
import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase					:			Tuple			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			List[Any]			      =		[
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

__lowerCAmelCase					:			Tuple			      =		[
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Tuple:
    __lowercase		:			Union[str, Any]	    =       torch.load(__lowerCAmelCase	,					map_location='''cpu'''	)
    return sd


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=rename_keys_prefix	) ->						Tuple:
    __lowercase		:			int	    =       OrderedDict()
    __lowercase		:			Optional[int]	    =       torch.arange(config.max_position_embeddings	).expand((1, -1)	)
    # detector_d = OrderedDict()
    for key in d:
        if "detector" in key:
            # detector_d[key.replace('detector.','')] = d[key]
            continue
        __lowercase		:			Tuple	    =       key
        for name_pair in rename_keys_prefix:
            __lowercase		:			Any	    =       new_key.replace(name_pair[0]	,					name_pair[1]	)
        __lowercase		:			Union[str, Any]	    =       d[key]
        if key == "bert.cls.predictions.decoder.weight":
            # Old bert code didn't have `decoder.bias`, but was added separately
            __lowercase		:			Union[str, Any]	    =       new_d['''cls.predictions.bias''']
    return new_d


@torch.no_grad()
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    assert (
        checkpoint_path.split('''/'''	)[-1] in ACCEPTABLE_CHECKPOINTS
    ), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'

    # Get Config
    if "pre" in checkpoint_path:
        __lowercase		:			Union[str, Any]	    =       '''pretraining'''
        if "vcr" in checkpoint_path:
            __lowercase		:			Tuple	    =       {'''visual_embedding_dim''': 512}
        elif "vqa_advanced" in checkpoint_path:
            __lowercase		:			Any	    =       {'''visual_embedding_dim''': 2_048}
        elif "vqa" in checkpoint_path:
            __lowercase		:			List[Any]	    =       {'''visual_embedding_dim''': 2_048}
        elif "nlvr" in checkpoint_path:
            __lowercase		:			Any	    =       {'''visual_embedding_dim''': 1_024}
        else:
            raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'	)
    else:
        if "vcr" in checkpoint_path:
            __lowercase		:			Tuple	    =       {'''visual_embedding_dim''': 512}
            __lowercase		:			str	    =       '''multichoice'''
        elif "vqa_advanced" in checkpoint_path:
            __lowercase		:			Tuple	    =       {'''visual_embedding_dim''': 2_048}
            __lowercase		:			List[Any]	    =       '''vqa_advanced'''
        elif "vqa" in checkpoint_path:
            __lowercase		:			int	    =       {'''visual_embedding_dim''': 2_048, '''num_labels''': 3_129}
            __lowercase		:			Tuple	    =       '''vqa'''
        elif "nlvr" in checkpoint_path:
            __lowercase		:			str	    =       {
                '''visual_embedding_dim''': 1_024,
                '''num_labels''': 2,
            }
            __lowercase		:			Optional[int]	    =       '''nlvr'''

    __lowercase		:			Optional[Any]	    =       VisualBertConfig(**__lowerCAmelCase	)

    # Load State Dict
    __lowercase		:			Optional[Any]	    =       load_state_dict(__lowerCAmelCase	)

    __lowercase		:			int	    =       get_new_dict(__lowerCAmelCase	,					__lowerCAmelCase	)

    if model_type == "pretraining":
        __lowercase		:			Any	    =       VisualBertForPreTraining(__lowerCAmelCase	)
    elif model_type == "vqa":
        __lowercase		:			int	    =       VisualBertForQuestionAnswering(__lowerCAmelCase	)
    elif model_type == "nlvr":
        __lowercase		:			str	    =       VisualBertForVisualReasoning(__lowerCAmelCase	)
    elif model_type == "multichoice":
        __lowercase		:			str	    =       VisualBertForMultipleChoice(__lowerCAmelCase	)

    model.load_state_dict(__lowerCAmelCase	)
    # Save Checkpoints
    Path(__lowerCAmelCase	).mkdir(exist_ok=__lowerCAmelCase	)
    model.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
 __lowerCAmelCase					:			List[str]			      =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
 parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
 __lowerCAmelCase					:			List[str]			      =		parser.parse_args()
 convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)




import csv

import tweepy

# Twitter API credentials
__lowerCAmelCase					:			str			      =		""
__lowerCAmelCase					:			Any			      =		""
__lowerCAmelCase					:			Any			      =		""
__lowerCAmelCase					:			Optional[int]			      =		""


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						None:
    # authorize twitter, initialize tweepy
    __lowercase		:			List[Any]	    =       tweepy.OAuthHandler(__lowerCAmelCase	,					__lowerCAmelCase	)
    auth.set_access_token(__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			str	    =       tweepy.API(__lowerCAmelCase	)

    # initialize a list to hold all the tweepy Tweets
    __lowercase		:			Union[str, Any]	    =       []

    # make initial request for most recent tweets (200 is the maximum allowed count)
    __lowercase		:			List[Any]	    =       api.user_timeline(screen_name=__lowerCAmelCase	,					count=200	)

    # save most recent tweets
    alltweets.extend(__lowerCAmelCase	)

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

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

        # all subsequent requests use the max_id param to prevent duplicates
        __lowercase		:			Tuple	    =       api.user_timeline(
            screen_name=__lowerCAmelCase	,					count=200	,					max_id=__lowerCAmelCase	)

        # save most recent tweets
        alltweets.extend(__lowerCAmelCase	)

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

        print(F'...{len(__lowerCAmelCase	)} tweets downloaded so far'	)

    # transform the tweepy tweets into a 2D array that will populate the csv
    __lowercase		:			Optional[int]	    =       [[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:
        __lowercase		:			int	    =       csv.writer(__lowerCAmelCase	)
        writer.writerow(['''id''', '''created_at''', '''text''']	)
        writer.writerows(__lowerCAmelCase	)


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


