


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[Any]:
				lowerCAmelCase_ :      List[str]    				=				'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
				lowerCAmelCase_ :      int    				=				Image.open(requests.get(lowerCAmelCase_ ,   stream=lowerCAmelCase_ ).raw ).convert('''RGB''' )

				lowerCAmelCase_ :      str    				=				transforms.Compose(
				    [
				        transforms.Resize((image_size, image_size) ,   interpolation=InterpolationMode.BICUBIC ),
				        transforms.ToTensor(),
				        transforms.Normalize((0.48145466, 0.4578275, 0.40821073) ,   (0.26862954, 0.26130258, 0.27577711) ),
				    ] )
				lowerCAmelCase_ :      Optional[Any]    				=				transform(lowerCAmelCase_ ).unsqueeze(0 ).to(lowerCAmelCase_ )
				return image



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				if "visual_encoder" in key:
								lowerCAmelCase_ :      List[str]    				=				re.sub('''visual_encoder*''' ,   '''vision_model.encoder''' ,   lowerCAmelCase_ )
				if "blocks" in key:
								lowerCAmelCase_ :      Optional[Any]    				=				re.sub(r'''blocks''' ,   '''layers''' ,   lowerCAmelCase_ )
				if "attn" in key:
								lowerCAmelCase_ :      List[str]    				=				re.sub(r'''attn''' ,   '''self_attn''' ,   lowerCAmelCase_ )
				if "norm1" in key:
								lowerCAmelCase_ :      Union[str, Any]    				=				re.sub(r'''norm1''' ,   '''layer_norm1''' ,   lowerCAmelCase_ )
				if "norm2" in key:
								lowerCAmelCase_ :      List[str]    				=				re.sub(r'''norm2''' ,   '''layer_norm2''' ,   lowerCAmelCase_ )
				if "encoder.norm" in key:
								lowerCAmelCase_ :      Optional[Any]    				=				re.sub(r'''encoder.norm''' ,   '''post_layernorm''' ,   lowerCAmelCase_ )
				if "encoder.patch_embed.proj" in key:
								lowerCAmelCase_ :      Dict    				=				re.sub(r'''encoder.patch_embed.proj''' ,   '''embeddings.patch_embedding''' ,   lowerCAmelCase_ )

				if "encoder.pos_embed" in key:
								lowerCAmelCase_ :      Optional[Any]    				=				re.sub(r'''encoder.pos_embed''' ,   '''embeddings.position_embedding''' ,   lowerCAmelCase_ )
				if "encoder.cls_token" in key:
								lowerCAmelCase_ :      Any    				=				re.sub(r'''encoder.cls_token''' ,   '''embeddings.class_embedding''' ,   lowerCAmelCase_ )

				if "self_attn" in key:
								lowerCAmelCase_ :      str    				=				re.sub(r'''self_attn.proj''' ,   '''self_attn.projection''' ,   lowerCAmelCase_ )

				return key



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_=None )-> Any:
				if config_path is not None:
								lowerCAmelCase_ :      Optional[int]    				=				BlipConfig.from_pretrained(lowerCAmelCase_ )
				else:
								lowerCAmelCase_ :      int    				=				BlipConfig(projection_dim=512 ,   text_config={} ,   vision_config={} )

				lowerCAmelCase_ :      List[str]    				=				BlipForConditionalGeneration(lowerCAmelCase_ ).eval()

				lowerCAmelCase_ :      Any    				=				'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

				lowerCAmelCase_ :      Optional[Any]    				=				blip_decoder(pretrained=lowerCAmelCase_ ,   image_size=384 ,   vit='''base''' )
				lowerCAmelCase_ :      Union[str, Any]    				=				pt_model.eval()

				lowerCAmelCase_ :      int    				=				pt_model.state_dict()
				for key in modified_state_dict.copy():
								lowerCAmelCase_ :      List[Any]    				=				modified_state_dict.pop(lowerCAmelCase_ )
								lowerCAmelCase_ :      int    				=				rename_key(lowerCAmelCase_ )
								lowerCAmelCase_ :      int    				=				value

				hf_model.load_state_dict(lowerCAmelCase_ )

				lowerCAmelCase_ :      int    				=				384
				lowerCAmelCase_ :      Any    				=				load_demo_image(image_size=lowerCAmelCase_ ,   device='''cpu''' )
				lowerCAmelCase_ :      Dict    				=				BertTokenizer.from_pretrained('''bert-base-uncased''' )
				lowerCAmelCase_ :      Dict    				=				tokenizer(['''a picture of'''] ).input_ids

				lowerCAmelCase_ :      Union[str, Any]    				=				hf_model.generate(lowerCAmelCase_ ,   lowerCAmelCase_ )

				assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

				lowerCAmelCase_ :      str    				=				hf_model.generate(lowerCAmelCase_ )

				assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

				if pytorch_dump_folder_path is not None:
								hf_model.save_pretrained(lowerCAmelCase_ )

				# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
				lowerCAmelCase_ :      List[str]    				=				(
				    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
				)

				lowerCAmelCase_ :      Dict    				=				blip_vqa(pretrained=lowerCAmelCase_ ,   image_size=lowerCAmelCase_ ,   vit='''base''' )
				vqa_model.eval()

				lowerCAmelCase_ :      List[str]    				=				vqa_model.state_dict()
				for key in modified_state_dict.copy():
								lowerCAmelCase_ :      Tuple    				=				modified_state_dict.pop(lowerCAmelCase_ )
								lowerCAmelCase_ :      Optional[int]    				=				rename_key(lowerCAmelCase_ )
								lowerCAmelCase_ :      Optional[Any]    				=				value

				lowerCAmelCase_ :      str    				=				BlipForQuestionAnswering(lowerCAmelCase_ )

				hf_vqa_model.load_state_dict(lowerCAmelCase_ )

				lowerCAmelCase_ :      Optional[int]    				=				['''How many dogs are in this image?''']
				lowerCAmelCase_ :      List[Any]    				=				tokenizer(lowerCAmelCase_ ,   return_tensors='''pt''' ).input_ids

				lowerCAmelCase_ :      Union[str, Any]    				=				hf_vqa_model.generate(lowerCAmelCase_ ,   lowerCAmelCase_ )
				print(tokenizer.decode(answer[0] ) )

				assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
				if pytorch_dump_folder_path is not None:
								hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa''' )

				lowerCAmelCase_ :      Dict    				=				'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

				lowerCAmelCase_ :      Any    				=				blip_itm(pretrained=lowerCAmelCase_ ,   image_size=lowerCAmelCase_ ,   vit='''base''' )
				itm_model.eval()

				lowerCAmelCase_ :      str    				=				itm_model.state_dict()
				for key in modified_state_dict.copy():
								lowerCAmelCase_ :      Optional[int]    				=				modified_state_dict.pop(lowerCAmelCase_ )
								lowerCAmelCase_ :      List[Any]    				=				rename_key(lowerCAmelCase_ )
								lowerCAmelCase_ :      str    				=				value

				lowerCAmelCase_ :      str    				=				BlipForImageTextRetrieval(lowerCAmelCase_ )

				lowerCAmelCase_ :      List[str]    				=				['''A picture of a woman with a dog sitting in a beach''']
				lowerCAmelCase_ :      str    				=				tokenizer(
				    lowerCAmelCase_ ,   return_tensors='''pt''' ,   padding='''max_length''' ,   truncation=lowerCAmelCase_ ,   max_length=35 ,   ).input_ids

				hf_itm_model.load_state_dict(lowerCAmelCase_ )
				hf_itm_model.eval()

				lowerCAmelCase_ :      Tuple    				=				hf_itm_model(lowerCAmelCase_ ,   lowerCAmelCase_ ,   use_itm_head=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[Any]    				=				hf_itm_model(lowerCAmelCase_ ,   lowerCAmelCase_ ,   use_itm_head=lowerCAmelCase_ )

				assert out[0].item() == 0.2110687494277954
				assert torch.nn.functional.softmax(out_itm[0] ,   dim=1 )[:, 1].item() == 0.45698845386505127

				if pytorch_dump_folder_path is not None:
								hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm''' )


if __name__ == "__main__":
						_UpperCAmelCase			:  Any     =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						_UpperCAmelCase			:  Optional[Any]     =parser.parse_args()

						convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase			:  int     =""""""
_UpperCAmelCase			:  Optional[int]     =""""""
_UpperCAmelCase			:  Dict     =""""""
_UpperCAmelCase			:  str     =""""""



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				# authorize twitter, initialize tweepy
				lowerCAmelCase_ :      Optional[int]    				=				tweepy.OAuthHandler(lowerCAmelCase_ ,   lowerCAmelCase_ )
				auth.set_access_token(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				tweepy.API(lowerCAmelCase_ )

				# 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_ :      Optional[int]    				=				api.user_timeline(screen_name=lowerCAmelCase_ ,   count=200 )

				# save most recent tweets
				alltweets.extend(lowerCAmelCase_ )

				# save the id of the oldest tweet less one
				lowerCAmelCase_ :      str    				=				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
								lowerCAmelCase_ :      Optional[Any]    				=				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
								lowerCAmelCase_ :      Optional[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
				lowerCAmelCase_ :      Union[str, 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_ :      Optional[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""")