


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=False					)       ->		Tuple:
    _lowercase			:				Tuple 						=						[]
    for i in range(config.num_hidden_layers					):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')					)
        rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')					)
        rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')					)
        rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')					)
        rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')					)
        rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')					)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('cls_token', 'deit.embeddings.cls_token'),
            ('dist_token', 'deit.embeddings.distillation_token'),
            ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
            ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
            ('pos_embed', 'deit.embeddings.position_embeddings'),
        ]					)

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('norm.weight', 'layernorm.weight'),
                ('norm.bias', 'layernorm.bias'),
                ('pre_logits.fc.weight', 'pooler.dense.weight'),
                ('pre_logits.fc.bias', 'pooler.dense.bias'),
            ]					)

        # if just the base model, we should remove "deit" from all keys that start with "deit"
        _lowercase			:				List[str] 						=						[(pair[0], pair[1][4:]) if pair[1].startswith('deit'					) else pair for pair in rename_keys]
    else:
        # layernorm + classification heads
        rename_keys.extend(
            [
                ('norm.weight', 'deit.layernorm.weight'),
                ('norm.bias', 'deit.layernorm.bias'),
                ('head.weight', 'cls_classifier.weight'),
                ('head.bias', 'cls_classifier.bias'),
                ('head_dist.weight', 'distillation_classifier.weight'),
                ('head_dist.bias', 'distillation_classifier.bias'),
            ]					)

    return rename_keys



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		Optional[Any]:
    for i in range(config.num_hidden_layers					):
        if base_model:
            _lowercase			:				Dict 						=						''
        else:
            _lowercase			:				Union[str, Any] 						=						'deit.'
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _lowercase			:				Union[str, Any] 						=						state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''					)
        _lowercase			:				int 						=						state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''					)
        # next, add query, keys and values (in that order) to the state dict
        _lowercase			:				Union[str, Any] 						=						in_proj_weight[
            : config.hidden_size, :
        ]
        _lowercase			:				List[Any] 						=						in_proj_bias[: config.hidden_size]
        _lowercase			:				int 						=						in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _lowercase			:				Dict 						=						in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _lowercase			:				Tuple 						=						in_proj_weight[
            -config.hidden_size :, :
        ]
        _lowercase			:				List[str] 						=						in_proj_bias[-config.hidden_size :]



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    _lowercase			:				Any 						=						dct.pop(lowerCamelCase_					)
    _lowercase			:				int 						=						val



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



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    _lowercase			:				Tuple 						=						DeiTConfig()
    # all deit models have fine-tuned heads
    _lowercase			:				List[Any] 						=						False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    _lowercase			:				int 						=						1000
    _lowercase			:				List[str] 						=						'huggingface/label-files'
    _lowercase			:				List[str] 						=						'imagenet-1k-id2label.json'
    _lowercase			:				Optional[Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				Tuple 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				List[str] 						=						idalabel
    _lowercase			:				Tuple 						=						{v: k for k, v in idalabel.items()}
    _lowercase			:				Optional[Any] 						=						int(deit_name[-6:-4]					)
    _lowercase			:				Union[str, Any] 						=						int(deit_name[-3:]					)
    # size of the architecture
    if deit_name[9:].startswith('tiny'					):
        _lowercase			:				List[Any] 						=						192
        _lowercase			:				Union[str, Any] 						=						768
        _lowercase			:				Union[str, Any] 						=						12
        _lowercase			:				List[str] 						=						3
    elif deit_name[9:].startswith('small'					):
        _lowercase			:				Tuple 						=						384
        _lowercase			:				List[str] 						=						1536
        _lowercase			:				List[Any] 						=						12
        _lowercase			:				List[Any] 						=						6
    if deit_name[9:].startswith('base'					):
        pass
    elif deit_name[4:].startswith('large'					):
        _lowercase			:				List[Any] 						=						1024
        _lowercase			:				Tuple 						=						4096
        _lowercase			:				Union[str, Any] 						=						24
        _lowercase			:				Optional[Any] 						=						16

    # load original model from timm
    _lowercase			:				Union[str, Any] 						=						timm.create_model(lowerCamelCase_				,					pretrained=lowerCamelCase_					)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    _lowercase			:				List[str] 						=						timm_model.state_dict()
    _lowercase			:				List[Any] 						=						create_rename_keys(lowerCamelCase_				,					lowerCamelCase_					)
    for src, dest in rename_keys:
        rename_key(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    read_in_q_k_v(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # load HuggingFace model
    _lowercase			:				Dict 						=						DeiTForImageClassificationWithTeacher(lowerCamelCase_					).eval()
    model.load_state_dict(lowerCamelCase_					)

    # Check outputs on an image, prepared by DeiTImageProcessor
    _lowercase			:				str 						=						int(
        (256 / 224) * config.image_size					)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    _lowercase			:				Optional[Any] 						=						DeiTImageProcessor(size=lowerCamelCase_				,					crop_size=config.image_size					)
    _lowercase			:				List[str] 						=						image_processor(images=prepare_img()				,					return_tensors='pt'					)
    _lowercase			:				Union[str, Any] 						=						encoding['pixel_values']
    _lowercase			:				Tuple 						=						model(lowerCamelCase_					)

    _lowercase			:				Optional[int] 						=						timm_model(lowerCamelCase_					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(lowerCamelCase_				,					outputs.logits				,					atol=1e-3					)

    Path(lowerCamelCase_					).mkdir(exist_ok=lowerCamelCase_					)
    print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(lowerCamelCase_					)
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
    image_processor.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--deit_name",
     default="vit_deit_base_distilled_patch16_224",
     type=str,
     help="Name of the DeiT timm 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."
 )

 SCREAMING_SNAKE_CASE			: List[Any]				   =					parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






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)


