





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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


__UpperCAmelCase			:  Optional[int]										=				logging.get_logger(__name__)







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	["""pixel_values"""]




       def __init__(    self					:       Union[str, Any]				,							A					:       bool = True				,							A					:       Dict[str, int] = None				,							A					:       PILImageResampling = PIL.Image.NEAREST				,							A					:       bool = False				,							A					:       Dict[str, int] = None				,							A					:       Union[int, float] = 1 / 255				,							A					:       bool = False				,							A					:       bool = True				,							A					:       bool = True				,							A					:       Optional[Union[float, List[float]]] = None				,							A					:       Optional[Union[float, List[float]]] = None				,							A					:       bool = True				,							**A					:       List[Any]				,							):
         super().__init__(**A						)
         __snake_case:		Dict					    =		size if size is not None else {"""height""": 346, """width""": 346}
         __snake_case:		Dict					    =		get_size_dict(A						)
         __snake_case:		Any					    =		crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
         __snake_case:		Dict					    =		get_size_dict(A				,							param_name="""crop_size"""						)

         __snake_case:		Optional[int]					    =		do_resize
         __snake_case:		Dict					    =		size
         __snake_case:		Optional[Any]					    =		resample
         __snake_case:		Optional[Any]					    =		do_center_crop
         __snake_case:		Any					    =		crop_size
         __snake_case:		Optional[Any]					    =		do_rescale
         __snake_case:		Any					    =		rescale_factor
         __snake_case:		str					    =		rescale_offset
         __snake_case:		Optional[int]					    =		do_normalize
         __snake_case:		Optional[int]					    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __snake_case:		Union[str, Any]					    =		image_std if image_std is not None else IMAGENET_STANDARD_STD
         __snake_case:		str					    =		include_top




       def 				UpperCAmelCase__					(    self					:       Optional[int]				,							A					:       np.ndarray				,							A					:       Dict[str, int]				,							A					:       PILImageResampling = PIL.Image.NEAREST				,							A					:       Optional[Union[str, ChannelDimension]] = None				,							**A					:       List[Any]				,							):
         __snake_case:		Dict					    =		get_size_dict(A						)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
         return resize(
             A				,							size=(size["""height"""], size["""width"""])				,							resample=A				,							data_format=A				,							**A						)




       def 				UpperCAmelCase__					(    self					:       Any				,							A					:       np.ndarray				,							A					:       Dict[str, int]				,							A					:       Optional[Union[str, ChannelDimension]] = None				,							**A					:       str				,							):
         __snake_case:		List[Any]					    =		get_size_dict(A						)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
         return center_crop(A				,							size=(size["""height"""], size["""width"""])				,							data_format=A				,							**A						)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       np.ndarray				,							A					:       Union[int, float]				,							A					:       bool = True				,							A					:       Optional[Union[str, ChannelDimension]] = None				,							**A					:       List[Any]				,							):
         if offset:
           __snake_case:		List[str]					    =		(image - 127.5) * scale
           if data_format is not None:
             __snake_case:		Tuple					    =		to_channel_dimension_format(A				,							A						)
           __snake_case:		Any					    =		rescaled_image.astype(np.floataa						)
         else:
           __snake_case:		Optional[int]					    =		rescale(A				,							scale=A				,							data_format=A				,							**A						)
         return rescaled_image




       def 				UpperCAmelCase__					(    self					:       Any				,							A					:       np.ndarray				,							A					:       Union[float, List[float]]				,							A					:       Union[float, List[float]]				,							A					:       Optional[Union[str, ChannelDimension]] = None				,							**A					:       Dict				,							):
         return normalize(A				,							mean=A				,							std=A				,							data_format=A				,							**A						)






       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       ImageInput				,							A					:       bool = None				,							A					:       Dict[str, int] = None				,							A					:       Any=None				,							A					:       bool = None				,							A					:       Dict[str, int] = None				,							A					:       bool = None				,							A					:       float = None				,							A					:       bool = 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					:       ChannelDimension = ChannelDimension.FIRST				,							**A					:       Dict				,							):
         __snake_case:		Any					    =		do_resize if do_resize is not None else self.do_resize
         __snake_case:		Tuple					    =		resample if resample is not None else self.resample
         __snake_case:		Optional[int]					    =		do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case:		Optional[Any]					    =		do_rescale if do_rescale is not None else self.do_rescale
         __snake_case:		Tuple					    =		rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case:		Optional[Any]					    =		rescale_offset if rescale_offset is not None else self.rescale_offset
         __snake_case:		Tuple					    =		do_normalize if do_normalize is not None else self.do_normalize
         __snake_case:		Any					    =		image_mean if image_mean is not None else self.image_mean
         __snake_case:		int					    =		image_std if image_std is not None else self.image_std
         __snake_case:		List[Any]					    =		include_top if include_top is not None else self.include_top

         __snake_case:		Optional[Any]					    =		size if size is not None else self.size
         __snake_case:		Tuple					    =		get_size_dict(A						)
         __snake_case:		List[Any]					    =		crop_size if crop_size is not None else self.crop_size
         __snake_case:		Any					    =		get_size_dict(A				,							param_name="""crop_size"""						)

         __snake_case:		Optional[int]					    =		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 or resample is None:
           raise ValueError("""Size and resample 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."""						)

         # All transformations expect numpy arrays.
         __snake_case:		List[Any]					    =		[to_numpy_array(A						) for image in images]

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

         if do_center_crop:
           __snake_case:		int					    =		[self.center_crop(image=A				,							size=A						) for image in images]

         if do_rescale:
           __snake_case:		Any					    =		[self.rescale(image=A				,							scale=A				,							offset=A						) for image in images]

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

         if include_top:
           __snake_case:		List[str]					    =		[self.normalize(image=A				,							mean=[0, 0, 0]				,							std=A						) for image in images]

         __snake_case:		Union[str, Any]					    =		[to_channel_dimension_format(A				,							A						) for image in images]

         __snake_case:		Union[str, Any]					    =		{"""pixel_values""": images}
         return BatchFeature(data=A				,							tensor_type=A						)













import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  if "model" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""model."""   ,   """""")
  if "norm1" in orig_key:
    __snake_case:		Tuple					    =		orig_key.replace("""norm1"""   ,   """attention.output.LayerNorm""")
  if "norm2" in orig_key:
    __snake_case:		Any					    =		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:		str					    =		orig_key.split(""".""")[0].split("""_""")[-1]
    __snake_case:		int					    =		orig_key.replace(F'''transformer_{layer_num}'''   ,   F'''encoder.layer.{layer_num}''')
  if "mha.attn" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""mha.attn"""   ,   """attention.self""")
  if "mha" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mha"""   ,   """attention""")
  if "W_q" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""W_q"""   ,   """self.query""")
  if "W_k" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""W_k"""   ,   """self.key""")
  if "W_v" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""W_v"""   ,   """self.value""")
  if "ff1" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""ff1"""   ,   """intermediate.dense""")
  if "ff2" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""ff2"""   ,   """output.dense""")
  if "ff" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""ff"""   ,   """output.dense""")
  if "mlm_class" in orig_key:
    __snake_case:		Union[str, Any]					    =		orig_key.replace("""mlm.mlm_class"""   ,   """cls.predictions.decoder""")
  if "mlm" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mlm"""   ,   """cls.predictions.transform""")
  if "cls" not in orig_key:
    __snake_case:		Dict					    =		"""nystromformer.""" + orig_key

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

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

  __snake_case:		str					    =		orig_state_dict["""cls.predictions.decoder.bias"""]
  __snake_case:		int					    =		(
      torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
  )

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		convert_checkpoint_helper(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  model.load_state_dict(SCREAMING_SNAKE_CASE__)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

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


if __name__ == "__main__":
      __UpperCAmelCase			:  str										=				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."
      )
      __UpperCAmelCase			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






