



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__   :					Optional[Any] 						=      logging.get_logger(__name__)

a__   :					Optional[Any] 						=      {
    "facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Any												=     "timesformer"


   def __init__(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Dict=2_24      ,					lowerCAmelCase	:      Tuple=16      ,					lowerCAmelCase	:      str=3      ,					lowerCAmelCase	:      Optional[Any]=8      ,					lowerCAmelCase	:      Optional[int]=7_68      ,					lowerCAmelCase	:      Dict=12      ,					lowerCAmelCase	:      Any=12      ,					lowerCAmelCase	:      Dict=30_72      ,					lowerCAmelCase	:      Any="gelu"      ,					lowerCAmelCase	:      Tuple=0.0      ,					lowerCAmelCase	:      List[Any]=0.0      ,					lowerCAmelCase	:      Union[str, Any]=0.02      ,					lowerCAmelCase	:      Dict=1E-6      ,					lowerCAmelCase	:      Tuple=True      ,					lowerCAmelCase	:      Optional[int]="divided_space_time"      ,					lowerCAmelCase	:      int=0      ,					**lowerCAmelCase	:      Any      ,					)						->					Dict:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)

         lowercase__  =	image_size
         lowercase__  =	patch_size
         lowercase__  =	num_channels
         lowercase__  =	num_frames

         lowercase__  =	hidden_size
         lowercase__  =	num_hidden_layers
         lowercase__  =	num_attention_heads
         lowercase__  =	intermediate_size
         lowercase__  =	hidden_act
         lowercase__  =	hidden_dropout_prob
         lowercase__  =	attention_probs_dropout_prob
         lowercase__  =	initializer_range
         lowercase__  =	layer_norm_eps
         lowercase__  =	qkv_bias

         lowercase__  =	attention_type
         lowercase__  =	drop_path_rate





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)
