					sd_pipe.to(lowerCamelCase_		)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.get_dummy_inputs(lowerCamelCase_		)
            _UpperCAmelCase   	=					"french fries"
            _UpperCAmelCase   	=					sd_pipe(**lowerCamelCase_							,negative_prompt=lowerCamelCase_		)
            _UpperCAmelCase   	=					output.images
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 64, 64, 3)
  
            _UpperCAmelCase   	=					np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"cpu"  # ensure determinism for the device-dependent torch.Generator
            _UpperCAmelCase   	=					self.get_dummy_components()
            _UpperCAmelCase   	=					EulerAncestralDiscreteScheduler(
                beta_start=0.0_0_0_8_5							,beta_end=0.0_1_2							,beta_schedule="scaled_linear"		)
            _UpperCAmelCase   	=					StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
            _UpperCAmelCase   	=					sd_pipe.to(lowerCamelCase_		)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.get_dummy_inputs(lowerCamelCase_		)
            _UpperCAmelCase   	=					sd_pipe(**lowerCamelCase_		).images
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 64, 64, 3)
  
            _UpperCAmelCase   	=					np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"cpu"  # ensure determinism for the device-dependent torch.Generator
            _UpperCAmelCase   	=					self.get_dummy_components()
            _UpperCAmelCase   	=					PNDMScheduler()
            _UpperCAmelCase   	=					StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
            _UpperCAmelCase   	=					sd_pipe.to(lowerCamelCase_		)
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.get_dummy_inputs(lowerCamelCase_		)
            # the pipeline does not expect pndm so test if it raises error.
            with self.assertRaises(lowerCamelCase_		):
               _UpperCAmelCase   	=					sd_pipe(**lowerCamelCase_		).images
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            super().test_inference_batch_single_identical(expected_max_diff=5e-3		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            super().test_attention_slicing_forward_pass(expected_max_diff=5e-3		)
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=0		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					torch.manual_seed(lowerCamelCase_		)
            _UpperCAmelCase   	=					{
                "prompt": "A field of roses",
                "generator": generator,
                "num_inference_steps": 3,
                "guidance_scale": 7.5,
                "output_type": "numpy",
            }
            return inputs
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"CompVis/stable-diffusion-v1-4"
            _UpperCAmelCase   	=					StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_							,safety_checker=lowerCamelCase_		)
            pipe.to(lowerCamelCase_		)
            pipe.set_progress_bar_config(disable=lowerCamelCase_		)
            pipe.enable_attention_slicing()
  
            _UpperCAmelCase   	=					self.get_inputs()
            _UpperCAmelCase   	=					pipe(**lowerCamelCase_		).images
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1].flatten()
  
            assert image.shape == (1, 512, 512, 3)
  
            _UpperCAmelCase   	=					np.array(
                [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]		)
  
            assert np.abs(expected_slice - image_slice		).max() < 1e-2
  
            # make sure image changes after editing
            pipe.edit_model("A pack of roses"							,"A pack of blue roses"		)
  
            _UpperCAmelCase   	=					pipe(**lowerCamelCase_		).images
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1].flatten()
  
            assert image.shape == (1, 512, 512, 3)
  
            assert np.abs(expected_slice - image_slice		).max() > 1e-1
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()
  
            _UpperCAmelCase   	=					"CompVis/stable-diffusion-v1-4"
            _UpperCAmelCase   	=					DDIMScheduler.from_pretrained(lowerCamelCase_							,subfolder="scheduler"		)
            _UpperCAmelCase   	=					StableDiffusionModelEditingPipeline.from_pretrained(
                lowerCamelCase_							,scheduler=lowerCamelCase_							,safety_checker=lowerCamelCase_		)
            _UpperCAmelCase   	=					pipe.to(lowerCamelCase_		)
            pipe.set_progress_bar_config(disable=lowerCamelCase_		)
            pipe.enable_attention_slicing(1		)
            pipe.enable_sequential_cpu_offload()
  
            _UpperCAmelCase   	=					self.get_inputs()
            _UpperCAmelCase   	=					pipe(**lowerCamelCase_		)
  
            _UpperCAmelCase   	=					torch.cuda.max_memory_allocated()
            # make sure that less than 4.4 GB is allocated
            assert mem_bytes < 4.4 * 10**9
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
     if "model" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
     if "norm1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
     if "norm2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
     if "norm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
        _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
     if "mha" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
     if "W_q" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
     if "W_k" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
     if "W_v" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
     if "ff.0" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
     if "ff.2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
     if "ff" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
     if "mlm_class" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
     if "mlm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
     if "backbone.backbone.encoders" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
     if "cls" not in orig_key:
        _UpperCAmelCase   	=					"mra." + orig_key
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
     for key in orig_state_dict.copy().keys():
        _UpperCAmelCase   	=					orig_state_dict.pop(a_				)
  
        if ("pooler" in key) or ("sen_class" in key):
           continue
        else:
           _UpperCAmelCase   	=					val
  
     _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
     _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2
  
     return orig_state_dict
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
     _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
     _UpperCAmelCase   	=					MraForMaskedLM(a_				)
  
     _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)
  
     print(model.load_state_dict(a_				)				)
     model.eval()
     model.save_pretrained(a_				)
  
     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Mra model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  