



"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def __lowercase			(							_a    ):


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

    return name




def __lowercase			(							_a		,			_a    ):


    for key in orig_state_dict.copy().keys():
        snake_case_ :     Union[str, Any]				=	orig_state_dict.pop(lowerCAmelCase_    )

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

    return orig_state_dict




def __lowercase			(							):
    snake_case_ :     Optional[int]				=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case_ :     Union[str, Any]				=	Image.open(requests.get(lowerCAmelCase_		,			stream=lowerCAmelCase_    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a="groupvit-gcc-yfcc"		,			_a=False    ):
    snake_case_ :     List[Any]				=	GroupViTConfig()
    snake_case_ :     Any				=	GroupViTModel(lowerCAmelCase_    ).eval()

    snake_case_ :     Dict				=	torch.load(lowerCAmelCase_		,			map_location='''cpu'''    )["""model"""]
    snake_case_ :     Optional[int]				=	convert_state_dict(lowerCAmelCase_		,			lowerCAmelCase_    )
    snake_case_ :     str				=	model.load_state_dict(lowerCAmelCase_		,			strict=lowerCAmelCase_    )
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase_    ) == 0)

    # verify result
    snake_case_ :     int				=	CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''    )
    snake_case_ :     Union[str, Any]				=	prepare_img()
    snake_case_ :     List[Any]				=	processor(text=['''a photo of a cat''', '''a photo of a dog''']		,			images=lowerCAmelCase_		,			padding=lowerCAmelCase_		,			return_tensors='''pt'''    )

    with torch.no_grad():
        snake_case_ :     List[str]				=	model(**lowerCAmelCase_    )

    if model_name == "groupvit-gcc-yfcc":
        snake_case_ :     List[str]				=	torch.tensor([[13.3_523, 6.3629]]    )
    elif model_name == "groupvit-gcc-redcaps":
        snake_case_ :     Optional[int]				=	torch.tensor([[16.1_873, 8.6230]]    )
    else:
        raise ValueError(f"Model name {model_name} not supported."    )
    assert torch.allclose(outputs.logits_per_image		,			lowerCAmelCase_		,			atol=1E-3    )

    processor.save_pretrained(lowerCAmelCase_    )
    model.save_pretrained(lowerCAmelCase_    )
    print('''Successfully saved processor and model to'''		,			lowerCAmelCase_    )

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


if __name__ == "__main__":
     lowercase__    :  Any							    =	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`.''',
     )
     lowercase__    :  Union[str, Any]							    =	parser.parse_args()

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










'''simple docstring'''






from torch import nn



class 							lowercase       (  nn.Module				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   List[Any]:
        super().__init__()
        _UpperCAmelCase  :						Dict			     = class_size
        _UpperCAmelCase  :						Union[str, Any]			     = embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        _UpperCAmelCase  :						List[Any]			     = nn.Linear(a_  ,a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
        # hidden_state = self.mlp2(hidden_state)
        _UpperCAmelCase  :						Optional[int]			     = self.mlp(a_							)
        return logits



