



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def    _snake_case (       __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		SwinConfig(image_size=1_9_2    )

      if "base" in model_name:
            UpperCAmelCase_  :	List[str]				      =		6
            UpperCAmelCase_  :	int				      =		1_2_8
            UpperCAmelCase_  :	Dict				      =		(2, 2, 1_8, 2)
            UpperCAmelCase_  :	Dict				      =		(4, 8, 1_6, 3_2)
      elif "large" in model_name:
            UpperCAmelCase_  :	List[str]				      =		1_2
            UpperCAmelCase_  :	Any				      =		1_9_2
            UpperCAmelCase_  :	Optional[Any]				      =		(2, 2, 1_8, 2)
            UpperCAmelCase_  :	int				      =		(6, 1_2, 2_4, 4_8)
      else:
            raise ValueError("Model not supported, only supports base and large variants"    )

      UpperCAmelCase_  :	List[str]				      =		window_size
      UpperCAmelCase_  :	Optional[Any]				      =		embed_dim
      UpperCAmelCase_  :	Optional[int]				      =		depths
      UpperCAmelCase_  :	Optional[int]				      =		num_heads

      return config







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      if "encoder.mask_token" in name:
            UpperCAmelCase_  :	int				      =		name.replace("encoder.mask_token"					,  "embeddings.mask_token"    )
      if "encoder.patch_embed.proj" in name:
            UpperCAmelCase_  :	Union[str, Any]				      =		name.replace("encoder.patch_embed.proj"					,  "embeddings.patch_embeddings.projection"    )
      if "encoder.patch_embed.norm" in name:
            UpperCAmelCase_  :	Optional[int]				      =		name.replace("encoder.patch_embed.norm"					,  "embeddings.norm"    )
      if "attn.proj" in name:
            UpperCAmelCase_  :	int				      =		name.replace("attn.proj"					,  "attention.output.dense"    )
      if "attn" in name:
            UpperCAmelCase_  :	Union[str, Any]				      =		name.replace("attn"					,  "attention.self"    )
      if "norm1" in name:
            UpperCAmelCase_  :	Tuple				      =		name.replace("norm1"					,  "layernorm_before"    )
      if "norm2" in name:
            UpperCAmelCase_  :	Optional[int]				      =		name.replace("norm2"					,  "layernorm_after"    )
      if "mlp.fc1" in name:
            UpperCAmelCase_  :	Optional[Any]				      =		name.replace("mlp.fc1"					,  "intermediate.dense"    )
      if "mlp.fc2" in name:
            UpperCAmelCase_  :	Tuple				      =		name.replace("mlp.fc2"					,  "output.dense"    )

      if name == "encoder.norm.weight":
            UpperCAmelCase_  :	str				      =		"layernorm.weight"
      if name == "encoder.norm.bias":
            UpperCAmelCase_  :	Dict				      =		"layernorm.bias"

      if "decoder" in name:
            pass
      else:
            UpperCAmelCase_  :	Optional[int]				      =		"swin." + name

      return name







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_  :	Union[str, Any]				      =		orig_state_dict.pop(__snake_case    )

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

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

      return orig_state_dict







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		torch.load(__snake_case					,  map_location="cpu"    )["model"]

      UpperCAmelCase_  :	Dict				      =		get_swin_config(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		SwinForMaskedImageModeling(__snake_case    )
      model.eval()

      UpperCAmelCase_  :	Any				      =		convert_state_dict(__snake_case					,  __snake_case    )
      model.load_state_dict(__snake_case    )

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

      UpperCAmelCase_  :	int				      =		ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}    )
      UpperCAmelCase_  :	Tuple				      =		Image.open(requests.get(__snake_case					,  stream=__snake_case    ).raw    )
      UpperCAmelCase_  :	str				      =		image_processor(images=__snake_case					,  return_tensors="pt"    )

      with torch.no_grad():
            UpperCAmelCase_  :	List[str]				      =		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         =			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         =			parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		FunnelConfig.from_json_file(__snake_case    )
      print(F"""Building PyTorch model from configuration: {config}"""    )
      UpperCAmelCase_  :	str				      =		FunnelBaseModel(__snake_case    ) if base_model else FunnelModel(__snake_case    )

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(__snake_case					,  __snake_case					,  __snake_case    )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""    )
      torch.save(model.state_dict()					,  __snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
   )
   __lowerCamelCase         =			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )
