





"""simple docstring"""






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      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__  :	Optional[int]		 =			1.5
			a__  :	List[str]		 =			int(factor * num_class_images			)
			a__  :	str		 =			ClipClient(
			    url="https://knn.laion.ai/knn-service"							,			indice_name="laion_400m"							,			num_images=lowerCAmelCase__							,			aesthetic_weight=0.1			)

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

			while True:
						a__  :	Dict		 =			client.query(text=lowerCAmelCase__			)
						if len(lowerCAmelCase__			) >= factor * num_class_images or num_images > 1E4:
									break
						else:
									a__  :	Optional[int]		 =			int(factor * num_images			)
									a__  :	Any		 =			ClipClient(
									    url="https://knn.laion.ai/knn-service"							,			indice_name="laion_400m"							,			num_images=lowerCAmelCase__							,			aesthetic_weight=0.1							,			)

			a__  :	Optional[int]		 =			0
			a__  :	Tuple		 =			0
			a__  :	Union[str, Any]		 =			tqdm(desc="downloading real regularization images"							,			total=lowerCAmelCase__			)

			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:
									a__  :	List[Any]		 =			class_images[count]
									count += 1
									try:
												a__  :	Optional[Any]		 =			requests.get(images["url"]			)
												if img.status_code == 2_0_0:
															a__  :	List[str]		 =			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      lowercase__ (					)    ->			Any:



			'''simple docstring'''
			a__  :	Optional[int]		 =			argparse.ArgumentParser(""							,			add_help=lowerCAmelCase__			)
			parser.add_argument("--class_prompt"							,			help="text prompt to retrieve images"							,			required=lowerCAmelCase__							,			type=lowerCAmelCase__			)
			parser.add_argument("--class_data_dir"							,			help="path to save images"							,			required=lowerCAmelCase__							,			type=lowerCAmelCase__			)
			parser.add_argument("--num_class_images"							,			help="number of images to download"							,			default=2_0_0							,			type=lowerCAmelCase__			)
			return parser.parse_args()


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






"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase						=			{
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

__UpperCAmelCase						=			AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]=False			)    ->			List[str]:



			'''simple docstring'''
			a__ ,	a__  :	Optional[int]		 =			create_model(
			    "HTSAT-tiny"							,			"roberta"							,			lowerCAmelCase__							,			precision="fp32"							,			device="cuda:0" if torch.cuda.is_available() else "cpu"							,			enable_fusion=lowerCAmelCase__							,			fusion_type="aff_2d" if enable_fusion else None							,			)
			return model, model_cfg






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]			)    ->			Dict:



			'''simple docstring'''
			a__  :	Any		 =			{}

			a__  :	Tuple		 =			R".*sequential.(\d+).*"
			a__  :	Tuple		 =			R".*_projection.(\d+).*"

			for key, value in state_dict.items():
						# check if any key needs to be modified
						for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
									if key_to_modify in key:
												a__  :	str		 =			key.replace(lowerCAmelCase__							,			lowerCAmelCase__			)

						if re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									# replace sequential layers with list
									a__  :	Optional[Any]		 =			re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)

									a__  :	int		 =			key.replace(F"sequential.{sequential_layer}."							,			F"layers.{int(lowerCAmelCase__			)//3}.linear."			)
						elif re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									a__  :	Tuple		 =			int(re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)			)

									# Because in CLAP they use `nn.Sequential`...
									a__  :	List[Any]		 =			1 if projecton_layer == 0 else 2

									a__  :	Dict		 =			key.replace(F"_projection.{projecton_layer}."							,			F"_projection.linear{transformers_projection_layer}."			)

						if "audio" and "qkv" in key:
									# split qkv into query key and value
									a__  :	List[Any]		 =			value
									a__  :	List[Any]		 =			mixed_qkv.size(0			) // 3

									a__  :	Optional[int]		 =			mixed_qkv[:qkv_dim]
									a__  :	List[str]		 =			mixed_qkv[qkv_dim : qkv_dim * 2]
									a__  :	Optional[Any]		 =			mixed_qkv[qkv_dim * 2 :]

									a__  :	Tuple		 =			query_layer
									a__  :	int		 =			key_layer
									a__  :	Optional[int]		 =			value_layer
						else:
									a__  :	List[str]		 =			value

			return model_state_dict






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Tuple=False			)    ->			Tuple:



			'''simple docstring'''
			a__ ,	a__  :	Tuple		 =			init_clap(lowerCAmelCase__							,			enable_fusion=lowerCAmelCase__			)

			clap_model.eval()
			a__  :	Optional[int]		 =			clap_model.state_dict()
			a__  :	Optional[Any]		 =			rename_state_dict(lowerCAmelCase__			)

			a__  :	Union[str, Any]		 =			ClapConfig()
			a__  :	Dict		 =			enable_fusion
			a__  :	Any		 =			ClapModel(lowerCAmelCase__			)

			# ignore the spectrogram embedding layer
			model.load_state_dict(lowerCAmelCase__							,			strict=lowerCAmelCase__			)

			model.save_pretrained(lowerCAmelCase__			)
			transformers_config.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
		__UpperCAmelCase						=			parser.parse_args()

		convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)