






'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

							return name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[str]:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__ :     str	  =	orig_state_dict.pop(UpperCamelCase				)

														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
																					lowerCamelCase__ :     Optional[Any]	  =	key.split("""."""				)
																					lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	int(key_split[2]				), int(key_split[4]				)
																					lowerCamelCase__ :     List[str]	  =	config.vision_config.hidden_size
																					if "weight" in key:
																												lowerCamelCase__ :     Any	  =	val[:dim, :]
																												lowerCamelCase__ :     List[str]	  =	val[dim : dim * 2, :]
																												lowerCamelCase__ :     int	  =	val[-dim:, :]
																					else:
																												lowerCamelCase__ :     List[str]	  =	val[:dim]
																												lowerCamelCase__ :     str	  =	val[dim : dim * 2]
																												lowerCamelCase__ :     Dict	  =	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
																					lowerCamelCase__ :     Any	  =	key.split("""."""				)
																					lowerCamelCase__ :     Tuple	  =	int(key_split[3]				)
																					lowerCamelCase__ :     Any	  =	config.text_config.hidden_size
																					if "weight" in key:
																												lowerCamelCase__ :     Optional[Any]	  =	val[:dim, :]
																												lowerCamelCase__ :     int	  =	val[
																												    dim : dim * 2, :
																												]
																												lowerCamelCase__ :     int	  =	val[-dim:, :]
																					else:
																												lowerCamelCase__ :     Dict	  =	val[:dim]
																												lowerCamelCase__ :     str	  =	val[dim : dim * 2]
																												lowerCamelCase__ :     str	  =	val[-dim:]
														else:
																					lowerCamelCase__ :     List[str]	  =	rename_key(UpperCamelCase				)
																					# 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
																					):
																												lowerCamelCase__ :     Any	  =	val.squeeze_()
																					else:
																												lowerCamelCase__ :     Union[str, Any]	  =	val

							return orig_state_dict





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     Optional[int]	  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__ :     Any	  =	Image.open(requests.get(UpperCamelCase    ,      stream=UpperCamelCase				).raw				)
							return im





@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase="groupvit-gcc-yfcc"    ,      UpperCamelCase=False				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	GroupViTConfig()
							lowerCamelCase__ :     List[Any]	  =	GroupViTModel(UpperCamelCase				).eval()

							lowerCamelCase__ :     List[str]	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)["""model"""]
							lowerCamelCase__ :     str	  =	convert_state_dict(UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	model.load_state_dict(UpperCamelCase    ,      strict=UpperCamelCase				)
							assert missing_keys == ["text_model.embeddings.position_ids"]
							assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCamelCase				) == 0)

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

							with torch.no_grad():
														lowerCamelCase__ :     Optional[int]	  =	model(**UpperCamelCase				)

							if model_name == "groupvit-gcc-yfcc":
														lowerCamelCase__ :     Optional[int]	  =	torch.tensor([[13.3523, 6.3629]]				)
							elif model_name == "groupvit-gcc-redcaps":
														lowerCamelCase__ :     List[str]	  =	torch.tensor([[16.1873, 8.6230]]				)
							else:
														raise ValueError(f'''Model name {model_name} not supported.'''				)
							assert torch.allclose(outputs.logits_per_image    ,      UpperCamelCase    ,      atol=1E-3				)

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

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


if __name__ == "__main__":
			_A     :      int   					=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     :      List[str]   					=parser.parse_args()

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









'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[Any]:
							if "cls_token" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""cls_token"""    ,      """vit.embeddings.cls_token"""				)
							if "mask_token" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""mask_token"""    ,      """decoder.mask_token"""				)
							if "decoder_pos_embed" in name:
														lowerCamelCase__ :     Tuple	  =	name.replace("""decoder_pos_embed"""    ,      """decoder.decoder_pos_embed"""				)
							if "pos_embed" in name and "decoder" not in name:
														lowerCamelCase__ :     str	  =	name.replace("""pos_embed"""    ,      """vit.embeddings.position_embeddings"""				)
							if "patch_embed.proj" in name:
														lowerCamelCase__ :     Optional[int]	  =	name.replace("""patch_embed.proj"""    ,      """vit.embeddings.patch_embeddings.projection"""				)
							if "patch_embed.norm" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""patch_embed.norm"""    ,      """vit.embeddings.norm"""				)
							if "decoder_blocks" in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""decoder_blocks"""    ,      """decoder.decoder_layers"""				)
							if "blocks" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""blocks"""    ,      """vit.encoder.layer"""				)
							if "attn.proj" in name:
														lowerCamelCase__ :     List[Any]	  =	name.replace("""attn.proj"""    ,      """attention.output.dense"""				)
							if "attn" in name:
														lowerCamelCase__ :     List[str]	  =	name.replace("""attn"""    ,      """attention.self"""				)
							if "norm1" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""norm1"""    ,      """layernorm_before"""				)
							if "norm2" in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""norm2"""    ,      """layernorm_after"""				)
							if "mlp.fc1" in name:
														lowerCamelCase__ :     str	  =	name.replace("""mlp.fc1"""    ,      """intermediate.dense"""				)
							if "mlp.fc2" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""mlp.fc2"""    ,      """output.dense"""				)
							if "decoder_embed" in name:
														lowerCamelCase__ :     Tuple	  =	name.replace("""decoder_embed"""    ,      """decoder.decoder_embed"""				)
							if "decoder_norm" in name:
														lowerCamelCase__ :     Optional[int]	  =	name.replace("""decoder_norm"""    ,      """decoder.decoder_norm"""				)
							if "decoder_pred" in name:
														lowerCamelCase__ :     int	  =	name.replace("""decoder_pred"""    ,      """decoder.decoder_pred"""				)
							if "norm.weight" in name and "decoder" not in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""norm.weight"""    ,      """vit.layernorm.weight"""				)
							if "norm.bias" in name and "decoder" not in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""norm.bias"""    ,      """vit.layernorm.bias"""				)

							return name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Tuple:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__ :     List[str]	  =	orig_state_dict.pop(UpperCamelCase				)

														if "qkv" in key:
																					lowerCamelCase__ :     List[Any]	  =	key.split("""."""				)
																					lowerCamelCase__ :     Optional[int]	  =	int(key_split[1]				)
																					if "decoder_blocks" in key:
																												lowerCamelCase__ :     str	  =	config.decoder_hidden_size
																												lowerCamelCase__ :     List[Any]	  =	"""decoder.decoder_layers."""
																												if "weight" in key:
																																			lowerCamelCase__ :     int	  =	val[:dim, :]
																																			lowerCamelCase__ :     int	  =	val[dim : dim * 2, :]
																																			lowerCamelCase__ :     Tuple	  =	val[-dim:, :]
																												elif "bias" in key:
																																			lowerCamelCase__ :     Tuple	  =	val[:dim]
																																			lowerCamelCase__ :     Optional[int]	  =	val[dim : dim * 2]
																																			lowerCamelCase__ :     List[Any]	  =	val[-dim:]
																					else:
																												lowerCamelCase__ :     List[Any]	  =	config.hidden_size
																												lowerCamelCase__ :     Optional[int]	  =	"""vit.encoder.layer."""
																												if "weight" in key:
																																			lowerCamelCase__ :     str	  =	val[:dim, :]
																																			lowerCamelCase__ :     List[Any]	  =	val[dim : dim * 2, :]
																																			lowerCamelCase__ :     Optional[int]	  =	val[-dim:, :]
																												elif "bias" in key:
																																			lowerCamelCase__ :     int	  =	val[:dim]
																																			lowerCamelCase__ :     List[Any]	  =	val[dim : dim * 2]
																																			lowerCamelCase__ :     Optional[int]	  =	val[-dim:]

														else:
																					lowerCamelCase__ :     int	  =	val

							return orig_state_dict





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Any	  =	ViTMAEConfig()
							if "large" in checkpoint_url:
														lowerCamelCase__ :     Any	  =	1024
														lowerCamelCase__ :     Optional[Any]	  =	4096
														lowerCamelCase__ :     List[str]	  =	24
														lowerCamelCase__ :     Union[str, Any]	  =	16
							elif "huge" in checkpoint_url:
														lowerCamelCase__ :     List[str]	  =	14
														lowerCamelCase__ :     Dict	  =	1280
														lowerCamelCase__ :     Tuple	  =	5120
														lowerCamelCase__ :     List[str]	  =	32
														lowerCamelCase__ :     Union[str, Any]	  =	16

							lowerCamelCase__ :     List[Any]	  =	ViTMAEForPreTraining(UpperCamelCase				)

							lowerCamelCase__ :     str	  =	torch.hub.load_state_dict_from_url(UpperCamelCase    ,      map_location="""cpu"""				)["""model"""]

							lowerCamelCase__ :     Union[str, Any]	  =	ViTMAEImageProcessor(size=config.image_size				)

							lowerCamelCase__ :     List[str]	  =	convert_state_dict(UpperCamelCase    ,      UpperCamelCase				)

							model.load_state_dict(UpperCamelCase				)
							model.eval()

							lowerCamelCase__ :     Union[str, Any]	  =	"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

							lowerCamelCase__ :     List[Any]	  =	Image.open(requests.get(UpperCamelCase    ,      stream=UpperCamelCase				).raw				)
							lowerCamelCase__ :     str	  =	ViTMAEImageProcessor(size=config.image_size				)
							lowerCamelCase__ :     Any	  =	image_processor(images=UpperCamelCase    ,      return_tensors="""pt"""				)

							# forward pass
							torch.manual_seed(2				)
							lowerCamelCase__ :     Optional[Any]	  =	model(**UpperCamelCase				)
							lowerCamelCase__ :     Optional[Any]	  =	outputs.logits

							if "large" in checkpoint_url:
														lowerCamelCase__ :     List[Any]	  =	torch.tensor(
														    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]				)
							elif "huge" in checkpoint_url:
														lowerCamelCase__ :     Optional[Any]	  =	torch.tensor(
														    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
							else:
														lowerCamelCase__ :     int	  =	torch.tensor(
														    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]				)

							# verify logits
							assert torch.allclose(logits[0, :3, :3]    ,      UpperCamelCase    ,      atol=1E-4				)

							print(f'''Saving model to {pytorch_dump_folder_path}'''				)
							model.save_pretrained(UpperCamelCase				)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
							image_processor.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
			_A     :      str   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
			    type=str,
			    help='''URL of the checkpoint you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_A     :      Tuple   					=parser.parse_args()
			convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

