

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 				snake_case_(_UpperCamelCase					)							->     Dict:



  """simple docstring"""






  _snake_case						=							[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
  _snake_case						=							True if '''large''' in model_name or '''huge''' in model_name else False
  _snake_case						=							True if '''large''' in model_name or '''huge''' in model_name else False
  _snake_case						=							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:
      _snake_case						=							[3, 3, 3, 3]
      _snake_case						=							[5, 5, 5, 5]
    elif "fl4" in model_name:
      _snake_case						=							[4, 4, 4, 4]
      _snake_case						=							[3, 3, 3, 3]

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

  if "tiny" in model_name:
    _snake_case						=							96
  elif "small" in model_name:
    _snake_case						=							96
  elif "base" in model_name:
    _snake_case						=							128
  elif "large" in model_name:
    _snake_case						=							192
  elif "xlarge" in model_name:
    _snake_case						=							256
  elif "huge" in model_name:
    _snake_case						=							352

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

  _snake_case						=							json.load(open(hf_hub_download(_UpperCamelCase			,	_UpperCamelCase			,	repo_type='''dataset'''					)			,	'''r'''					)					)
  _snake_case						=							{int(_UpperCamelCase					): v for k, v in idalabel.items()}
  _snake_case						=							{v: k for k, v in idalabel.items()}

  _snake_case						=							FocalNetConfig(
      embed_dim=_UpperCamelCase			,	depths=_UpperCamelCase			,	focal_levels=_UpperCamelCase			,	focal_windows=_UpperCamelCase			,	use_conv_embed=_UpperCamelCase			,	idalabel=_UpperCamelCase			,	labelaid=_UpperCamelCase			,	use_post_layernorm=_UpperCamelCase			,	use_layerscale=_UpperCamelCase			,	)

  return config



def 				snake_case_(_UpperCamelCase					)							->     str:



  """simple docstring"""






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

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

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

  return name



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=False					)							->     Dict:



  """simple docstring"""






  _snake_case						=							{
      '''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

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

  # rename keys
  for key in state_dict.copy().keys():
    _snake_case						=							state_dict.pop(_UpperCamelCase					)
    _snake_case						=							val

  _snake_case						=							get_focalnet_config(_UpperCamelCase					)
  _snake_case						=							FocalNetForImageClassification(_UpperCamelCase					)
  model.eval()

  # load state dict
  model.load_state_dict(_UpperCamelCase					)

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

  _snake_case						=							BitImageProcessor(
      do_resize=_UpperCamelCase			,	size={'''shortest_edge''': 256}			,	resample=PILImageResampling.BILINEAR			,	do_center_crop=_UpperCamelCase			,	crop_size=224			,	do_normalize=_UpperCamelCase			,	image_mean=_UpperCamelCase			,	image_std=_UpperCamelCase			,	)
  _snake_case						=							Image.open(requests.get(_UpperCamelCase			,	stream=_UpperCamelCase					).raw					)
  _snake_case						=							processor(images=_UpperCamelCase			,	return_tensors='''pt'''					)

  _snake_case						=							transforms.Compose(
      [
          transforms.Resize(256					),
          transforms.CenterCrop(224					),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.485, 0.456, 0.406]			,	std=[0.229, 0.224, 0.225]					),
      ]					)

  _snake_case						=							image_transforms(_UpperCamelCase					).unsqueeze(0					)

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

  _snake_case						=							model(**_UpperCamelCase					)

  _snake_case						=							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":
    _snake_case						=							torch.tensor([0.2166, -0.4368, 0.2191]					)
  elif model_name == "focalnet-tiny-lrf":
    _snake_case						=							torch.tensor([1.1669, 0.0125, -0.1695]					)
  elif model_name == "focalnet-small":
    _snake_case						=							torch.tensor([0.4917, -0.0430, 0.1341]					)
  elif model_name == "focalnet-small-lrf":
    _snake_case						=							torch.tensor([-0.2588, -0.5342, -0.2331]					)
  elif model_name == "focalnet-base":
    _snake_case						=							torch.tensor([-0.1655, -0.4090, -0.1730]					)
  elif model_name == "focalnet-base-lrf":
    _snake_case						=							torch.tensor([0.5306, -0.0483, -0.3928]					)
  assert torch.allclose(outputs.logits[0, :3]			,	_UpperCamelCase			,	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(_UpperCamelCase					)
    processor.save_pretrained(_UpperCamelCase					)

  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__":
 __A      		=     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.''',
 )

 __A      		=     parser.parse_args()
 convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''					)
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]  # remove the leading "0b"
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]
  _snake_case						=							max(len(_UpperCamelCase					)			,	len(_UpperCamelCase					)					)
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)					)					)
      for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase					)			,	b_binary.zfill(_UpperCamelCase					)					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

