




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def      lowerCamelCase_						(			UpperCamelCase__							: Union[str, Any] )	->			Any:





				"""simple docstring"""


				if "img_encoder.pos_embed" in name:
								__lowerCamelCase									=       name.replace('img_encoder.pos_embed'					,	'vision_model.embeddings.position_embeddings' )
				if "img_encoder.patch_embed.proj" in name:
								__lowerCamelCase									=       name.replace('img_encoder.patch_embed.proj'					,	'vision_model.embeddings.patch_embeddings.projection' )
				if "img_encoder.patch_embed.norm" in name:
								__lowerCamelCase									=       name.replace('img_encoder.patch_embed.norm'					,	'vision_model.embeddings.layernorm' )
				if "img_encoder.layers" in name:
								__lowerCamelCase									=       name.replace('img_encoder.layers'					,	'vision_model.encoder.stages' )
				if "blocks" in name and "res" not in name:
								__lowerCamelCase									=       name.replace('blocks'					,	'layers' )
				if "attn" in name and "pre_assign" not in name:
								__lowerCamelCase									=       name.replace('attn'					,	'self_attn' )
				if "proj" in name and "self_attn" in name and "text" not in name:
								__lowerCamelCase									=       name.replace('proj'					,	'out_proj' )
				if "pre_assign_attn.attn.proj" in name:
								__lowerCamelCase									=       name.replace('pre_assign_attn.attn.proj'					,	'pre_assign_attn.attn.out_proj' )
				if "norm1" in name:
								__lowerCamelCase									=       name.replace('norm1'					,	'layer_norm1' )
				if "norm2" in name and "pre_assign" not in name:
								__lowerCamelCase									=       name.replace('norm2'					,	'layer_norm2' )
				if "img_encoder.norm" in name:
								__lowerCamelCase									=       name.replace('img_encoder.norm'					,	'vision_model.layernorm' )
				# text encoder
				if "text_encoder.token_embedding" in name:
								__lowerCamelCase									=       name.replace('text_encoder.token_embedding'					,	'text_model.embeddings.token_embedding' )
				if "text_encoder.positional_embedding" in name:
								__lowerCamelCase									=       name.replace('text_encoder.positional_embedding'					,	'text_model.embeddings.position_embedding.weight' )
				if "text_encoder.transformer.resblocks." in name:
								__lowerCamelCase									=       name.replace('text_encoder.transformer.resblocks.'					,	'text_model.encoder.layers.' )
				if "ln_1" in name:
								__lowerCamelCase									=       name.replace('ln_1'					,	'layer_norm1' )
				if "ln_2" in name:
								__lowerCamelCase									=       name.replace('ln_2'					,	'layer_norm2' )
				if "c_fc" in name:
								__lowerCamelCase									=       name.replace('c_fc'					,	'fc1' )
				if "c_proj" in name:
								__lowerCamelCase									=       name.replace('c_proj'					,	'fc2' )
				if "text_encoder" in name:
								__lowerCamelCase									=       name.replace('text_encoder'					,	'text_model' )
				if "ln_final" in name:
								__lowerCamelCase									=       name.replace('ln_final'					,	'final_layer_norm' )
				# projection layers
				if "img_projector.linear_hidden." in name:
								__lowerCamelCase									=       name.replace('img_projector.linear_hidden.'					,	'visual_projection.' )
				if "img_projector.linear_out." in name:
								__lowerCamelCase									=       name.replace('img_projector.linear_out.'					,	'visual_projection.3.' )
				if "text_projector.linear_hidden" in name:
								__lowerCamelCase									=       name.replace('text_projector.linear_hidden'					,	'text_projection' )
				if "text_projector.linear_out" in name:
								__lowerCamelCase									=       name.replace('text_projector.linear_out'					,	'text_projection.3' )

				return name







def      lowerCamelCase_						(			UpperCamelCase__							: List[str]					,	UpperCamelCase__							: List[str] )	->			Optional[int]:





				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								__lowerCamelCase									=       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									=       key.split('.' )
												__lowerCamelCase			,		__lowerCamelCase									=       int(key_split[2] ), int(key_split[4] )
												__lowerCamelCase									=       config.vision_config.hidden_size
												if "weight" in key:
																__lowerCamelCase									=       val[:dim, :]
																__lowerCamelCase									=       val[dim : dim * 2, :]
																__lowerCamelCase									=       val[-dim:, :]
												else:
																__lowerCamelCase									=       val[:dim]
																__lowerCamelCase									=       val[dim : dim * 2]
																__lowerCamelCase									=       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									=       key.split('.' )
												__lowerCamelCase									=       int(key_split[3] )
												__lowerCamelCase									=       config.text_config.hidden_size
												if "weight" in key:
																__lowerCamelCase									=       val[:dim, :]
																__lowerCamelCase									=       val[
																    dim : dim * 2, :
																]
																__lowerCamelCase									=       val[-dim:, :]
												else:
																__lowerCamelCase									=       val[:dim]
																__lowerCamelCase									=       val[dim : dim * 2]
																__lowerCamelCase									=       val[-dim:]
								else:
												__lowerCamelCase									=       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									=       val.squeeze_()
												else:
																__lowerCamelCase									=       val

				return orig_state_dict







def      lowerCamelCase_						(			)	->			Any:





				"""simple docstring"""


				__lowerCamelCase									=       'http://images.cocodataset.org/val2017/000000039769.jpg'
				__lowerCamelCase									=       Image.open(requests.get(UpperCamelCase__					,	stream=UpperCamelCase__ ).raw )
				return im







@torch.no_grad()
def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Optional[Any]="groupvit-gcc-yfcc"					,	UpperCamelCase__							: Tuple=False )	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       GroupViTConfig()
				__lowerCamelCase									=       GroupViTModel(UpperCamelCase__ ).eval()

				__lowerCamelCase									=       torch.load(UpperCamelCase__					,	map_location='cpu' )['model']
				__lowerCamelCase									=       convert_state_dict(UpperCamelCase__					,	UpperCamelCase__ )
				__lowerCamelCase			,		__lowerCamelCase									=       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									=       CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32' )
				__lowerCamelCase									=       prepare_img()
				__lowerCamelCase									=       processor(text=['a photo of a cat', 'a photo of a dog']					,	images=UpperCamelCase__					,	padding=UpperCamelCase__					,	return_tensors='pt' )

				with torch.no_grad():
								__lowerCamelCase									=       model(**UpperCamelCase__ )

				if model_name == "groupvit-gcc-yfcc":
								__lowerCamelCase									=       torch.tensor([[13.35_23, 6.36_29]] )
				elif model_name == "groupvit-gcc-redcaps":
								__lowerCamelCase									=       torch.tensor([[16.18_73, 8.62_30]] )
				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								=       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)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''vit_mae'''


			def __init__( self     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=12     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=224     ,		lowerCamelCase__=16     ,		lowerCamelCase__=3     ,		lowerCamelCase__=True     ,		lowerCamelCase__=16     ,		lowerCamelCase__=512     ,		lowerCamelCase__=8     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=0.75     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)

							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       qkv_bias
							__lowerCamelCase									=       decoder_num_attention_heads
							__lowerCamelCase									=       decoder_hidden_size
							__lowerCamelCase									=       decoder_num_hidden_layers
							__lowerCamelCase									=       decoder_intermediate_size
							__lowerCamelCase									=       mask_ratio
							__lowerCamelCase									=       norm_pix_loss


