




'''simple docstring'''





import colorsys

from PIL import Image  # type: ignore





def       __a      (  _UpperCamelCase:       float   ,		_UpperCamelCase:       float   ,		_UpperCamelCase:       int       )		->					float:

     """simple docstring"""




     _snake_case				= x
     _snake_case				= y
     for step in range(_UpperCamelCase       ):  # noqa: B007
          _snake_case				= a * a - b * b + x
          _snake_case				= 2 * a * b + y
          _snake_case				= 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       __a      (  _UpperCamelCase:       float       )		->					tuple:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       float       )		->					tuple:

     """simple docstring"""




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





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

     """simple docstring"""




     _snake_case				= Image.new("RGB"   ,		(image_width, image_height)       )
     _snake_case				= img.load()

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

               _snake_case				= get_distance(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )

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

     return img


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # colored version, full figure
   UpperCamelCase_		:     Tuple          =		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()








'''simple docstring'''





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]





def       __a      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

     if k.startswith("encoder"       ):
          _snake_case				= k.replace(".attn"   ,		".self_attn"       )
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"final_layer_norm"       )
     elif k.startswith("decoder"       ):
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"encoder_attn_layer_norm"       )
          _snake_case				= k.replace("norm3"   ,		"final_layer_norm"       )
     return k





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _snake_case				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )
     _snake_case				= model["model"]
     _snake_case				= BlenderbotConfig.from_json_file(_UpperCamelCase       )
     _snake_case				= BlenderbotForConditionalGeneration(_UpperCamelCase       )
     _snake_case				= m.model.state_dict().keys()
     _snake_case				= []
     _snake_case				= {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          _snake_case				= rename_state_dict_key(_UpperCamelCase       )
          if new_k not in valid_keys:
               failures.append([k, new_k]       )
          else:
               _snake_case				= v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(_UpperCamelCase       )
     m.model.load_state_dict(_UpperCamelCase   ,		strict=_UpperCamelCase       )
     m.half()
     m.save_pretrained(_UpperCamelCase       )


if __name__ == "__main__":
   UpperCamelCase_		:     Any          =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
   parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
   parser.add_argument(
       '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
   )
   UpperCamelCase_		:     Optional[int]          =		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


