





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   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


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

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

      if "tiny" in model_name:
            SCREAMING_SNAKE_CASE		=						96
      elif "small" in model_name:
            SCREAMING_SNAKE_CASE		=						96
      elif "base" in model_name:
            SCREAMING_SNAKE_CASE		=						1_28
      elif "large" in model_name:
            SCREAMING_SNAKE_CASE		=						1_92
      elif "xlarge" in model_name:
            SCREAMING_SNAKE_CASE		=						2_56
      elif "huge" in model_name:
            SCREAMING_SNAKE_CASE		=						3_52

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

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

      SCREAMING_SNAKE_CASE		=						FocalNetConfig(
          embed_dim=_SCREAMING_SNAKE_CASE ,						depths=_SCREAMING_SNAKE_CASE ,						focal_levels=_SCREAMING_SNAKE_CASE ,						focal_windows=_SCREAMING_SNAKE_CASE ,						use_conv_embed=_SCREAMING_SNAKE_CASE ,						idalabel=_SCREAMING_SNAKE_CASE ,						labelaid=_SCREAMING_SNAKE_CASE ,						use_post_layernorm=_SCREAMING_SNAKE_CASE ,						use_layerscale=_SCREAMING_SNAKE_CASE ,						)

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


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

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

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

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   Dict:


      '''simple docstring'''


      SCREAMING_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

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

      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE		=						val

      SCREAMING_SNAKE_CASE		=						get_focalnet_config(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						FocalNetForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.eval()

      # load state dict
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

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

      SCREAMING_SNAKE_CASE		=						BitImageProcessor(
          do_resize=_SCREAMING_SNAKE_CASE ,						size={"""shortest_edge""": 2_56} ,						resample=PILImageResampling.BILINEAR ,						do_center_crop=_SCREAMING_SNAKE_CASE ,						crop_size=2_24 ,						do_normalize=_SCREAMING_SNAKE_CASE ,						image_mean=_SCREAMING_SNAKE_CASE ,						image_std=_SCREAMING_SNAKE_CASE ,						)
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,						stream=_SCREAMING_SNAKE_CASE	).raw	)
      SCREAMING_SNAKE_CASE		=						processor(images=_SCREAMING_SNAKE_CASE ,						return_tensors="""pt"""	)

      SCREAMING_SNAKE_CASE		=						transforms.Compose(
          [
              transforms.Resize(2_56	),
              transforms.CenterCrop(2_24	),
              transforms.ToTensor(),
              transforms.Normalize(mean=[0.485, 0.456, 0.406] ,						std=[0.229, 0.224, 0.225]	),
          ]	)

      SCREAMING_SNAKE_CASE		=						image_transforms(_SCREAMING_SNAKE_CASE	).unsqueeze(0	)

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

      SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	)

      SCREAMING_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":
            SCREAMING_SNAKE_CASE		=						torch.tensor([0.2_166, -0.4_368, 0.2_191]	)
      elif model_name == "focalnet-tiny-lrf":
            SCREAMING_SNAKE_CASE		=						torch.tensor([1.1_669, 0.0_125, -0.1_695]	)
      elif model_name == "focalnet-small":
            SCREAMING_SNAKE_CASE		=						torch.tensor([0.4_917, -0.0_430, 0.1_341]	)
      elif model_name == "focalnet-small-lrf":
            SCREAMING_SNAKE_CASE		=						torch.tensor([-0.2_588, -0.5_342, -0.2_331]	)
      elif model_name == "focalnet-base":
            SCREAMING_SNAKE_CASE		=						torch.tensor([-0.1_655, -0.4_090, -0.1_730]	)
      elif model_name == "focalnet-base-lrf":
            SCREAMING_SNAKE_CASE		=						torch.tensor([0.5_306, -0.0_483, -0.3_928]	)
      assert torch.allclose(outputs.logits[0, :3] ,						_SCREAMING_SNAKE_CASE ,						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(_SCREAMING_SNAKE_CASE	)
            processor.save_pretrained(_SCREAMING_SNAKE_CASE	)

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









from pathlib import Path

import fire



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      for path in src_dir.iterdir():
            SCREAMING_SNAKE_CASE		=						[x.rstrip() for x in list(path.open().readlines()	)][:n]
            SCREAMING_SNAKE_CASE		=						dest_dir.joinpath(path.name	)
            print(_SCREAMING_SNAKE_CASE	)
            dest_path.open("""w"""	).write("""\n""".join(_SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
      fire.Fire(minify)


