





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      =[
    ['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     lowerCamelCase__			(__lowerCamelCase	):
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							k.replace(__lowerCamelCase,       __lowerCamelCase	)

     if k.startswith("encoder"	):
          _SCREAMING_SNAKE_CASE       :	Any						    =							k.replace(".attn",       ".self_attn"	)
          _SCREAMING_SNAKE_CASE       :	str						    =							k.replace("norm1",       "self_attn_layer_norm"	)
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							k.replace("norm2",       "final_layer_norm"	)
     elif k.startswith("decoder"	):
          _SCREAMING_SNAKE_CASE       :	Any						    =							k.replace("norm1",       "self_attn_layer_norm"	)
          _SCREAMING_SNAKE_CASE       :	str						    =							k.replace("norm2",       "encoder_attn_layer_norm"	)
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							k.replace("norm3",       "final_layer_norm"	)
     return k






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	int						    =							[
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							sd.pop(__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							k.replace("layernorm_embedding",       "layer_norm"	)
          assert new_k not in sd
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							v


UpperCamelCase__      =['START']






@torch.no_grad()
def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.load(__lowerCamelCase,       map_location="cpu"	)
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model["model"]
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							BlenderbotConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BlenderbotForConditionalGeneration(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							m.model.state_dict().keys()
     _SCREAMING_SNAKE_CASE       :	int						    =							[]
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
       UpperCamelCase__      =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__      =parser.parse_args()
       convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






from PIL import Image






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							image.size
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							image.load()
     for i in range(__lowerCamelCase	):
          for j in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__lowerCamelCase	):
          for i in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	int						    =							255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
       UpperCamelCase__      =mean_threshold(Image.open('path_to_image').convert('L'))
       image.save('output_image_path')