





import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase				:       Tuple	   =				""""""
_UpperCAmelCase				:       Union[str, Any]	   =				""""""
_UpperCAmelCase				:       int	   =				""""""
_UpperCAmelCase				:       Optional[Any]	   =				""""""


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     None:
    # authorize twitter, initialize tweepy
    lowerCamelCase__    :      List[Any]					 =		tweepy.OAuthHandler(_UpperCAmelCase						,	_UpperCAmelCase )
    auth.set_access_token(_UpperCAmelCase						,	_UpperCAmelCase )
    lowerCamelCase__    :      List[Any]					 =		tweepy.API(_UpperCAmelCase )

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

    # make initial request for most recent tweets (200 is the maximum allowed count)
    lowerCamelCase__    :      Tuple					 =		api.user_timeline(screen_name=_UpperCAmelCase						,	count=200 )

    # save most recent tweets
    alltweets.extend(_UpperCAmelCase )

    # save the id of the oldest tweet less one
    lowerCamelCase__    :      int					 =		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
        lowerCamelCase__    :      Optional[Any]					 =		api.user_timeline(
            screen_name=_UpperCAmelCase						,	count=200						,	max_id=_UpperCAmelCase )

        # save most recent tweets
        alltweets.extend(_UpperCAmelCase )

        # update the id of the oldest tweet less one
        lowerCamelCase__    :      List[Any]					 =		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
    lowerCamelCase__    :      Optional[Any]					 =		[[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:
        lowerCamelCase__    :      Dict					 =		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 torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	encoder_layers=args.encoder_layers						,	decoder_layers=args.decoder_layers						,	encoder_attention_heads=args.encoder_attention_heads						,	decoder_attention_heads=args.decoder_attention_heads						,	encoder_ffn_dim=args.encoder_ffn_embed_dim						,	decoder_ffn_dim=args.decoder_ffn_embed_dim						,	d_model=args.encoder_embed_dim						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""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.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





