


"""simple docstring"""







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      lowercase			(				A_       )->      int:
  '''simple docstring'''







  a						:		Optional[int]      							=			[2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
  a						:		Any      							=			True if "large" in model_name or "huge" in model_name else False
  a						:		Any      							=			True if "large" in model_name or "huge" in model_name else False
  a						:		Optional[Any]      							=			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						:		Optional[int]      							=			[3, 3, 3, 3]
      a						:		Dict      							=			[5, 5, 5, 5]
    elif "fl4" in model_name:
      a						:		List[str]      							=			[4, 4, 4, 4]
      a						:		Any      							=			[3, 3, 3, 3]

  if "tiny" in model_name or "small" in model_name or "base" in model_name:
    a						:		Tuple      							=			[3, 3, 3, 3]
    if "lrf" in model_name:
      a						:		Any      							=			[3, 3, 3, 3]
    else:
      a						:		str      							=			[2, 2, 2, 2]

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

  # set label information
  a						:		List[str]      							=			"huggingface/label-files"
  if "large" in model_name or "huge" in model_name:
    a						:		Any      							=			"imagenet-22k-id2label.json"
  else:
    a						:		Union[str, Any]      							=			"imagenet-1k-id2label.json"

  a						:		Any      							=			json.load(open(hf_hub_download(A_  ,   A_  ,   repo_type="dataset"       )  ,   "r"       )       )
  a						:		Optional[Any]      							=			{int(A_       ): v for k, v in idalabel.items()}
  a						:		List[Any]      							=			{v: k for k, v in idalabel.items()}

  a						:		List[Any]      							=			FocalNetConfig(
      embed_dim=A_  ,   depths=A_  ,   focal_levels=A_  ,   focal_windows=A_  ,   use_conv_embed=A_  ,   idalabel=A_  ,   labelaid=A_  ,   use_post_layernorm=A_  ,   use_layerscale=A_  ,   )

  return config


def      lowercase			(				A_       )->      str:
  '''simple docstring'''







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

  if name == "norm.weight":
    a						:		Optional[int]      							=			"layernorm.weight"
  if name == "norm.bias":
    a						:		Optional[int]      							=			"layernorm.bias"

  if "head" in name:
    a						:		Union[str, Any]      							=			name.replace("head"  ,   "classifier"       )
  else:
    a						:		str      							=			"focalnet." + name

  return name


def      lowercase			(				A_  ,   A_  ,   A_=False       )->      Tuple:
  '''simple docstring'''







  a						:		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

  a						:		List[str]      							=			model_name_to_url[model_name]
  print("Checkpoint URL: "  ,   A_       )
  a						:		str      							=			torch.hub.load_state_dict_from_url(A_  ,   map_location="cpu"       )["model"]

  # rename keys
  for key in state_dict.copy().keys():
    a						:		int      							=			state_dict.pop(A_       )
    a						:		Dict      							=			val

  a						:		Optional[int]      							=			get_focalnet_config(A_       )
  a						:		str      							=			FocalNetForImageClassification(A_       )
  model.eval()

  # load state dict
  model.load_state_dict(A_       )

  # verify conversion
  a						:		Tuple      							=			"http://images.cocodataset.org/val2017/000000039769.jpg"

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

  a						:		Dict      							=			transforms.Compose(
      [
          transforms.Resize(256       ),
          transforms.CenterCrop(224       ),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,   std=[0.2_2_9, 0.2_2_4, 0.2_2_5]       ),
      ]       )

  a						:		Optional[Any]      							=			image_transforms(A_       ).unsqueeze(0       )

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

  a						:		Optional[Any]      							=			model(**A_       )

  a						:		Any      							=			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						:		Dict      							=			torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]       )
  elif model_name == "focalnet-tiny-lrf":
    a						:		Union[str, Any]      							=			torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]       )
  elif model_name == "focalnet-small":
    a						:		Union[str, Any]      							=			torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]       )
  elif model_name == "focalnet-small-lrf":
    a						:		str      							=			torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]       )
  elif model_name == "focalnet-base":
    a						:		Tuple      							=			torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]       )
  elif model_name == "focalnet-base-lrf":
    a						:		Dict      							=			torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]       )
  assert torch.allclose(outputs.logits[0, :3]  ,   A_  ,   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(A_       )
    processor.save_pretrained(A_       )

  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__":
 __lowercase    					=      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.""",
 )

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














def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






