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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCAmelCase    :  Optional[Any]										= logging.get_logger(__name__)


if is_vision_available():
    import PIL
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					['pixel_values']






      def __init__(      self  ,		__lowerCamelCase = True  ,		__lowerCamelCase = None  ,		__lowerCamelCase = PILImageResampling.BICUBIC  ,		__lowerCamelCase = True  ,		__lowerCamelCase = None  ,		__lowerCamelCase = True  ,		__lowerCamelCase = 1 / 2_55  ,		__lowerCamelCase = True  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = True  ,		**__lowerCamelCase  ,		)     ->				None:


          '''simple docstring'''







          super().__init__(**__lowerCamelCase						)
          snake_case:						str         =   size if size is not None else {"""shortest_edge""": 2_24}
          snake_case:						List[Any]         =   get_size_dict(__lowerCamelCase  ,		default_to_square=__lowerCamelCase						)
          snake_case:						Any         =   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
          snake_case:						Optional[Any]         =   get_size_dict(__lowerCamelCase						)

          snake_case:						List[Any]         =   do_resize
          snake_case:						List[Any]         =   size
          snake_case:						str         =   resample
          snake_case:						Optional[Any]         =   do_center_crop
          snake_case:						Dict         =   crop_size
          snake_case:						Optional[int]         =   do_rescale
          snake_case:						Any         =   rescale_factor
          snake_case:						Union[str, Any]         =   do_normalize
          snake_case:						Union[str, Any]         =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          snake_case:						Dict         =   image_std if image_std is not None else OPENAI_CLIP_STD
          snake_case:						str         =   do_convert_rgb






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = PILImageResampling.BICUBIC  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				np.ndarray:


          '''simple docstring'''







          snake_case:						Optional[int]         =   get_size_dict(__lowerCamelCase  ,		default_to_square=__lowerCamelCase						)
          snake_case:						Any         =   get_resize_output_image_size(
              __lowerCamelCase  ,		size=(size["""height"""], size["""width"""])  ,		default_to_square=__lowerCamelCase						)
          return resize(__lowerCamelCase  ,		size=__lowerCamelCase  ,		resample=__lowerCamelCase  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				np.ndarray:


          '''simple docstring'''







          snake_case:						int         =   get_size_dict(__lowerCamelCase						)
          return center_crop(__lowerCamelCase  ,		size=(size["""height"""], size["""width"""])  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				str:


          '''simple docstring'''







          return rescale(__lowerCamelCase  ,		scale=__lowerCamelCase  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				np.ndarray:


          '''simple docstring'''







          return normalize(__lowerCamelCase  ,		mean=__lowerCamelCase  ,		std=__lowerCamelCase  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = ChannelDimension.FIRST  ,		**__lowerCamelCase  ,		)     ->				PIL.Image.Image:


          '''simple docstring'''







          snake_case:						int         =   do_resize if do_resize is not None else self.do_resize
          snake_case:						int         =   size if size is not None else self.size
          snake_case:						Optional[Any]         =   get_size_dict(__lowerCamelCase  ,		default_to_square=__lowerCamelCase						)
          snake_case:						Tuple         =   resample if resample is not None else self.resample
          snake_case:						str         =   do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case:						List[Any]         =   crop_size if crop_size is not None else self.crop_size
          snake_case:						Dict         =   get_size_dict(__lowerCamelCase						)
          snake_case:						Optional[int]         =   do_rescale if do_rescale is not None else self.do_rescale
          snake_case:						Dict         =   rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case:						Tuple         =   do_normalize if do_normalize is not None else self.do_normalize
          snake_case:						Optional[int]         =   image_mean if image_mean is not None else self.image_mean
          snake_case:						List[Any]         =   image_std if image_std is not None else self.image_std
          snake_case:						Any         =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          snake_case:						List[Any]         =   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_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

          if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale 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."""						)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
              snake_case:						Tuple         =   [convert_to_rgb(__lowerCamelCase						) for image in images]

          # All transformations expect numpy arrays.
          snake_case:						Optional[Any]         =   [to_numpy_array(__lowerCamelCase						) for image in images]

          if do_resize:
              snake_case:						Optional[int]         =   [self.resize(image=__lowerCamelCase  ,		size=__lowerCamelCase  ,		resample=__lowerCamelCase						) for image in images]

          if do_center_crop:
              snake_case:						Optional[Any]         =   [self.center_crop(image=__lowerCamelCase  ,		size=__lowerCamelCase						) for image in images]

          if do_rescale:
              snake_case:						Any         =   [self.rescale(image=__lowerCamelCase  ,		scale=__lowerCamelCase						) for image in images]

          if do_normalize:
              snake_case:						Union[str, Any]         =   [self.normalize(image=__lowerCamelCase  ,		mean=__lowerCamelCase  ,		std=__lowerCamelCase						) for image in images]

          snake_case:						str         =   [to_channel_dimension_format(__lowerCamelCase  ,		__lowerCamelCase						) for image in images]

          snake_case:						Any         =   {"""pixel_values""": images}
          return BatchFeature(data=__lowerCamelCase  ,		tensor_type=__lowerCamelCase						)



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def   a_					(_lowerCAmelCase  :   Tuple       )->				List[Any]:
    if "model" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""model."""			,	""""""       )
    if "norm1" in orig_key:
        snake_case:						int         =   orig_key.replace("""norm1"""			,	"""attention.output.LayerNorm"""       )
    if "norm2" in orig_key:
        snake_case:						Tuple         =   orig_key.replace("""norm2"""			,	"""output.LayerNorm"""       )
    if "norm" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""norm"""			,	"""LayerNorm"""       )
    if "transformer" in orig_key:
        snake_case:						Any         =   orig_key.split("""."""       )[0].split("""_"""       )[-1]
        snake_case:						List[Any]         =   orig_key.replace(F"transformer_{layer_num}"			,	F"encoder.layer.{layer_num}"       )
    if "mha.attn" in orig_key:
        snake_case:						List[Any]         =   orig_key.replace("""mha.attn"""			,	"""attention.self"""       )
    if "mha" in orig_key:
        snake_case:						str         =   orig_key.replace("""mha"""			,	"""attention"""       )
    if "W_q" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_q"""			,	"""self.query"""       )
    if "W_k" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_k"""			,	"""self.key"""       )
    if "W_v" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_v"""			,	"""self.value"""       )
    if "ff.0" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""ff.0"""			,	"""intermediate.dense"""       )
    if "ff.2" in orig_key:
        snake_case:						Optional[Any]         =   orig_key.replace("""ff.2"""			,	"""output.dense"""       )
    if "ff" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""ff"""			,	"""output.dense"""       )
    if "mlm_class" in orig_key:
        snake_case:						int         =   orig_key.replace("""mlm.mlm_class"""			,	"""cls.predictions.decoder"""       )
    if "mlm" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""mlm"""			,	"""cls.predictions.transform"""       )
    if "backbone.backbone.encoders" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""backbone.backbone.encoders"""			,	"""encoder.layer"""       )
    if "cls" not in orig_key:
        snake_case:						Dict         =   """mra.""" + orig_key

    return orig_key

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Any       )->				Dict:
    for key in orig_state_dict.copy().keys():
        snake_case:						Tuple         =   orig_state_dict.pop(_lowerCAmelCase       )

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

    snake_case:						str         =   orig_state_dict["""cls.predictions.decoder.bias"""]
    snake_case:						List[str]         =   torch.arange(_lowerCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   Any       )->				List[Any]:
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )["""model_state_dict"""]
    snake_case:						Optional[int]         =   MraConfig.from_json_file(_lowerCAmelCase       )
    snake_case:						List[str]         =   MraForMaskedLM(_lowerCAmelCase       )

    snake_case:						Any         =   convert_checkpoint_helper(config.max_position_embeddings			,	_lowerCAmelCase       )

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

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


if __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, Any]										= 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.'
    )
    __lowerCAmelCase    :  Dict										= parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


