


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      )	->   Dict:
      a__	:  Optional[int]     =   [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
      a__	:  Optional[int]     =   True if "large" in model_name or "huge" in model_name else False
      a__	:  List[str]     =   True if "large" in model_name or "huge" in model_name else False
      a__	:  int     =   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__	:  Optional[int]     =   [5, 5, 5, 5]
            elif "fl4" in model_name:
                  a__	:  int     =   [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__	:  Union[str, Any]     =   [3, 3, 3, 3]
            if "lrf" in model_name:
                  a__	:  Any     =   [3, 3, 3, 3]
            else:
                  a__	:  Dict     =   [2, 2, 2, 2]

      if "tiny" in model_name:
            a__	:  Dict     =   96
      elif "small" in model_name:
            a__	:  Dict     =   96
      elif "base" in model_name:
            a__	:  Dict     =   128
      elif "large" in model_name:
            a__	:  Tuple     =   192
      elif "xlarge" in model_name:
            a__	:  Union[str, Any]     =   256
      elif "huge" in model_name:
            a__	:  str     =   352

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

      a__	:  Tuple     =   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__	:  Optional[Any]     =   {v: k for k, v in idalabel.items()}

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

      if name == "norm.weight":
            a__	:  str     =   "layernorm.weight"
      if name == "norm.bias":
            a__	:  List[str]     =   "layernorm.bias"

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

      return name



def UpperCamelCase_				(				__a			,				__a			,				__a=False      )	->   List[Any]:
      # fmt: off
      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__	:  Any     =   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__	:  Optional[int]     =   state_dict.pop(__a      )
            a__	:  Optional[Any]     =   val

      a__	:  int     =   get_focalnet_config(__a      )
      a__	:  Tuple     =   FocalNetForImageClassification(__a      )
      model.eval()

      # load state dict
      model.load_state_dict(__a      )

      # verify conversion
      a__	:  Optional[int]     =   "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__	:  Optional[int]     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )
      a__	:  Union[str, Any]     =   processor(images=__a			,				return_tensors="pt"      )

      a__	:  Optional[int]     =   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]      ),
          ]      )

      a__	:  List[Any]     =   image_transforms(__a      ).unsqueeze(0      )

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

      a__	:  Tuple     =   model(**__a      )

      a__	:  Dict     =   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__	:  str     =   torch.tensor([0.2166, -0.4368, 0.2191]      )
      elif model_name == "focalnet-tiny-lrf":
            a__	:  int     =   torch.tensor([1.1669, 0.0125, -0.1695]      )
      elif model_name == "focalnet-small":
            a__	:  Dict     =   torch.tensor([0.4917, -0.0430, 0.1341]      )
      elif model_name == "focalnet-small-lrf":
            a__	:  Tuple     =   torch.tensor([-0.2588, -0.5342, -0.2331]      )
      elif model_name == "focalnet-base":
            a__	:  List[Any]     =   torch.tensor([-0.1655, -0.4090, -0.1730]      )
      elif model_name == "focalnet-base-lrf":
            a__	:  Tuple     =   torch.tensor([0.5306, -0.0483, -0.3928]      )
      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__":
    UpperCamelCase					:       Dict			    =     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.""",
    )

    UpperCamelCase					:       List[str]			    =     parser.parse_args()
    convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str] ):


							'''simple docstring'''







							create_state_space_tree(SCREAMING_SNAKE_CASE     ,    []     ,    0     ,    [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				list[int]     ,    ):


							'''simple docstring'''







							if index == len(SCREAMING_SNAKE_CASE ):
														print(SCREAMING_SNAKE_CASE )
														return

							for i in range(len(SCREAMING_SNAKE_CASE ) ):
														if not index_used[i]:
																					current_sequence.append(sequence[i] )
																					__lowerCamelCase   :   List[Any]       =True
																					create_state_space_tree(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    index + 1     ,    SCREAMING_SNAKE_CASE )
																					current_sequence.pop()
																					__lowerCamelCase   :   List[Any]       =False
_UpperCamelCase					      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

_UpperCamelCase					      =   ["A", "B", "C"]
generate_all_permutations(sequence_a)




