




"""simple docstring"""




import csv

import tweepy

# Twitter API credentials
lowercase__				:      List[Any]	   = ''
lowercase__				:      Union[str, Any]	   = ''
lowercase__				:      List[Any]	   = ''
lowercase__				:      Optional[int]	   = ''

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     # authorize twitter, initialize tweepy
     a     =				tweepy.OAuthHandler(UpperCamelCase__		, UpperCamelCase__)
     auth.set_access_token(UpperCamelCase__		, UpperCamelCase__)
     a     =				tweepy.API(UpperCamelCase__)

     # initialize a list to hold all the tweepy Tweets
     a     =				[]

     # make initial request for most recent tweets (200 is the maximum allowed count)
     a     =				api.user_timeline(screen_name=UpperCamelCase__		, count=2_00)

     # save most recent tweets
     alltweets.extend(UpperCamelCase__)

     # save the id of the oldest tweet less one
     a     =				alltweets[-1].id - 1

     # keep grabbing tweets until there are no tweets left to grab
     while len(UpperCamelCase__) > 0:
          print(f'''getting tweets before {oldest}''')

          # all subsequent requests use the max_id param to prevent duplicates
          a     =				api.user_timeline(
              screen_name=UpperCamelCase__		, count=2_00		, max_id=UpperCamelCase__)

          # save most recent tweets
          alltweets.extend(UpperCamelCase__)

          # update the id of the oldest tweet less one
          a     =				alltweets[-1].id - 1

          print(f'''...{len(UpperCamelCase__)} tweets downloaded so far''')

     # transform the tweepy tweets into a 2D array that will populate the csv
     a     =				[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

     # write the csv
     with open(f'''new_{screen_name}_tweets.csv'''		, "w") as f:
          a     =				csv.writer(UpperCamelCase__)
          writer.writerow(["id", "created_at", "text"])
          writer.writerows(UpperCamelCase__)


if __name__ == "__main__":
   # pass in the username of the account you want to download
   get_all_tweets("FirePing32")







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)

