
"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def       A_      (       lowercase   )  ->				Any:


     """simple docstring"""







     UpperCAmelCase_    :     List[str]            =	SwinConfig(image_size=192   )

     if "base" in model_name:
          UpperCAmelCase_    :     Optional[Any]            =	6
          UpperCAmelCase_    :     Any            =	128
          UpperCAmelCase_    :     List[str]            =	(2, 2, 18, 2)
          UpperCAmelCase_    :     Optional[int]            =	(4, 8, 16, 32)
     elif "large" in model_name:
          UpperCAmelCase_    :     Tuple            =	12
          UpperCAmelCase_    :     List[Any]            =	192
          UpperCAmelCase_    :     Any            =	(2, 2, 18, 2)
          UpperCAmelCase_    :     str            =	(6, 12, 24, 48)
     else:
          raise ValueError("""Model not supported, only supports base and large variants"""   )

     UpperCAmelCase_    :     List[Any]            =	window_size
     UpperCAmelCase_    :     Tuple            =	embed_dim
     UpperCAmelCase_    :     Union[str, Any]            =	depths
     UpperCAmelCase_    :     Any            =	num_heads

     return config




def       A_      (       lowercase   )  ->				Optional[int]:


     """simple docstring"""







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

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

     if "decoder" in name:
          pass
     else:
          UpperCAmelCase_    :     List[Any]            =	"""swin.""" + name

     return name




def       A_      (       lowercase    ,			lowercase   )  ->				str:


     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_    :     List[str]            =	orig_state_dict.pop(lowercase   )

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

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

     return orig_state_dict




def       A_      (       lowercase    ,			lowercase    ,			lowercase    ,			lowercase   )  ->				List[str]:


     """simple docstring"""







     UpperCAmelCase_    :     Optional[int]            =	torch.load(lowercase    ,			map_location="""cpu"""   )["""model"""]

     UpperCAmelCase_    :     Any            =	get_swin_config(lowercase   )
     UpperCAmelCase_    :     int            =	SwinForMaskedImageModeling(lowercase   )
     model.eval()

     UpperCAmelCase_    :     List[str]            =	convert_state_dict(lowercase    ,			lowercase   )
     model.load_state_dict(lowercase   )

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

     UpperCAmelCase_    :     Union[str, Any]            =	ViTImageProcessor(size={"""height""": 192, """width""": 192}   )
     UpperCAmelCase_    :     Optional[Any]            =	Image.open(requests.get(lowercase    ,			stream=lowercase   ).raw   )
     UpperCAmelCase_    :     List[Any]            =	image_processor(images=lowercase    ,			return_tensors="""pt"""   )

     with torch.no_grad():
          UpperCAmelCase_    :     int            =	model(**lowercase   ).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(lowercase   )

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

     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__":
       lowercase_         =       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."
       )

       lowercase_         =       parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""







import argparse
import json

from tqdm import tqdm




def       A_      (       )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path"""    ,			type=lowercase    ,			default="""biencoder-nq-dev.json"""    ,			help="""Path to raw DPR training data"""    ,			)
     parser.add_argument(
         """--evaluation_set"""    ,			type=lowercase    ,			help="""where to store parsed evaluation_set file"""    ,			)
     parser.add_argument(
         """--gold_data_path"""    ,			type=lowercase    ,			help="""where to store parsed gold_data_path file"""    ,			)
     UpperCAmelCase_    :     Dict            =	parser.parse_args()

     with open(args.src_path    ,			"""r"""   ) as src_file, open(args.evaluation_set    ,			"""w"""   ) as eval_file, open(
         args.gold_data_path    ,			"""w"""   ) as gold_file:
          UpperCAmelCase_    :     List[Any]            =	json.load(lowercase   )
          for dpr_record in tqdm(lowercase   ):
               UpperCAmelCase_    :     List[Any]            =	dpr_record["""question"""]
               UpperCAmelCase_    :     Dict            =	[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""   )
               gold_file.write("""\t""".join(lowercase   ) + """\n"""   )


if __name__ == "__main__":
       main()

