




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 				A__ (snake_case   :		Optional[Any]				)     ->						List[str]:
					# vision encoder
					if "img_encoder.pos_embed" in name:
										__UpperCamelCase					:	List[str]             =      name.replace("""img_encoder.pos_embed"""		,	"""vision_model.embeddings.position_embeddings"""				)
					if "img_encoder.patch_embed.proj" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""img_encoder.patch_embed.proj"""		,	"""vision_model.embeddings.patch_embeddings.projection"""				)
					if "img_encoder.patch_embed.norm" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""img_encoder.patch_embed.norm"""		,	"""vision_model.embeddings.layernorm"""				)
					if "img_encoder.layers" in name:
										__UpperCamelCase					:	str             =      name.replace("""img_encoder.layers"""		,	"""vision_model.encoder.stages"""				)
					if "blocks" in name and "res" not in name:
										__UpperCamelCase					:	int             =      name.replace("""blocks"""		,	"""layers"""				)
					if "attn" in name and "pre_assign" not in name:
										__UpperCamelCase					:	List[Any]             =      name.replace("""attn"""		,	"""self_attn"""				)
					if "proj" in name and "self_attn" in name and "text" not in name:
										__UpperCamelCase					:	List[Any]             =      name.replace("""proj"""		,	"""out_proj"""				)
					if "pre_assign_attn.attn.proj" in name:
										__UpperCamelCase					:	List[str]             =      name.replace("""pre_assign_attn.attn.proj"""		,	"""pre_assign_attn.attn.out_proj"""				)
					if "norm1" in name:
										__UpperCamelCase					:	Any             =      name.replace("""norm1"""		,	"""layer_norm1"""				)
					if "norm2" in name and "pre_assign" not in name:
										__UpperCamelCase					:	Optional[Any]             =      name.replace("""norm2"""		,	"""layer_norm2"""				)
					if "img_encoder.norm" in name:
										__UpperCamelCase					:	str             =      name.replace("""img_encoder.norm"""		,	"""vision_model.layernorm"""				)
					# text encoder
					if "text_encoder.token_embedding" in name:
										__UpperCamelCase					:	int             =      name.replace("""text_encoder.token_embedding"""		,	"""text_model.embeddings.token_embedding"""				)
					if "text_encoder.positional_embedding" in name:
										__UpperCamelCase					:	int             =      name.replace("""text_encoder.positional_embedding"""		,	"""text_model.embeddings.position_embedding.weight"""				)
					if "text_encoder.transformer.resblocks." in name:
										__UpperCamelCase					:	Any             =      name.replace("""text_encoder.transformer.resblocks."""		,	"""text_model.encoder.layers."""				)
					if "ln_1" in name:
										__UpperCamelCase					:	int             =      name.replace("""ln_1"""		,	"""layer_norm1"""				)
					if "ln_2" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""ln_2"""		,	"""layer_norm2"""				)
					if "c_fc" in name:
										__UpperCamelCase					:	Any             =      name.replace("""c_fc"""		,	"""fc1"""				)
					if "c_proj" in name:
										__UpperCamelCase					:	Optional[int]             =      name.replace("""c_proj"""		,	"""fc2"""				)
					if "text_encoder" in name:
										__UpperCamelCase					:	str             =      name.replace("""text_encoder"""		,	"""text_model"""				)
					if "ln_final" in name:
										__UpperCamelCase					:	Optional[Any]             =      name.replace("""ln_final"""		,	"""final_layer_norm"""				)
					# projection layers
					if "img_projector.linear_hidden." in name:
										__UpperCamelCase					:	Optional[int]             =      name.replace("""img_projector.linear_hidden."""		,	"""visual_projection."""				)
					if "img_projector.linear_out." in name:
										__UpperCamelCase					:	Optional[Any]             =      name.replace("""img_projector.linear_out."""		,	"""visual_projection.3."""				)
					if "text_projector.linear_hidden" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""text_projector.linear_hidden"""		,	"""text_projection"""				)
					if "text_projector.linear_out" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""text_projector.linear_out"""		,	"""text_projection.3"""				)

					return name





def 				A__ (snake_case   :		List[str]		,	snake_case   :		str				)     ->						Tuple:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase					:	Dict             =      orig_state_dict.pop(snake_case				)

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__UpperCamelCase					:	List[Any]             =      key.split("""."""				)
															__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      int(key_split[2]				), int(key_split[4]				)
															__UpperCamelCase					:	int             =      config.vision_config.hidden_size
															if "weight" in key:
																				__UpperCamelCase					:	List[str]             =      val[:dim, :]
																				__UpperCamelCase					:	Dict             =      val[dim : dim * 2, :]
																				__UpperCamelCase					:	str             =      val[-dim:, :]
															else:
																				__UpperCamelCase					:	Tuple             =      val[:dim]
																				__UpperCamelCase					:	Optional[Any]             =      val[dim : dim * 2]
																				__UpperCamelCase					:	Tuple             =      val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__UpperCamelCase					:	Dict             =      key.split("""."""				)
															__UpperCamelCase					:	List[str]             =      int(key_split[3]				)
															__UpperCamelCase					:	Union[str, Any]             =      config.text_config.hidden_size
															if "weight" in key:
																				__UpperCamelCase					:	List[Any]             =      val[:dim, :]
																				__UpperCamelCase					:	Any             =      val[
																				    dim : dim * 2, :
																				]
																				__UpperCamelCase					:	List[Any]             =      val[-dim:, :]
															else:
																				__UpperCamelCase					:	Any             =      val[:dim]
																				__UpperCamelCase					:	Any             =      val[dim : dim * 2]
																				__UpperCamelCase					:	Union[str, Any]             =      val[-dim:]
										else:
															__UpperCamelCase					:	int             =      rename_key(snake_case				)
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				__UpperCamelCase					:	Union[str, Any]             =      val.squeeze_()
															else:
																				__UpperCamelCase					:	Any             =      val

					return orig_state_dict





def 				A__ ()     ->						Union[str, Any]:
					__UpperCamelCase					:	Tuple             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__UpperCamelCase					:	Dict             =      Image.open(requests.get(snake_case		,	stream=snake_case				).raw				)
					return im





@torch.no_grad()
def 				A__ (snake_case   :		str		,	snake_case   :		str		,	snake_case   :		Union[str, Any]="groupvit-gcc-yfcc"		,	snake_case   :		Optional[Any]=False				)     ->						List[str]:
					__UpperCamelCase					:	int             =      GroupViTConfig()
					__UpperCamelCase					:	Any             =      GroupViTModel(snake_case				).eval()

					__UpperCamelCase					:	Optional[Any]             =      torch.load(snake_case		,	map_location="""cpu"""				)["""model"""]
					__UpperCamelCase					:	str             =      convert_state_dict(snake_case		,	snake_case				)
					__UpperCamelCase      ,			__UpperCamelCase					:	Union[str, Any]             =      model.load_state_dict(snake_case		,	strict=snake_case				)
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(snake_case				) == 0)

					# verify result
					__UpperCamelCase					:	int             =      CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""				)
					__UpperCamelCase					:	Optional[int]             =      prepare_img()
					__UpperCamelCase					:	Optional[int]             =      processor(text=["""a photo of a cat""", """a photo of a dog"""]		,	images=snake_case		,	padding=snake_case		,	return_tensors="""pt"""				)

					with torch.no_grad():
										__UpperCamelCase					:	Optional[Any]             =      model(**snake_case				)

					if model_name == "groupvit-gcc-yfcc":
										__UpperCamelCase					:	Tuple             =      torch.tensor([[13.3523, 6.3629]]				)
					elif model_name == "groupvit-gcc-redcaps":
										__UpperCamelCase					:	Tuple             =      torch.tensor([[16.1873, 8.6230]]				)
					else:
										raise ValueError(F'''Model name {model_name} not supported.'''				)
					assert torch.allclose(outputs.logits_per_image		,	snake_case		,	atol=1e-3				)

					processor.save_pretrained(snake_case				)
					model.save_pretrained(snake_case				)
					print("""Successfully saved processor and model to"""		,	snake_case				)

					if push_to_hub:
										print("""Pushing to the hub..."""				)
										processor.push_to_hub(snake_case		,	organization="""nielsr"""				)
										model.push_to_hub(snake_case		,	organization="""nielsr"""				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
	)
	parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
	parser.add_argument(
	    '''--model_name''',
	    default='''groupvit-gccy-fcc''',
	    type=str,
	    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
	)
	a__		     =     parser.parse_args()

	convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












def 				A__ (snake_case   :		int				)     ->						bool:
					if number < 0:
										raise ValueError("""number must not be negative"""				)
					return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()






