




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def       lowerCAmelCase_      (	lowerCamelCase:							Any    )       ->      List[str]:


      '''simple docstring'''




      if "model" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('model.'  ,					''    )
      if "norm1" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('norm1'  ,					'attention.output.LayerNorm'    )
      if "norm2" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('norm2'  ,					'output.LayerNorm'    )
      if "norm" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('norm'  ,					'LayerNorm'    )
      if "transformer" in orig_key:
            lowerCamelCase__   	=					orig_key.split('.'    )[0].split('_'    )[-1]
            lowerCamelCase__   	=					orig_key.replace(f"""transformer_{layer_num}"""  ,					f"""encoder.layer.{layer_num}"""    )
      if "mha.attn" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mha.attn'  ,					'attention.self'    )
      if "mha" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mha'  ,					'attention'    )
      if "W_q" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('W_q'  ,					'self.query'    )
      if "W_k" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('W_k'  ,					'self.key'    )
      if "W_v" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('W_v'  ,					'self.value'    )
      if "ff1" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('ff1'  ,					'intermediate.dense'    )
      if "ff2" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('ff2'  ,					'output.dense'    )
      if "ff" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('ff'  ,					'output.dense'    )
      if "mlm_class" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mlm.mlm_class'  ,					'cls.predictions.decoder'    )
      if "mlm" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mlm'  ,					'cls.predictions.transform'    )
      if "cls" not in orig_key:
            lowerCamelCase__   	=					'nystromformer.' + orig_key

      return orig_key

def       lowerCAmelCase_      (	lowerCamelCase:							List[Any]  ,					lowerCamelCase:							int    )       ->      Dict:


      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__   	=					orig_state_dict.pop(lowerCamelCase    )

            if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
                  continue
            else:
                  lowerCamelCase__   	=					val

      lowerCamelCase__   	=					orig_state_dict['cls.predictions.decoder.bias']
      lowerCamelCase__   	=					(
          torch.arange(config.max_position_embeddings    ).expand((1, -1)    ) + 2
      )

      return orig_state_dict

def       lowerCAmelCase_      (	lowerCamelCase:							Dict  ,					lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							Optional[int]    )       ->      Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )['model_state_dict']
      lowerCamelCase__   	=					NystromformerConfig.from_json_file(lowerCamelCase    )
      lowerCamelCase__   	=					NystromformerForMaskedLM(lowerCamelCase    )

      lowerCamelCase__   	=					convert_checkpoint_helper(lowerCamelCase  ,					lowerCamelCase    )

      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__":
      __SCREAMING_SNAKE_CASE     : Tuple      =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for Nystromformer model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





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


__SCREAMING_SNAKE_CASE     : Dict      =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						Any  =["pixel_values"]




     def __init__(     self	: Union[str, Any]						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: bool = True						,	A__	: Union[int, float] = 1 / 2_5_5						,	A__	: bool = True						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = True						,	**A__	: Optional[int]						,	)					->							None:
           super().__init__(**A__      )
           lowerCamelCase__   	=					size if size is not None else {'shortest_edge': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__      )

           lowerCamelCase__   	=					do_resize
           lowerCamelCase__   	=					size
           lowerCamelCase__   	=					resample
           lowerCamelCase__   	=					do_center_crop
           lowerCamelCase__   	=					crop_size
           lowerCamelCase__   	=					do_rescale
           lowerCamelCase__   	=					rescale_factor
           lowerCamelCase__   	=					do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowerCamelCase__   	=					image_std if image_std is not None else OPENAI_CLIP_STD
           lowerCamelCase__   	=					do_convert_rgb




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Optional[int]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					get_resize_output_image_size(
               A__						,	size=(size['height'], size['width'])						,	default_to_square=A__      )
           return resize(A__						,	size=A__						,	resample=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__      )
           return center_crop(A__						,	size=(size['height'], size['width'])						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Dict						,	A__	: np.ndarray						,	A__	: Union[int, float]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							Tuple:
           return rescale(A__						,	scale=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Union[float, List[float]]						,	A__	: Union[float, List[float]]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Tuple						,	)					->							np.ndarray:
           return normalize(A__						,	mean=A__						,	std=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Tuple						,	A__	: ImageInput						,	A__	: bool = None						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = None						,	A__	: bool = None						,	A__	: int = None						,	A__	: bool = None						,	A__	: float = None						,	A__	: bool = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = None						,	A__	: Optional[Union[str, TensorType]] = None						,	A__	: Optional[ChannelDimension] = ChannelDimension.FIRST						,	**A__	: List[str]						,	)					->							PIL.Image.Image:
           lowerCamelCase__   	=					do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__   	=					size if size is not None else self.size
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					resample if resample is not None else self.resample
           lowerCamelCase__   	=					do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCamelCase__   	=					crop_size if crop_size is not None else self.crop_size
           lowerCamelCase__   	=					get_size_dict(A__      )
           lowerCamelCase__   	=					do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__   	=					rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__   	=					do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__   	=					image_std if image_std is not None else self.image_std
           lowerCamelCase__   	=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowerCamelCase__   	=					make_list_of_images(A__      )

           if not valid_images(A__      ):
                 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:
                 lowerCamelCase__   	=					[convert_to_rgb(A__      ) for image in images]

           # All transformations expect numpy arrays.
           lowerCamelCase__   	=					[to_numpy_array(A__      ) for image in images]

           if do_resize:
                 lowerCamelCase__   	=					[self.resize(image=A__						,	size=A__						,	resample=A__      ) for image in images]

           if do_center_crop:
                 lowerCamelCase__   	=					[self.center_crop(image=A__						,	size=A__      ) for image in images]

           if do_rescale:
                 lowerCamelCase__   	=					[self.rescale(image=A__						,	scale=A__      ) for image in images]

           if do_normalize:
                 lowerCamelCase__   	=					[self.normalize(image=A__						,	mean=A__						,	std=A__      ) for image in images]

           lowerCamelCase__   	=					[to_channel_dimension_format(A__						,	A__      ) for image in images]

           lowerCamelCase__   	=					{'pixel_values': images}
           return BatchFeature(data=A__						,	tensor_type=A__      )