code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import random
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
  from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch
  
  
  enable_full_determinism()
  
  
  
  
  
  
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					3
            _UpperCAmelCase   	=					(32, 32)
  
            _UpperCAmelCase   	=					floats_tensor((batch_size, num_channels) + sizes							,rng=random.Random(0		)		).to(lowerCamelCase_		)
            return image
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					UNetaDModel(
                block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=6							,out_channels=3							,down_block_types=("DownBlock2D", "AttnDownBlock2D")							,up_block_types=("AttnUpBlock2D", "UpBlock2D")							,)
            return model
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					VQModel(
                block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,latent_channels=3							,)
            return model
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"cpu"
            _UpperCAmelCase   	=					self.dummy_uncond_unet
            _UpperCAmelCase   	=					DDIMScheduler()
            _UpperCAmelCase   	=					self.dummy_vq_model
  
            _UpperCAmelCase   	=					LDMSuperResolutionPipeline(unet=lowerCamelCase_							,vqvae=lowerCamelCase_							,scheduler=lowerCamelCase_		)
            ldm.to(lowerCamelCase_		)
            ldm.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.dummy_image.to(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					torch.Generator(device=lowerCamelCase_		).manual_seed(0		)
            _UpperCAmelCase   	=					ldm(image=lowerCamelCase_							,generator=lowerCamelCase_							,num_inference_steps=2							,output_type="numpy"		).images
  
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 64, 64, 3)
            _UpperCAmelCase   	=					np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
  
  
  
  
  
         @unittest.skipIf(torch_device != "cuda"							,"This test requires a GPU"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.dummy_uncond_unet
            _UpperCAmelCase   	=					DDIMScheduler()
            _UpperCAmelCase   	=					self.dummy_vq_model
  
            # put models in fp16
            _UpperCAmelCase   	=					unet.half()
            _UpperCAmelCase   	=					vqvae.half()
  
            _UpperCAmelCase   	=					LDMSuperResolutionPipeline(unet=lowerCamelCase_							,vqvae=lowerCamelCase_							,scheduler=lowerCamelCase_		)
            ldm.to(lowerCamelCase_		)
            ldm.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.dummy_image.to(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					ldm(lowerCamelCase_							,num_inference_steps=2							,output_type="numpy"		).images
  
            assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  
  @slow
  @require_torch
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                "/vq_diffusion/teddy_bear_pool.png"		)
            _UpperCAmelCase   	=					init_image.resize((64, 64)							,resample=PIL_INTERPOLATION["lanczos"]		)
  
            _UpperCAmelCase   	=					LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution"							,device_map="auto"		)
            ldm.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					torch.manual_seed(0		)
            _UpperCAmelCase   	=					ldm(image=lowerCamelCase_							,generator=lowerCamelCase_							,num_inference_steps=20							,output_type="numpy"		).images
  
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 256, 256, 3)
            _UpperCAmelCase   	=					np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
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)
  
  