


"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							SwinConfig()
      UpperCAmelCase_      			=							swin_name.split("_"				)

      UpperCAmelCase_      			=							name_split[1]
      UpperCAmelCase_      			=							int(name_split[4]				)
      UpperCAmelCase_      			=							int(name_split[3][-1]				)

      if model_size == "tiny":
            UpperCAmelCase_      			=							96
            UpperCAmelCase_      			=							(2, 2, 6, 2)
            UpperCAmelCase_      			=							(3, 6, 12, 24)
      elif model_size == "small":
            UpperCAmelCase_      			=							96
            UpperCAmelCase_      			=							(2, 2, 18, 2)
            UpperCAmelCase_      			=							(3, 6, 12, 24)
      elif model_size == "base":
            UpperCAmelCase_      			=							128
            UpperCAmelCase_      			=							(2, 2, 18, 2)
            UpperCAmelCase_      			=							(4, 8, 16, 32)
      else:
            UpperCAmelCase_      			=							192
            UpperCAmelCase_      			=							(2, 2, 18, 2)
            UpperCAmelCase_      			=							(6, 12, 24, 48)

      if "in22k" in swin_name:
            UpperCAmelCase_      			=							21841
      else:
            UpperCAmelCase_      			=							1000
            UpperCAmelCase_      			=							"huggingface/label-files"
            UpperCAmelCase_      			=							"imagenet-1k-id2label.json"
            UpperCAmelCase_      			=							json.load(open(hf_hub_download(a__     ,      a__     ,      repo_type="dataset"				)     ,      "r"				)				)
            UpperCAmelCase_      			=							{int(a__				): v for k, v in idalabel.items()}
            UpperCAmelCase_      			=							idalabel
            UpperCAmelCase_      			=							{v: k for k, v in idalabel.items()}

      UpperCAmelCase_      			=							img_size
      UpperCAmelCase_      			=							num_classes
      UpperCAmelCase_      			=							embed_dim
      UpperCAmelCase_      			=							depths
      UpperCAmelCase_      			=							num_heads
      UpperCAmelCase_      			=							window_size

      return config
def        a__     (				lowerCAmelCase__				):
      if "patch_embed.proj" in name:
            UpperCAmelCase_      			=							name.replace("patch_embed.proj"     ,      "embeddings.patch_embeddings.projection"				)
      if "patch_embed.norm" in name:
            UpperCAmelCase_      			=							name.replace("patch_embed.norm"     ,      "embeddings.norm"				)
      if "layers" in name:
            UpperCAmelCase_      			=							"encoder." + name
      if "attn.proj" in name:
            UpperCAmelCase_      			=							name.replace("attn.proj"     ,      "attention.output.dense"				)
      if "attn" in name:
            UpperCAmelCase_      			=							name.replace("attn"     ,      "attention.self"				)
      if "norm1" in name:
            UpperCAmelCase_      			=							name.replace("norm1"     ,      "layernorm_before"				)
      if "norm2" in name:
            UpperCAmelCase_      			=							name.replace("norm2"     ,      "layernorm_after"				)
      if "mlp.fc1" in name:
            UpperCAmelCase_      			=							name.replace("mlp.fc1"     ,      "intermediate.dense"				)
      if "mlp.fc2" in name:
            UpperCAmelCase_      			=							name.replace("mlp.fc2"     ,      "output.dense"				)

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

      if "head" in name:
            UpperCAmelCase_      			=							name.replace("head"     ,      "classifier"				)
      else:
            UpperCAmelCase_      			=							"swin." + name

      return name
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_      			=							orig_state_dict.pop(a__				)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  UpperCAmelCase_      			=							key.split("."				)
                  UpperCAmelCase_      			=							int(key_split[1]				)
                  UpperCAmelCase_      			=							int(key_split[3]				)
                  UpperCAmelCase_      			=							model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        UpperCAmelCase_      			=							val[:dim, :]
                        UpperCAmelCase_      			=							val[
                            dim : dim * 2, :
                        ]
                        UpperCAmelCase_      			=							val[-dim:, :]
                  else:
                        UpperCAmelCase_      			=							val[
                            :dim
                        ]
                        UpperCAmelCase_      			=							val[
                            dim : dim * 2
                        ]
                        UpperCAmelCase_      			=							val[
                            -dim:
                        ]
            else:
                  UpperCAmelCase_      			=							val

      return orig_state_dict
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							timm.create_model(a__     ,      pretrained=a__				)
      timm_model.eval()

      UpperCAmelCase_      			=							get_swin_config(a__				)
      UpperCAmelCase_      			=							SwinForImageClassification(a__				)
      model.eval()

      UpperCAmelCase_      			=							convert_state_dict(timm_model.state_dict()     ,      a__				)
      model.load_state_dict(a__				)

      UpperCAmelCase_      			=							"http://images.cocodataset.org/val2017/000000039769.jpg"

      UpperCAmelCase_      			=							AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"     ,      "-"				)				)				)
      UpperCAmelCase_      			=							Image.open(requests.get(a__     ,      stream=a__				).raw				)
      UpperCAmelCase_      			=							image_processor(images=a__     ,      return_tensors="pt"				)

      UpperCAmelCase_      			=							timm_model(inputs["pixel_values"]				)
      UpperCAmelCase_      			=							model(**a__				).logits

      assert torch.allclose(a__     ,      a__     ,      atol=1e-3				)

      print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""				)
      model.save_pretrained(a__				)

      print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
      image_processor.save_pretrained(a__				)


if __name__ == "__main__":
  lowerCamelCase       	=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin timm 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."""
  )

  lowerCamelCase       	=						parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









"""simple docstring"""





def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__				) - ngram_size + 1				)]


if __name__ == "__main__":
  from doctest import testmod

  testmod()




