




"""simple docstring"""




import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 8)      ->					str:
     a     =				ascii_letters + digits + punctuation
     return "".join(secrets.choice(__UpperCamelCase) for _ in range(__UpperCamelCase))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					str:
     # Password Generator = full boot with random_number, random_letters, and
     # random_character FUNCTIONS
     # Put your code here...
     i -= len(__UpperCamelCase)
     a     =				i // 3
     a     =				i % 3
     # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
     #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
     a     =				(
         chars_incl
         + random(__UpperCamelCase		, quotient + remainder)
         + random(__UpperCamelCase		, __UpperCamelCase)
         + random(__UpperCamelCase		, __UpperCamelCase)
     )
     a     =				list(__UpperCamelCase)
     shuffle(__UpperCamelCase)
     return "".join(__UpperCamelCase)

     # random is a generalised function for letters, characters and numbers

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					str:
     return "".join(secrets.choice(__UpperCamelCase) for _ in range(__UpperCamelCase))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     pass  # Put your code here...

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Union[str, Any]:
     pass  # Put your code here...

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
     pass  # Put your code here...

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase = 8)      ->					bool:

     if len(__UpperCamelCase) < min_length:
          # Your Password must be at least 8 characters long
          return False

     a     =				any(char in ascii_uppercase for char in password)
     a     =				any(char in ascii_lowercase for char in password)
     a     =				any(char in digits for char in password)
     a     =				any(char in punctuation for char in password)

     return upper and lower and num and spec_char
     # Passwords should contain UPPERCASE, lowerase
     # numbers, and special characters

def  SCREAMING_SNAKE_CASE							(       )      ->					Any:
     a     =				int(input("Please indicate the max length of your password: ").strip())
     a     =				input(
         "Please indicate the characters that must be in your password: ").strip()
     print("Password generated:"		, password_generator(__UpperCamelCase))
     print(
         "Alternative Password generated:"		, alternative_password_generator(__UpperCamelCase		, __UpperCamelCase)		, )
     print("[If you are thinking of using this passsword, You better save it.]")


if __name__ == "__main__":
   main()







import argparse

import torch

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


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = [
    ["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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				[
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          a     =				sd.pop(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     a     =				m.model.state_dict().keys()
     a     =				[]
     a     =				{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          a     =				rename_state_dict_key(__UpperCamelCase)
          if new_k not in valid_keys:
               failures.append([k, new_k])
          else:
               a     =				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__":
   lowercase__				:      int	   = 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"
   )
   lowercase__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

