


'''simple docstring'''




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   List[Any]:
							lowercase_      :    List[Any]       		= s.rsplit(UpperCAmelCase__   ,   UpperCAmelCase__				)
							return new.join(UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Dict				)    ->   int:
							# encoder.embeddings are double copied in original FLAVA
							return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]				)    ->   Union[str, Any]:
							lowercase_      :    int       		= {}

							lowercase_      :    Any       		= ["""group_1""", """group_2""", """group_3""", """group_4"""]
							for key, value in state_dict.items():
														for group_key in group_keys:
																					if group_key in key:
																												lowercase_      :    Tuple       		= key.replace(F'''{group_key}.'''   ,   F'''{group_key}.group.'''				)

														if "res_path" in key:
																					lowercase_      :    Any       		= key.replace("""res_path."""   ,   """res_path.path."""				)

														if key.endswith(""".w"""				):
																					lowercase_      :    int       		= rreplace(UpperCAmelCase__   ,   """.w"""   ,   """.weight"""   ,   1				)
														if key.endswith(""".b"""				):
																					lowercase_      :    int       		= rreplace(UpperCAmelCase__   ,   """.b"""   ,   """.bias"""   ,   1				)

														lowercase_      :    int       		= value.float()

							return upgrade




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      List[Any]=None   ,   UpperCAmelCase__ :      Optional[Any]=True				)    ->   Tuple:
							from dall_e import Encoder

							lowercase_      :    Tuple       		= Encoder()
							if os.path.exists(UpperCAmelCase__				):
														lowercase_      :    Tuple       		= torch.load(UpperCAmelCase__				)
							else:
														lowercase_      :    Dict       		= torch.hub.load_state_dict_from_url(UpperCAmelCase__				)

							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Dict       		= ckpt.state_dict()
							encoder.load_state_dict(UpperCAmelCase__				)

							if config_path is not None:
														lowercase_      :    Dict       		= FlavaImageCodebookConfig.from_pretrained(UpperCAmelCase__				)
							else:
														lowercase_      :    Any       		= FlavaImageCodebookConfig()

							lowercase_      :    Optional[int]       		= FlavaImageCodebook(UpperCAmelCase__				).eval()
							lowercase_      :    int       		= encoder.state_dict()

							lowercase_      :    Optional[Any]       		= upgrade_state_dict(UpperCAmelCase__				)
							hf_model.load_state_dict(UpperCAmelCase__				)
							lowercase_      :    int       		= hf_model.state_dict()
							lowercase_      :    Dict       		= count_parameters(UpperCAmelCase__				)
							lowercase_      :    List[str]       		= count_parameters(UpperCAmelCase__				)

							assert torch.allclose(UpperCAmelCase__   ,   UpperCAmelCase__   ,   atol=1e-3				)

							if save_checkpoint:
														hf_model.save_pretrained(UpperCAmelCase__				)
							else:
														return hf_state_dict


if __name__ == "__main__":
						_lowercase		:				Optional[Any]	       =		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 flava checkpoint")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						_lowercase		:				Optional[int]	       =		parser.parse_args()

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






'''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 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   List[Any]:
							lowercase_      :    str       		= 1.5
							lowercase_      :    List[Any]       		= int(factor * num_class_images				)
							lowercase_      :    int       		= ClipClient(
							    url="""https://knn.laion.ai/knn-service"""   ,   indice_name="""laion_400m"""   ,   num_images=UpperCAmelCase__   ,   aesthetic_weight=0.1				)

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

							while True:
														lowercase_      :    List[str]       		= client.query(text=UpperCAmelCase__				)
														if len(UpperCAmelCase__				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					lowercase_      :    List[str]       		= int(factor * num_images				)
																					lowercase_      :    List[str]       		= ClipClient(
																					    url="""https://knn.laion.ai/knn-service"""   ,   indice_name="""laion_400m"""   ,   num_images=UpperCAmelCase__   ,   aesthetic_weight=0.1   ,   )

							lowercase_      :    List[str]       		= 0
							lowercase_      :    Dict       		= 0
							lowercase_      :    Tuple       		= tqdm(desc="""downloading real regularization images"""   ,   total=UpperCAmelCase__				)

							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_      :    str       		= class_images[count]
																					count += 1
																					try:
																												lowercase_      :    Union[str, Any]       		= requests.get(images["""url"""]				)
																												if img.status_code == 200:
																																			lowercase_      :    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 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Any       		= argparse.ArgumentParser(""""""   ,   add_help=UpperCAmelCase__				)
							parser.add_argument("""--class_prompt"""   ,   help="""text prompt to retrieve images"""   ,   required=UpperCAmelCase__   ,   type=UpperCAmelCase__				)
							parser.add_argument("""--class_data_dir"""   ,   help="""path to save images"""   ,   required=UpperCAmelCase__   ,   type=UpperCAmelCase__				)
							parser.add_argument("""--num_class_images"""   ,   help="""number of images to download"""   ,   default=200   ,   type=UpperCAmelCase__				)
							return parser.parse_args()


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





