



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
a__   :					Union[str, Any] 						=      logging.get_logger()



@dataclass
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	nn.Module
   A     :	List[nn.Module]												=     field(default_factory=lowerCamelCase						)
   A     :	list												=     field(default_factory=lowerCamelCase						)


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Tensor      ,					lowerCAmelCase	:      Tensor)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	len(list(m.modules())) == 1 or isinstance(lowerCAmelCase      ,					nn.Convad) or isinstance(lowerCAmelCase      ,					nn.BatchNormad)
         if has_not_submodules:
               self.traced.append(lowerCAmelCase)


   def __call__(    self	:      List[Any]      ,					lowerCAmelCase	:      Tensor)						->					List[str]:




         """simple docstring"""

         for m in self.module.modules():
               self.handles.append(m.register_forward_hook(self._forward_hook))
         self.module(lowerCAmelCase)
         [x.remove() for x in self.handles]
         return self



   @property
   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return list(filter(lambda lowerCAmelCase: len(list(x.state_dict().keys())) > 0      ,					self.traced))



@dataclass
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	nn.Module
   A     :	nn.Module
   A     :	int												=     0
   A     :	List												=     field(default_factory=lowerCamelCase						)
   A     :	List												=     field(default_factory=lowerCamelCase						)


   def __call__(    self	:      List[Any]      ,					lowerCAmelCase	:      Tensor)						->					str:




         """simple docstring"""

         lowercase__  =	Tracker(self.dest)(lowerCAmelCase).parametrized
         lowercase__  =	Tracker(self.src)(lowerCAmelCase).parametrized

         lowercase__  =	list(filter(lambda lowerCAmelCase: type(lowerCAmelCase) not in self.src_skip      ,					lowerCAmelCase))
         lowercase__  =	list(filter(lambda lowerCAmelCase: type(lowerCAmelCase) not in self.dest_skip      ,					lowerCAmelCase))

         if len(lowerCAmelCase) != len(lowerCAmelCase):
               raise Exception(
                   f'''Numbers of operations are different. Source module has {len(lowerCAmelCase)} operations while'''
                   f''' destination module has {len(lowerCAmelCase)}.''')

         for dest_m, src_m in zip(lowerCAmelCase      ,					lowerCAmelCase):
               dest_m.load_state_dict(src_m.state_dict())
               if self.verbose == 1:
                     print(f'''Transfered from={src_m} to={dest_m}''')

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__ = True						):
      print(F'''Converting {name}...'''						)
      with torch.no_grad():
            lowercase__  =	timm.create_model(A__    ,	pretrained=A__						).eval()
            lowercase__  =	ResNetForImageClassification(A__						).eval()
            lowercase__  =	ModuleTransfer(src=A__    ,	dest=A__						)
            lowercase__  =	torch.randn((1, 3, 224, 224)						)
            module_transfer(A__						)

      assert torch.allclose(from_model(A__						)    ,	our_model(A__						).logits						), "The model logits don't match the original one."

      lowercase__  =	F'''resnet{'-'.join(name.split('resnet'						)						)}'''
      print(A__						)

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name    ,	commit_message='Add model'    ,	use_temp_dir=A__    ,	)

            # we can use the convnext one
            lowercase__  =	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name    ,	commit_message='Add image processor'    ,	use_temp_dir=A__    ,	)

            print(F'''Pushed {checkpoint_name}'''						)

def 	_lowerCAmelCase (    A__    ,	A__ = None    ,	A__ = True						):
      lowercase__  =	'imagenet-1k-id2label.json'
      lowercase__  =	1_000
      lowercase__  =	(1, num_labels)

      lowercase__  =	'huggingface/label-files'
      lowercase__  =	num_labels
      lowercase__  =	json.load(open(hf_hub_download(A__    ,	A__    ,	repo_type='dataset'						)    ,	'r'						)						)
      lowercase__  =	{int(A__						): v for k, v in idalabel.items()}

      lowercase__  =	idalabel
      lowercase__  =	{v: k for k, v in idalabel.items()}

      lowercase__  =	partial(A__    ,	num_labels=A__    ,	idalabel=A__    ,	labelaid=A__						)

      lowercase__  =	{
          'resnet18': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]    ,	hidden_sizes=[64, 128, 256, 512]    ,	layer_type='basic'						),
          'resnet26': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]    ,	hidden_sizes=[256, 512, 1_024, 2_048]    ,	layer_type='bottleneck'						),
          'resnet34': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]    ,	hidden_sizes=[64, 128, 256, 512]    ,	layer_type='basic'						),
          'resnet50': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]    ,	hidden_sizes=[256, 512, 1_024, 2_048]    ,	layer_type='bottleneck'						),
          'resnet101': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]    ,	hidden_sizes=[256, 512, 1_024, 2_048]    ,	layer_type='bottleneck'						),
          'resnet152': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]    ,	hidden_sizes=[256, 512, 1_024, 2_048]    ,	layer_type='bottleneck'						),
      }

      if model_name:
            convert_weight_and_push(A__    ,	names_to_config[model_name]    ,	A__    ,	A__						)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(A__    ,	A__    ,	A__    ,	A__						)
      return config, expected_shape


if __name__ == "__main__":
       a__   :					List[str] 						=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default=None,
           type=str,
           help=(
               "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
               " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=Path,
           required=True,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument(
           "--push_to_hub",
           default=True,
           type=bool,
           required=False,
           help="If True, push model and image processor to the hub.",
       )

       a__   :					Union[str, Any] 						=      parser.parse_args()
       a__   :					Path 						=      args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	_lowerCAmelCase (    A__						):
      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 	_lowerCAmelCase (    A__						):
      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 	_lowerCAmelCase (    A__    ,	A__						):
      for key in orig_state_dict.copy().keys():
            lowercase__  =	orig_state_dict.pop(A__						)

            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 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	torch.load(A__    ,	map_location='cpu'						)['model']

      lowercase__  =	get_swin_config(A__						)
      lowercase__  =	SwinForMaskedImageModeling(A__						)
      model.eval()

      lowercase__  =	convert_state_dict(A__    ,	A__						)
      model.load_state_dict(A__						)

      lowercase__  =	'http://images.cocodataset.org/val2017/000000039769.jpg'

      lowercase__  =	ViTImageProcessor(size={'height': 192, 'width': 192}						)
      lowercase__  =	Image.open(requests.get(A__    ,	stream=A__						).raw						)
      lowercase__  =	image_processor(images=A__    ,	return_tensors='pt'						)

      with torch.no_grad():
            lowercase__  =	model(**A__						).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(A__						)

            print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
            image_processor.save_pretrained(A__						)

      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__":
       a__   :					Optional[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."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
