
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__		)



"""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)

