


import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				List[str] 						=						OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('module.encoder'					):
            _lowercase			:				List[Any] 						=						key.replace('module.encoder'				,					'glpn.encoder'					)
        if key.startswith('module.decoder'					):
            _lowercase			:				Optional[int] 						=						key.replace('module.decoder'				,					'decoder.stages'					)
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            _lowercase			:				Tuple 						=						key[key.find('patch_embed'					) + len('patch_embed'					)]
            _lowercase			:				Union[str, Any] 						=						key.replace(F'''patch_embed{idx}'''				,					F'''patch_embeddings.{int(lowerCamelCase_					)-1}'''					)
        if "norm" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('norm'				,					'layer_norm'					)
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            _lowercase			:				Dict 						=						key[key.find('glpn.encoder.layer_norm'					) + len('glpn.encoder.layer_norm'					)]
            _lowercase			:				Dict 						=						key.replace(F'''layer_norm{idx}'''				,					F'''layer_norm.{int(lowerCamelCase_					)-1}'''					)
        if "layer_norm1" in key:
            _lowercase			:				Dict 						=						key.replace('layer_norm1'				,					'layer_norm_1'					)
        if "layer_norm2" in key:
            _lowercase			:				Dict 						=						key.replace('layer_norm2'				,					'layer_norm_2'					)
        if "block" in key:
            # replace for example block1 by block.0
            _lowercase			:				Optional[int] 						=						key[key.find('block'					) + len('block'					)]
            _lowercase			:				str 						=						key.replace(F'''block{idx}'''				,					F'''block.{int(lowerCamelCase_					)-1}'''					)
        if "attn.q" in key:
            _lowercase			:				List[Any] 						=						key.replace('attn.q'				,					'attention.self.query'					)
        if "attn.proj" in key:
            _lowercase			:				Tuple 						=						key.replace('attn.proj'				,					'attention.output.dense'					)
        if "attn" in key:
            _lowercase			:				Optional[int] 						=						key.replace('attn'				,					'attention.self'					)
        if "fc1" in key:
            _lowercase			:				Dict 						=						key.replace('fc1'				,					'dense1'					)
        if "fc2" in key:
            _lowercase			:				Optional[int] 						=						key.replace('fc2'				,					'dense2'					)
        if "linear_pred" in key:
            _lowercase			:				str 						=						key.replace('linear_pred'				,					'classifier'					)
        if "linear_fuse" in key:
            _lowercase			:				List[Any] 						=						key.replace('linear_fuse.conv'				,					'linear_fuse'					)
            _lowercase			:				str 						=						key.replace('linear_fuse.bn'				,					'batch_norm'					)
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            _lowercase			:				int 						=						key[key.find('linear_c'					) + len('linear_c'					)]
            _lowercase			:				str 						=						key.replace(F'''linear_c{idx}'''				,					F'''linear_c.{int(lowerCamelCase_					)-1}'''					)
        if "bot_conv" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('bot_conv'				,					'0.convolution'					)
        if "skip_conv1" in key:
            _lowercase			:				int 						=						key.replace('skip_conv1'				,					'1.convolution'					)
        if "skip_conv2" in key:
            _lowercase			:				List[Any] 						=						key.replace('skip_conv2'				,					'2.convolution'					)
        if "fusion1" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('fusion1'				,					'1.fusion'					)
        if "fusion2" in key:
            _lowercase			:				Tuple 						=						key.replace('fusion2'				,					'2.fusion'					)
        if "fusion3" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('fusion3'				,					'3.fusion'					)
        if "fusion" in key and "conv" in key:
            _lowercase			:				Tuple 						=						key.replace('conv'				,					'convolutional_layer'					)
        if key.startswith('module.last_layer_depth'					):
            _lowercase			:				Dict 						=						key.replace('module.last_layer_depth'				,					'head.head'					)
        _lowercase			:				str 						=						value

    return new_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks					):
        for j in range(config.depths[i]					):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            _lowercase			:				Tuple 						=						state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
            _lowercase			:				str 						=						state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''					)
            # next, add keys and values (in that order) to the state dict
            _lowercase			:				int 						=						kv_weight[
                : config.hidden_sizes[i], :
            ]
            _lowercase			:				int 						=						kv_bias[: config.hidden_sizes[i]]
            _lowercase			:				str 						=						kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _lowercase			:				Any 						=						kv_bias[config.hidden_sizes[i] :]



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				int 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				Optional[Any] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return image



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False				,					lowerCamelCase_=None					)       ->		Optional[int]:
    _lowercase			:				List[Any] 						=						GLPNConfig(hidden_sizes=[64, 128, 320, 512]				,					decoder_hidden_size=64				,					depths=[3, 8, 27, 3]					)

    # load image processor (only resize + rescale)
    _lowercase			:				Optional[Any] 						=						GLPNImageProcessor()

    # prepare image
    _lowercase			:				List[Any] 						=						prepare_img()
    _lowercase			:				Union[str, Any] 						=						image_processor(images=lowerCamelCase_				,					return_tensors='pt'					).pixel_values

    logger.info('Converting model...'					)

    # load original state dict
    _lowercase			:				Tuple 						=						torch.load(lowerCamelCase_				,					map_location=torch.device('cpu'					)					)

    # rename keys
    _lowercase			:				Any 						=						rename_keys(lowerCamelCase_					)

    # key and value matrices need special treatment
    read_in_k_v(lowerCamelCase_				,					lowerCamelCase_					)

    # create HuggingFace model and load state dict
    _lowercase			:				Tuple 						=						GLPNForDepthEstimation(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # forward pass
    _lowercase			:				Optional[Any] 						=						model(lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            _lowercase			:				List[str] 						=						torch.tensor(
                [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]					)
        elif "kitti" in model_name:
            _lowercase			:				List[str] 						=						torch.tensor(
                [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					)
        else:
            raise ValueError(F'''Unknown model name: {model_name}'''					)

        _lowercase			:				List[Any] 						=						torch.Size([1, 480, 640]					)

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]				,					lowerCamelCase_				,					atol=1e-4					)
        print('Looks ok!'					)

    # finally, push to hub if required
    if push_to_hub:
        logger.info('Pushing model and image processor to the hub...'					)
        model.push_to_hub(
            repo_path_or_name=Path(lowerCamelCase_				,					lowerCamelCase_					)				,					organization='nielsr'				,					commit_message='Add model'				,					use_temp_dir=lowerCamelCase_				,					)
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowerCamelCase_				,					lowerCamelCase_					)				,					organization='nielsr'				,					commit_message='Add image processor'				,					use_temp_dir=lowerCamelCase_				,					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path",
     default=None,
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
 )
 parser.add_argument(
     "--model_name",
     default="glpn-kitti",
     type=str,
     help="Name of the model in case you're pushing to the hub.",
 )
 SCREAMING_SNAKE_CASE			: str				   =					parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    _lowercase			:				Tuple 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Any 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Dict 						=						True if 'large' in model_name or 'huge' in model_name else False

    if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
        if "fl3" in model_name:
            _lowercase			:				Any 						=						[3, 3, 3, 3]
            _lowercase			:				Any 						=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowercase			:				Dict 						=						[4, 4, 4, 4]
            _lowercase			:				Tuple 						=						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowercase			:				str 						=						[3, 3, 3, 3]
        if "lrf" in model_name:
            _lowercase			:				Optional[int] 						=						[3, 3, 3, 3]
        else:
            _lowercase			:				Dict 						=						[2, 2, 2, 2]

    if "tiny" in model_name:
        _lowercase			:				List[str] 						=						96
    elif "small" in model_name:
        _lowercase			:				Dict 						=						96
    elif "base" in model_name:
        _lowercase			:				Optional[int] 						=						128
    elif "large" in model_name:
        _lowercase			:				List[Any] 						=						192
    elif "xlarge" in model_name:
        _lowercase			:				Optional[Any] 						=						256
    elif "huge" in model_name:
        _lowercase			:				Dict 						=						352

    # set label information
    _lowercase			:				int 						=						'huggingface/label-files'
    if "large" in model_name or "huge" in model_name:
        _lowercase			:				str 						=						'imagenet-22k-id2label.json'
    else:
        _lowercase			:				Tuple 						=						'imagenet-1k-id2label.json'

    _lowercase			:				Union[str, Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    _lowercase			:				Optional[Any] 						=						FocalNetConfig(
        embed_dim=lowerCamelCase_				,					depths=lowerCamelCase_				,					focal_levels=lowerCamelCase_				,					focal_windows=lowerCamelCase_				,					use_conv_embed=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid=lowerCamelCase_				,					use_post_layernorm=lowerCamelCase_				,					use_layerscale=lowerCamelCase_				,					)

    return config



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    if "patch_embed.proj" in name:
        _lowercase			:				Optional[Any] 						=						name.replace('patch_embed.proj'				,					'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        _lowercase			:				str 						=						name.replace('patch_embed.norm'				,					'embeddings.norm'					)
    if "layers" in name:
        _lowercase			:				Any 						=						'encoder.' + name
    if "encoder.layers" in name:
        _lowercase			:				int 						=						name.replace('encoder.layers'				,					'encoder.stages'					)
    if "downsample.proj" in name:
        _lowercase			:				Tuple 						=						name.replace('downsample.proj'				,					'downsample.projection'					)
    if "blocks" in name:
        _lowercase			:				str 						=						name.replace('blocks'				,					'layers'					)
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        _lowercase			:				List[str] 						=						name.replace('modulation.f'				,					'modulation.projection_in'					)
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        _lowercase			:				int 						=						name.replace('modulation.h'				,					'modulation.projection_context'					)
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        _lowercase			:				Any 						=						name.replace('modulation.proj'				,					'modulation.projection_out'					)

    if name == "norm.weight":
        _lowercase			:				Any 						=						'layernorm.weight'
    if name == "norm.bias":
        _lowercase			:				Tuple 						=						'layernorm.bias'

    if "head" in name:
        _lowercase			:				Optional[int] 						=						name.replace('head'				,					'classifier'					)
    else:
        _lowercase			:				Optional[int] 						=						'focalnet.' + name

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    # fmt: off
    _lowercase			:				Dict 						=						{
        'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
        'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
        'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
        'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
        'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
        'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
        'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
        'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
        'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
        'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
    }
    # fmt: on

    _lowercase			:				Dict 						=						model_name_to_url[model_name]
    print('Checkpoint URL: '				,					lowerCamelCase_					)
    _lowercase			:				List[str] 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    # rename keys
    for key in state_dict.copy().keys():
        _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						val

    _lowercase			:				Union[str, Any] 						=						get_focalnet_config(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						FocalNetForImageClassification(lowerCamelCase_					)
    model.eval()

    # load state dict
    model.load_state_dict(lowerCamelCase_					)

    # verify conversion
    _lowercase			:				Optional[Any] 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowercase			:				Any 						=						BitImageProcessor(
        do_resize=lowerCamelCase_				,					size={'shortest_edge': 256}				,					resample=PILImageResampling.BILINEAR				,					do_center_crop=lowerCamelCase_				,					crop_size=224				,					do_normalize=lowerCamelCase_				,					image_mean=lowerCamelCase_				,					image_std=lowerCamelCase_				,					)
    _lowercase			:				List[str] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[Any] 						=						processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    _lowercase			:				str 						=						transforms.Compose(
        [
            transforms.Resize(256					),
            transforms.CenterCrop(224					),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,					std=[0.2_29, 0.2_24, 0.2_25]					),
        ]					)

    _lowercase			:				List[str] 						=						image_transforms(lowerCamelCase_					).unsqueeze(0					)

    # verify pixel_values
    assert torch.allclose(inputs.pixel_values				,					lowerCamelCase_				,					atol=1e-4					)

    _lowercase			:				Dict 						=						model(**lowerCamelCase_					)

    _lowercase			:				int 						=						outputs.logits.argmax(-1					).item()
    print('Predicted class:'				,					model.config.idalabel[predicted_class_idx]					)

    print('First values of logits:'				,					outputs.logits[0, :3]					)

    if model_name == "focalnet-tiny":
        _lowercase			:				Optional[Any] 						=						torch.tensor([0.21_66, -0.43_68, 0.21_91]					)
    elif model_name == "focalnet-tiny-lrf":
        _lowercase			:				int 						=						torch.tensor([1.16_69, 0.01_25, -0.16_95]					)
    elif model_name == "focalnet-small":
        _lowercase			:				str 						=						torch.tensor([0.49_17, -0.04_30, 0.13_41]					)
    elif model_name == "focalnet-small-lrf":
        _lowercase			:				Any 						=						torch.tensor([-0.25_88, -0.53_42, -0.23_31]					)
    elif model_name == "focalnet-base":
        _lowercase			:				List[Any] 						=						torch.tensor([-0.16_55, -0.40_90, -0.17_30]					)
    elif model_name == "focalnet-base-lrf":
        _lowercase			:				int 						=						torch.tensor([0.53_06, -0.04_83, -0.39_28]					)
    assert torch.allclose(outputs.logits[0, :3]				,					lowerCamelCase_				,					atol=1e-4					)
    print('Looks ok!'					)

    if pytorch_dump_folder_path is not None:
        print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''					)
        model.save_pretrained(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

    if push_to_hub:
        print(F'''Pushing model and processor of {model_name} to the hub...'''					)
        model.push_to_hub(F'''{model_name}'''					)
        processor.push_to_hub(F'''{model_name}'''					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: int				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="focalnet-tiny",
     type=str,
     help="Name of the FocalNet model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
     help="Whether to push the model and processor to the hub.",
 )

 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					parser.parse_args()
 convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


