
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [
	    '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(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase  ,					lowercase       =    emb.weight.shape
	lowercase       =    nn.Linear(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							bias=__SCREAMING_SNAKE_CASE							)
	lowercase       =    emb.weight.data
	return lin_layer






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    torch.load(__SCREAMING_SNAKE_CASE ,							map_location='cpu'							)
	lowercase       =    mam_aaa['args'] or mam_aaa['cfg']['model']
	lowercase       =    mam_aaa['model']
	remove_ignore_keys_(__SCREAMING_SNAKE_CASE							)
	lowercase       =    state_dict['encoder.embed_tokens.weight'].shape[0]

	lowercase       =    MaMaaaConfig(
	    vocab_size=__SCREAMING_SNAKE_CASE ,							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' ,							)

	lowercase       =    state_dict['decoder.embed_tokens.weight']
	lowercase       =    MaMaaaForConditionalGeneration(__SCREAMING_SNAKE_CASE							)
	model.model.load_state_dict(__SCREAMING_SNAKE_CASE ,							strict=__SCREAMING_SNAKE_CASE							)
	lowercase       =    make_linear_from_emb(model.model.shared							)

	return model


if __name__ == "__main__":
	UpperCAmelCase	      = 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	      = parser.parse_args()
	UpperCAmelCase	      = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)



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)

