




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
      _UpperCAmelCase      =			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """_float_tensor""",
          """decoder.output_projection.weight""",
      ]
      for k in ignore_keys:
            state_dict.pop(snake_case			,     snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
      _UpperCAmelCase    ,					_UpperCAmelCase      =			emb.weight.shape
      _UpperCAmelCase      =			nn.Linear(snake_case			,     snake_case			,     bias=snake_case       )
      _UpperCAmelCase      =			emb.weight.data
      return lin_layer




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case="facebook/mbart-large-en-ro"			,     snake_case=False			,     snake_case=False       )						->				Any:
      _UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )["""model"""]
      remove_ignore_keys_(snake_case       )
      _UpperCAmelCase      =			state_dict["""encoder.embed_tokens.weight"""].shape[0]

      _UpperCAmelCase      =			MBartConfig.from_pretrained(snake_case			,     vocab_size=snake_case       )
      if mbart_aa and finetuned:
            _UpperCAmelCase      =			"""relu"""

      _UpperCAmelCase      =			state_dict["""decoder.embed_tokens.weight"""]
      _UpperCAmelCase      =			MBartForConditionalGeneration(snake_case       )
      model.model.load_state_dict(snake_case       )

      if finetuned:
            _UpperCAmelCase      =			make_linear_from_emb(model.model.shared       )

      return model


if __name__ == "__main__":
    a													=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
    )
    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument(
        "--hf_config",
        default="facebook/mbart-large-cc25",
        type=str,
        help="Which huggingface architecture to use: mbart-large",
    )
    parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
    parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
    a													=			parser.parse_args()
    a													=			convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)





import csv

import tweepy

# Twitter API credentials
a													=			""
a													=			""
a													=			""
a													=			""




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				None:
						# authorize twitter, initialize tweepy
						_UpperCAmelCase      =			tweepy.OAuthHandler(snake_case			,     snake_case       )
						auth.set_access_token(snake_case			,     snake_case       )
						_UpperCAmelCase      =			tweepy.API(snake_case       )

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

						# make initial request for most recent tweets (200 is the maximum allowed count)
						_UpperCAmelCase      =			api.user_timeline(screen_name=snake_case			,     count=2_0_0       )

						# save most recent tweets
						alltweets.extend(snake_case       )

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

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

												# all subsequent requests use the max_id param to prevent duplicates
												_UpperCAmelCase      =			api.user_timeline(
												    screen_name=snake_case			,     count=2_0_0			,     max_id=snake_case       )

												# save most recent tweets
												alltweets.extend(snake_case       )

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

												print(f"...{len(snake_case       )} tweets downloaded so far"       )

						# transform the tweepy tweets into a 2D array that will populate the csv
						_UpperCAmelCase      =			[[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:
												_UpperCAmelCase      =			csv.writer(snake_case       )
												writer.writerow(["""id""", """created_at""", """text"""]       )
												writer.writerows(snake_case       )


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