
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     (      a_		)						->      int:







				"""simple docstring"""



				__A       =					[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 1_8, 2]
				__A       =					True if 'large' in model_name or 'huge' in model_name else False
				__A       =					True if 'large' in model_name or 'huge' in model_name else False
				__A       =					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:
												__A       =					[3, 3, 3, 3]
												__A       =					[5, 5, 5, 5]
								elif "fl4" in model_name:
												__A       =					[4, 4, 4, 4]
												__A       =					[3, 3, 3, 3]

				if "tiny" in model_name or "small" in model_name or "base" in model_name:
								__A       =					[3, 3, 3, 3]
								if "lrf" in model_name:
												__A       =					[3, 3, 3, 3]
								else:
												__A       =					[2, 2, 2, 2]

				if "tiny" in model_name:
								__A       =					9_6
				elif "small" in model_name:
								__A       =					9_6
				elif "base" in model_name:
								__A       =					1_2_8
				elif "large" in model_name:
								__A       =					1_9_2
				elif "xlarge" in model_name:
								__A       =					2_5_6
				elif "huge" in model_name:
								__A       =					3_5_2

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

				__A       =					json.load(open(hf_hub_download(lowerCamelCase_						,  lowerCamelCase_						,  repo_type="dataset"		)						,  "r"		)		)
				__A       =					{int(lowerCamelCase_		): v for k, v in idalabel.items()}
				__A       =					{v: k for k, v in idalabel.items()}

				__A       =					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     (      a_		)						->      Any:







				"""simple docstring"""



				if "patch_embed.proj" in name:
								__A       =					name.replace("patch_embed.proj"						,  "embeddings.patch_embeddings.projection"		)
				if "patch_embed.norm" in name:
								__A       =					name.replace("patch_embed.norm"						,  "embeddings.norm"		)
				if "layers" in name:
								__A       =					'encoder.' + name
				if "encoder.layers" in name:
								__A       =					name.replace("encoder.layers"						,  "encoder.stages"		)
				if "downsample.proj" in name:
								__A       =					name.replace("downsample.proj"						,  "downsample.projection"		)
				if "blocks" in name:
								__A       =					name.replace("blocks"						,  "layers"		)
				if "modulation.f.weight" in name or "modulation.f.bias" in name:
								__A       =					name.replace("modulation.f"						,  "modulation.projection_in"		)
				if "modulation.h.weight" in name or "modulation.h.bias" in name:
								__A       =					name.replace("modulation.h"						,  "modulation.projection_context"		)
				if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
								__A       =					name.replace("modulation.proj"						,  "modulation.projection_out"		)

				if name == "norm.weight":
								__A       =					'layernorm.weight'
				if name == "norm.bias":
								__A       =					'layernorm.bias'

				if "head" in name:
								__A       =					name.replace("head"						,  "classifier"		)
				else:
								__A       =					'focalnet.' + name

				return name


def 		UpperCAmelCase     (      a_						,  a_						,  a_=False		)						->      str:







				"""simple docstring"""



				__A       =					{
				    '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

				__A       =					model_name_to_url[model_name]
				print("Checkpoint URL: "						,  lowerCamelCase_		)
				__A       =					torch.hub.load_state_dict_from_url(lowerCamelCase_						,  map_location="cpu"		)['model']

				# rename keys
				for key in state_dict.copy().keys():
								__A       =					state_dict.pop(lowerCamelCase_		)
								__A       =					val

				__A       =					get_focalnet_config(lowerCamelCase_		)
				__A       =					FocalNetForImageClassification(lowerCamelCase_		)
				model.eval()

				# load state dict
				model.load_state_dict(lowerCamelCase_		)

				# verify conversion
				__A       =					'http://images.cocodataset.org/val2017/000000039769.jpg'

				__A       =					BitImageProcessor(
				    do_resize=lowerCamelCase_						,  size={"shortest_edge": 2_5_6}						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=lowerCamelCase_						,  crop_size=2_2_4						,  do_normalize=lowerCamelCase_						,  image_mean=lowerCamelCase_						,  image_std=lowerCamelCase_						,  )
				__A       =					Image.open(requests.get(lowerCamelCase_						,  stream=lowerCamelCase_		).raw		)
				__A       =					processor(images=lowerCamelCase_						,  return_tensors="pt"		)

				__A       =					transforms.Compose(
				    [
				        transforms.Resize(2_5_6		),
				        transforms.CenterCrop(2_2_4		),
				        transforms.ToTensor(),
				        transforms.Normalize(mean=[0.485, 0.456, 0.406]						,  std=[0.229, 0.224, 0.225]		),
				    ]		)

				__A       =					image_transforms(lowerCamelCase_		).unsqueeze(0		)

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

				__A       =					model(**lowerCamelCase_		)

				__A       =					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":
								__A       =					torch.tensor([0.2_166, -0.4_368, 0.2_191]		)
				elif model_name == "focalnet-tiny-lrf":
								__A       =					torch.tensor([1.1_669, 0.0_125, -0.1_695]		)
				elif model_name == "focalnet-small":
								__A       =					torch.tensor([0.4_917, -0.0_430, 0.1_341]		)
				elif model_name == "focalnet-small-lrf":
								__A       =					torch.tensor([-0.2_588, -0.5_342, -0.2_331]		)
				elif model_name == "focalnet-base":
								__A       =					torch.tensor([-0.1_655, -0.4_090, -0.1_730]		)
				elif model_name == "focalnet-base-lrf":
								__A       =					torch.tensor([0.5_306, -0.0_483, -0.3_928]		)
				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)




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)


