





'''simple docstring'''




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       argparse.ArgumentParser(
      description=(
          '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
          ''' Distillation'''
      )
  )
  parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
  parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
  parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
  parser.add_argument('''--vocab_transform''', action='''store_true''')
  lowercase__       : Dict         =       parser.parse_args()

  if args.model_type == "bert":
    lowercase__       : Dict         =       BertForMaskedLM.from_pretrained(args.model_name)
    lowercase__       : int         =       '''bert'''
  else:
    raise ValueError('''args.model_type should be "bert".''')

  lowercase__       : List[Any]         =       model.state_dict()
  lowercase__       : Optional[Any]         =       {}

  for w in ["word_embeddings", "position_embeddings"]:
    lowercase__       : Dict         =       state_dict[f"""{prefix}.embeddings.{w}.weight"""]
  for w in ["weight", "bias"]:
    lowercase__       : Any         =       state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

  lowercase__       : List[Any]         =       0
  for teacher_idx in [0, 2, 4, 7, 9, 11]:
    for w in ["weight", "bias"]:
      lowercase__       : Optional[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
      ]
      lowercase__       : str         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
      ]
      lowercase__       : Optional[int]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
      ]

      lowercase__       : Dict         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      lowercase__       : List[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      lowercase__       : Dict         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      lowercase__       : List[str]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      lowercase__       : List[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  lowercase__       : int         =       state_dict['''cls.predictions.decoder.weight''']
  lowercase__       : List[Any]         =       state_dict['''cls.predictions.bias''']
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      lowercase__       : Optional[Any]         =       state_dict[f"""cls.predictions.transform.dense.{w}"""]
      lowercase__       : Union[str, Any]         =       state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

  print(f"""N layers selected for distillation: {std_idx}""")
  print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

  print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
  torch.save(compressed_sd, args.dump_checkpoint)


'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


    lowerCAmelCase 						=							x
    lowerCAmelCase 						=							y
    for step in range(snake_case			):  # noqa: B007
        lowerCAmelCase 						=							a * a - b * b + x
        lowerCAmelCase 						=							2 * a * b + y
        lowerCAmelCase 						=							a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(snake_case					,		1					,		1			)			)


def       snake_case   (	snake_case      : int = 800					,		snake_case      : int = 600					,		snake_case      : float = -0.6					,		snake_case      : float = 0					,		snake_case      : float = 3.2					,		snake_case      : int = 50					,		snake_case      : bool = True					,		) ->  Image.Image:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							img.load()

    # loop through the image-coordinates
    for image_x in range(snake_case			):
        for image_y in range(snake_case			):
            # determine the figure-coordinates based on the image-coordinates
            lowerCAmelCase 						=							figure_width / image_width * image_height
            lowerCAmelCase 						=							figure_center_x + (image_x / image_width - 0.5) * figure_width
            lowerCAmelCase 						=							figure_center_y + (image_y / image_height - 0.5) * figure_height

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                lowerCAmelCase 						=							get_color_coded_rgb(snake_case			)
            else:
                lowerCAmelCase 						=							get_black_and_white_rgb(snake_case			)

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  Dict							       =				get_image()

     # uncomment for colored version, different section, zoomed in
     # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
     # figure_width = 0.8)

     # uncomment for black and white version, full figure
     # img = get_image(use_distance_color_coding = False)

     # uncomment to save the image
     # img.save("mandelbrot.png")

     img.show()





