
import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase	      = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    torchvision.models.resnetaaa(pretrained=snake_case       )
		lowercase       =    list(model.children()       )[:-2]
		lowercase       =    nn.Sequential(*snake_case       )
		lowercase       =    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
		lowercase       =    self.pool(self.model(snake_case       )       )
		lowercase       =    torch.flatten(snake_case		,    start_dim=2       )
		lowercase       =    out.transpose(1		,    2       ).contiguous()
		return out  # BxNx2048







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    [json.loads(snake_case       ) for l in open(snake_case       )]
		lowercase       =    os.path.dirname(snake_case       )
		lowercase       =    tokenizer
		lowercase       =    labels
		lowercase       =    len(snake_case       )
		lowercase       =    max_seq_length

		lowercase       =    transforms





	def __len__(       self       ):
		return len(self.data       )





	def __getitem__(       self		,    snake_case       ):
		lowercase       =    torch.LongTensor(self.tokenizer.encode(self.data[index]['text']		,    add_special_tokens=snake_case       )       )
		lowercase  ,					lowercase  ,					lowercase       =    sentence[0], sentence[1:-1], sentence[-1]
		lowercase       =    sentence[: self.max_seq_length]

		lowercase       =    torch.zeros(self.n_classes       )
		lowercase       =    1

		lowercase       =    Image.open(os.path.join(self.data_dir		,    self.data[index]['img']       )       ).convert('RGB'       )
		lowercase       =    self.transforms(snake_case       )

		return {
		    "image_start_token": start_token,
		    "image_end_token": end_token,
		    "sentence": sentence,
		    "image": image,
		    "label": label,
		}





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Counter()
		for row in self.data:
			label_freqs.update(row['label']       )
		return label_freqs






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [len(row['sentence']							) for row in batch]
	lowercase  ,					lowercase       =    len(__SCREAMING_SNAKE_CASE							), max(__SCREAMING_SNAKE_CASE							)

	lowercase       =    torch.zeros(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							dtype=torch.long							)
	lowercase       =    torch.zeros(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							dtype=torch.long							)

	for i_batch, (input_row, length) in enumerate(zip(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)							):
		lowercase       =    input_row['sentence']
		lowercase       =    1

	lowercase       =    torch.stack([row['image'] for row in batch]							)
	lowercase       =    torch.stack([row['label'] for row in batch]							)
	lowercase       =    torch.stack([row['image_start_token'] for row in batch]							)
	lowercase       =    torch.stack([row['image_end_token'] for row in batch]							)

	return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor






def 							UpperCAmelCase_		(							):
	return [
	    "Crime",
	    "Drama",
	    "Thriller",
	    "Action",
	    "Comedy",
	    "Romance",
	    "Documentary",
	    "Short",
	    "Mystery",
	    "History",
	    "Family",
	    "Adventure",
	    "Fantasy",
	    "Sci-Fi",
	    "Western",
	    "Horror",
	    "Sport",
	    "War",
	    "Music",
	    "Musical",
	    "Animation",
	    "Biography",
	    "Film-Noir",
	]






def 							UpperCAmelCase_		(							):
	return transforms.Compose(
	    [
	        transforms.Resize(256							),
	        transforms.CenterCrop(224							),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17] ,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69] ,							),
	    ]							)



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    1.5
	lowercase       =    int(factor * num_class_images							)
	lowercase       =    ClipClient(
	    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1							)

	os.makedirs(F'''{class_data_dir}/images''' ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if len(list(Path(F'''{class_data_dir}/images'''							).iterdir()							)							) >= num_class_images:
		return

	while True:
		lowercase       =    client.query(text=__SCREAMING_SNAKE_CASE							)
		if len(__SCREAMING_SNAKE_CASE							) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			lowercase       =    int(factor * num_images							)
			lowercase       =    ClipClient(
			    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1 ,							)

	lowercase       =    0
	lowercase       =    0
	lowercase       =    tqdm(desc='downloading real regularization images' ,							total=__SCREAMING_SNAKE_CASE							)

	with open(F'''{class_data_dir}/caption.txt''' ,							'w'							) as fa, open(F'''{class_data_dir}/urls.txt''' ,							'w'							) as fa, open(
	    F'''{class_data_dir}/images.txt''' ,							'w'							) as fa:
		while total < num_class_images:
			lowercase       =    class_images[count]
			count += 1
			try:
				lowercase       =    requests.get(images['url']							)
				if img.status_code == 200:
					lowercase       =    Image.open(BytesIO(img.content							)							)
					with open(F'''{class_data_dir}/images/{total}.jpg''' ,							'wb'							) as f:
						f.write(img.content							)
					fa.write(images['caption'] + '\n'							)
					fa.write(images['url'] + '\n'							)
					fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'							)
					total += 1
					pbar.update(1							)
				else:
					continue
			except Exception:
				continue
	return






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser('' ,							add_help=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_prompt' ,							help='text prompt to retrieve images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_data_dir' ,							help='path to save images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--num_class_images' ,							help='number of images to download' ,							default=200 ,							type=__SCREAMING_SNAKE_CASE							)
	return parser.parse_args()


if __name__ == "__main__":
	UpperCAmelCase	      = parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)

