






'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def __a (	__lowerCamelCase			:						int				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      SwinConfig(image_size=192				)

      if "base" in model_name:
            lowercase_     =      6
            lowercase_     =      128
            lowercase_     =      (2, 2, 18, 2)
            lowercase_     =      (4, 8, 16, 32)
      elif "large" in model_name:
            lowercase_     =      12
            lowercase_     =      192
            lowercase_     =      (2, 2, 18, 2)
            lowercase_     =      (6, 12, 24, 48)
      else:
            raise ValueError("Model not supported, only supports base and large variants"				)

      lowercase_     =      window_size
      lowercase_     =      embed_dim
      lowercase_     =      depths
      lowercase_     =      num_heads

      return config


def __a (	__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      if "encoder.mask_token" in name:
            lowercase_     =      name.replace("encoder.mask_token" ,		"embeddings.mask_token"				)
      if "encoder.patch_embed.proj" in name:
            lowercase_     =      name.replace("encoder.patch_embed.proj" ,		"embeddings.patch_embeddings.projection"				)
      if "encoder.patch_embed.norm" in name:
            lowercase_     =      name.replace("encoder.patch_embed.norm" ,		"embeddings.norm"				)
      if "attn.proj" in name:
            lowercase_     =      name.replace("attn.proj" ,		"attention.output.dense"				)
      if "attn" in name:
            lowercase_     =      name.replace("attn" ,		"attention.self"				)
      if "norm1" in name:
            lowercase_     =      name.replace("norm1" ,		"layernorm_before"				)
      if "norm2" in name:
            lowercase_     =      name.replace("norm2" ,		"layernorm_after"				)
      if "mlp.fc1" in name:
            lowercase_     =      name.replace("mlp.fc1" ,		"intermediate.dense"				)
      if "mlp.fc2" in name:
            lowercase_     =      name.replace("mlp.fc2" ,		"output.dense"				)

      if name == "encoder.norm.weight":
            lowercase_     =      "layernorm.weight"
      if name == "encoder.norm.bias":
            lowercase_     =      "layernorm.bias"

      if "decoder" in name:
            pass
      else:
            lowercase_     =      "swin." + name

      return name


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						List[str]				)	->							Optional[Any]:
      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            lowercase_     =      orig_state_dict.pop(_snake_case				)

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  lowercase_     =      key.split("."				)
                  lowercase_     =      int(key_split[2]				)
                  lowercase_     =      int(key_split[4]				)
                  lowercase_     =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

      return orig_state_dict


def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Dict				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      torch.load(_snake_case ,		map_location="cpu"				)["model"]

      lowercase_     =      get_swin_config(_snake_case				)
      lowercase_     =      SwinForMaskedImageModeling(_snake_case				)
      model.eval()

      lowercase_     =      convert_state_dict(_snake_case ,		_snake_case				)
      model.load_state_dict(_snake_case				)

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

      lowercase_     =      ViTImageProcessor(size={"height": 192, "width": 192}				)
      lowercase_     =      Image.open(requests.get(_snake_case ,		stream=_snake_case				).raw				)
      lowercase_     =      image_processor(images=_snake_case ,		return_tensors="pt"				)

      with torch.no_grad():
            lowercase_     =      model(**_snake_case				).logits

      print(outputs.keys()				)
      print("Looks ok!"				)

      if pytorch_dump_folder_path is not None:
            print(f'Saving model {model_name} to {pytorch_dump_folder_path}'				)
            model.save_pretrained(_snake_case				)

            print(f'Saving image processor to {pytorch_dump_folder_path}'				)
            image_processor.save_pretrained(_snake_case				)

      if push_to_hub:
            print(f'Pushing model and image processor for {model_name} to hub'				)
            model.push_to_hub(f'microsoft/{model_name}'				)
            image_processor.push_to_hub(f'microsoft/{model_name}'				)


if __name__ == "__main__":
       lowerCAmelCase_ : Union[str, Any]						  =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="swin-base-simmim-window6-192",
           type=str,
           choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
           help="Name of the Swin SimMIM model you'd like to convert.",
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       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 or not to push the converted model to the 🤗 hub."
       )

       lowerCAmelCase_ : Any						  =      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	Callable   ,		__lowerCAmelCase				:	Optional[Features] = None   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[dict] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					List[Any]:
         super().__init__(
             features=__lowerCAmelCase   ,		cache_dir=__lowerCAmelCase   ,		keep_in_memory=__lowerCAmelCase   ,		streaming=__lowerCAmelCase   ,		num_proc=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)
         lowercase_     =      Generator(
             cache_dir=__lowerCAmelCase   ,		features=__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		gen_kwargs=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[str]:
         # Build iterable dataset
         if self.streaming:
               lowercase_     =      self.builder.as_streaming_dataset(split="train")
         # Build regular (map-style) dataset
         else:
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None

               self.builder.download_and_prepare(
                   download_config=__lowerCAmelCase   ,		download_mode=__lowerCAmelCase   ,		verification_mode=__lowerCAmelCase   ,		base_path=__lowerCAmelCase   ,		num_proc=self.num_proc   ,		)
               lowercase_     =      self.builder.as_dataset(
                   split="train"   ,		verification_mode=__lowerCAmelCase   ,		in_memory=self.keep_in_memory)
         return dataset






