code: 
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)
  
  
  if is_vision_available():
    import PIL
  
  
  
  
  
  
  class     __a			(					UpperCamelCase__			):
         _lowerCamelCase							:							Dict       =			["""pixel_values"""]
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_ = True							,lowerCamelCase_ = None							,lowerCamelCase_ = PILImageResampling.BILINEAR							,lowerCamelCase_ = True							,lowerCamelCase_ = False							,lowerCamelCase_ = True							,lowerCamelCase_ = True							,lowerCamelCase_ = 1 / 255							,lowerCamelCase_ = True							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            super().__init__(**lowerCAmelCase__		)
  
            _UpperCAmelCase   	=					size if size is not None else {"height": 2560, "width": 1920}
            if isinstance(lowerCAmelCase__							,(tuple, list)		):
               # The previous feature extractor size parameter was in (width, height) format
               _UpperCAmelCase   	=					size[::-1]
            _UpperCAmelCase   	=					get_size_dict(lowerCAmelCase__		)
  
            _UpperCAmelCase   	=					do_resize
            _UpperCAmelCase   	=					size
            _UpperCAmelCase   	=					resample
            _UpperCAmelCase   	=					do_thumbnail
            _UpperCAmelCase   	=					do_align_long_axis
            _UpperCAmelCase   	=					do_pad
            _UpperCAmelCase   	=					do_rescale
            _UpperCAmelCase   	=					rescale_factor
            _UpperCAmelCase   	=					do_normalize
            _UpperCAmelCase   	=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _UpperCAmelCase   	=					image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = None		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					get_image_size(lowerCAmelCase__		)
            _UpperCAmelCase   	=					size["height"], size["width"]
  
            if (output_width < output_height and input_width > input_height) or (
                output_width > output_height and input_width < input_height
            ):
               _UpperCAmelCase   	=					np.rotaa(lowerCAmelCase__							,3		)
  
            if data_format is not None:
               _UpperCAmelCase   	=					to_channel_dimension_format(lowerCAmelCase__							,lowerCAmelCase__		)
  
            return image
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = False							,lowerCamelCase_ = None							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					size["height"], size["width"]
            _UpperCAmelCase   	=					get_image_size(lowerCAmelCase__		)
  
            _UpperCAmelCase   	=					output_width - input_width
            _UpperCAmelCase   	=					output_height - input_height
  
            if random_padding:
               _UpperCAmelCase   	=					np.random.randint(low=0							,high=delta_height + 1		)
               _UpperCAmelCase   	=					np.random.randint(low=0							,high=delta_width + 1		)
            else:
               _UpperCAmelCase   	=					delta_height // 2
               _UpperCAmelCase   	=					delta_width // 2
  
            _UpperCAmelCase   	=					delta_height - pad_top
            _UpperCAmelCase   	=					delta_width - pad_left
  
            _UpperCAmelCase   	=					((pad_top, pad_bottom), (pad_left, pad_right))
            return pad(lowerCAmelCase__							,lowerCAmelCase__							,data_format=lowerCAmelCase__		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,*lowerCamelCase_							,**lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."		)
            return self.pad_image(*lowerCAmelCase__							,**lowerCAmelCase__		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = PILImageResampling.BICUBIC							,lowerCamelCase_ = None							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					get_image_size(lowerCAmelCase__		)
            _UpperCAmelCase   	=					size["height"], size["width"]
  
            # We always resize to the smallest of either the input or output size.
            _UpperCAmelCase   	=					min(lowerCAmelCase__							,lowerCAmelCase__		)
            _UpperCAmelCase   	=					min(lowerCAmelCase__							,lowerCAmelCase__		)
  
            if height == input_height and width == input_width:
               return image
  
            if input_height > input_width:
               _UpperCAmelCase   	=					int(input_width * height / input_height		)
            elif input_width > input_height:
               _UpperCAmelCase   	=					int(input_height * width / input_width		)
  
            return resize(
                lowerCAmelCase__							,size=(height, width)							,resample=lowerCAmelCase__							,reducing_gap=2.0							,data_format=lowerCAmelCase__							,**lowerCAmelCase__		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = PILImageResampling.BICUBIC							,lowerCamelCase_ = None							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					get_size_dict(lowerCAmelCase__		)
            _UpperCAmelCase   	=					min(size["height"]							,size["width"]		)
            _UpperCAmelCase   	=					get_resize_output_image_size(lowerCAmelCase__							,size=lowerCAmelCase__							,default_to_square=lowerCAmelCase__		)
            _UpperCAmelCase   	=					resize(lowerCAmelCase__							,size=lowerCAmelCase__							,resample=lowerCAmelCase__							,data_format=lowerCAmelCase__							,**lowerCAmelCase__		)
            return resized_image
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = None							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            return rescale(lowerCAmelCase__							,scale=lowerCAmelCase__							,data_format=lowerCAmelCase__							,**lowerCAmelCase__		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = None							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            return normalize(lowerCAmelCase__							,mean=lowerCAmelCase__							,std=lowerCAmelCase__							,data_format=lowerCAmelCase__							,**lowerCAmelCase__		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = False							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = None							,lowerCamelCase_ = ChannelDimension.FIRST							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					do_resize if do_resize is not None else self.do_resize
            _UpperCAmelCase   	=					size if size is not None else self.size
            if isinstance(lowerCAmelCase__							,(tuple, list)		):
               # Previous feature extractor had size in (width, height) format
               _UpperCAmelCase   	=					size[::-1]
            _UpperCAmelCase   	=					get_size_dict(lowerCAmelCase__		)
            _UpperCAmelCase   	=					resample if resample is not None else self.resample
            _UpperCAmelCase   	=					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
            _UpperCAmelCase   	=					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
            _UpperCAmelCase   	=					do_pad if do_pad is not None else self.do_pad
            _UpperCAmelCase   	=					do_rescale if do_rescale is not None else self.do_rescale
            _UpperCAmelCase   	=					rescale_factor if rescale_factor is not None else self.rescale_factor
            _UpperCAmelCase   	=					do_normalize if do_normalize is not None else self.do_normalize
            _UpperCAmelCase   	=					image_mean if image_mean is not None else self.image_mean
            _UpperCAmelCase   	=					image_std if image_std is not None else self.image_std
  
            _UpperCAmelCase   	=					make_list_of_images(lowerCAmelCase__		)
  
            if not valid_images(lowerCAmelCase__		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)
  
            if do_resize and size is None:
               raise ValueError("Size must be specified if do_resize is True."		)
  
            if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."		)
  
            if do_pad and size is None:
               raise ValueError("Size must be specified if do_pad is True."		)
  
            if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)
  
            # All transformations expect numpy arrays.
            _UpperCAmelCase   	=					[to_numpy_array(lowerCAmelCase__		) for image in images]
  
            if do_align_long_axis:
               _UpperCAmelCase   	=					[self.align_long_axis(lowerCAmelCase__							,size=lowerCAmelCase__		) for image in images]
  
            if do_resize:
               _UpperCAmelCase   	=					[self.resize(image=lowerCAmelCase__							,size=lowerCAmelCase__							,resample=lowerCAmelCase__		) for image in images]
  
            if do_thumbnail:
               _UpperCAmelCase   	=					[self.thumbnail(image=lowerCAmelCase__							,size=lowerCAmelCase__		) for image in images]
  
            if do_pad:
               _UpperCAmelCase   	=					[self.pad_image(image=lowerCAmelCase__							,size=lowerCAmelCase__							,random_padding=lowerCAmelCase__		) for image in images]
  
            if do_rescale:
               _UpperCAmelCase   	=					[self.rescale(image=lowerCAmelCase__							,scale=lowerCAmelCase__		) for image in images]
  
            if do_normalize:
               _UpperCAmelCase   	=					[self.normalize(image=lowerCAmelCase__							,mean=lowerCAmelCase__							,std=lowerCAmelCase__		) for image in images]
  
            _UpperCAmelCase   	=					[to_channel_dimension_format(lowerCAmelCase__							,lowerCAmelCase__		) for image in images]
  
            _UpperCAmelCase   	=					{"pixel_values": images}
            return BatchFeature(data=lowerCAmelCase__							,tensor_type=lowerCAmelCase__		)
  
  
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)
  
  