





'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any] ):
   lowercase_			:Dict    			= SwinConfig(image_size=1_92 )

   if "base" in model_name:
      lowercase_			:List[str]    			= 6
      lowercase_			:List[str]    			= 1_28
      lowercase_			:Optional[int]    			= (2, 2, 18, 2)
      lowercase_			:Union[str, Any]    			= (4, 8, 16, 32)
   elif "large" in model_name:
      lowercase_			:Union[str, Any]    			= 12
      lowercase_			:int    			= 1_92
      lowercase_			:Any    			= (2, 2, 18, 2)
      lowercase_			:Optional[int]    			= (6, 12, 24, 48)
   else:
      raise ValueError("Model not supported, only supports base and large variants" )

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

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

   if name == "encoder.norm.weight":
      lowercase_			:List[Any]    			= "layernorm.weight"
   if name == "encoder.norm.bias":
      lowercase_			:Dict    			= "layernorm.bias"

   if "decoder" in name:
      pass
   else:
      lowercase_			:Union[str, Any]    			= "swin." + name

   return name
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Tuple ):
   for key in orig_state_dict.copy().keys():
      lowercase_			:Union[str, Any]    			= orig_state_dict.pop(__lowerCAmelCase )

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

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

   return orig_state_dict
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Tuple ):
   lowercase_			:Dict    			= torch.load(__lowerCAmelCase	,map_location="cpu" )["model"]

   lowercase_			:List[str]    			= get_swin_config(__lowerCAmelCase )
   lowercase_			:int    			= SwinForMaskedImageModeling(__lowerCAmelCase )
   model.eval()

   lowercase_			:List[Any]    			= convert_state_dict(__lowerCAmelCase	,__lowerCAmelCase )
   model.load_state_dict(__lowerCAmelCase )

   lowercase_			:Optional[Any]    			= "http://images.cocodataset.org/val2017/000000039769.jpg"

   lowercase_			:Union[str, Any]    			= ViTImageProcessor(size={"height": 1_92, "width": 1_92} )
   lowercase_			:Optional[int]    			= Image.open(requests.get(__lowerCAmelCase	,stream=__lowerCAmelCase ).raw )
   lowercase_			:List[str]    			= image_processor(images=__lowerCAmelCase	,return_tensors="pt" )

   with torch.no_grad():
      lowercase_			:Tuple    			= model(**__lowerCAmelCase ).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(__lowerCAmelCase )

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

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




import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__   :			Dict												=    {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Any        =		"""mask2former"""
		a__       :						Dict        =		["""swin"""]
		a__       :						Any        =		{"""hidden_size""": """hidden_dim"""}






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Dict] = None							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 10_24							,			SCREAMING_SNAKE_CASE__		:							str = "relu"							,			SCREAMING_SNAKE_CASE__		:							int = 6							,			SCREAMING_SNAKE_CASE__		:							int = 10							,			SCREAMING_SNAKE_CASE__		:							int = 8							,			SCREAMING_SNAKE_CASE__		:							float = 0.0							,			SCREAMING_SNAKE_CASE__		:							int = 20_48							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							int = 4							,			SCREAMING_SNAKE_CASE__		:							int = 2_55							,			SCREAMING_SNAKE_CASE__		:							int = 1_00							,			SCREAMING_SNAKE_CASE__		:							float = 0.1							,			SCREAMING_SNAKE_CASE__		:							float = 2.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							int = 1_25_44							,			SCREAMING_SNAKE_CASE__		:							float = 3.0							,			SCREAMING_SNAKE_CASE__		:							float = 0.75							,			SCREAMING_SNAKE_CASE__		:							float = 0.02							,			SCREAMING_SNAKE_CASE__		:							float = 1.0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							List[int] = [4, 8, 16, 32]							,			SCREAMING_SNAKE_CASE__		:							bool = None							,			**SCREAMING_SNAKE_CASE__		:							Tuple							,			)     ->							str:
						if backbone_config is None:
										logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''  )
										__lowerCamelCase    					=	CONFIG_MAPPING['''swin'''](
										    image_size=2_24							,			in_channels=3							,			patch_size=4							,			embed_dim=96							,			depths=[2, 2, 18, 2]							,			num_heads=[3, 6, 12, 24]							,			window_size=7							,			drop_path_rate=0.3							,			use_absolute_embeddings=SCREAMING_SNAKE_CASE__							,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,			)

						if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	backbone_config.pop('''model_type'''  )
										__lowerCamelCase    					=	CONFIG_MAPPING[backbone_model_type]
										__lowerCamelCase    					=	config_class.from_dict(SCREAMING_SNAKE_CASE__  )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
										logger.warning_once(
										    f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
										    f'''Supported model types: {','.join(self.backbones_supported  )}'''  )

						__lowerCamelCase    					=	backbone_config
						__lowerCamelCase    					=	feature_size
						__lowerCamelCase    					=	mask_feature_size
						__lowerCamelCase    					=	hidden_dim
						__lowerCamelCase    					=	encoder_feedforward_dim
						__lowerCamelCase    					=	activation_function
						__lowerCamelCase    					=	encoder_layers
						__lowerCamelCase    					=	decoder_layers
						__lowerCamelCase    					=	num_attention_heads
						__lowerCamelCase    					=	dropout
						__lowerCamelCase    					=	dim_feedforward
						__lowerCamelCase    					=	pre_norm
						__lowerCamelCase    					=	enforce_input_projection
						__lowerCamelCase    					=	common_stride
						__lowerCamelCase    					=	ignore_value
						__lowerCamelCase    					=	num_queries
						__lowerCamelCase    					=	no_object_weight
						__lowerCamelCase    					=	class_weight
						__lowerCamelCase    					=	mask_weight
						__lowerCamelCase    					=	dice_weight
						__lowerCamelCase    					=	train_num_points
						__lowerCamelCase    					=	oversample_ratio
						__lowerCamelCase    					=	importance_sample_ratio
						__lowerCamelCase    					=	init_std
						__lowerCamelCase    					=	init_xavier_std
						__lowerCamelCase    					=	use_auxiliary_loss
						__lowerCamelCase    					=	feature_strides
						__lowerCamelCase    					=	output_auxiliary_logits
						__lowerCamelCase    					=	decoder_layers

						super().__init__(**SCREAMING_SNAKE_CASE__  )






		@classmethod
		def  __A       (		cls		:							Any							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							List[Any]:
						return cls(
						    backbone_config=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							Any  )     ->							Dict[str, any]:
						__lowerCamelCase    					=	copy.deepcopy(self.__dict__  )
						__lowerCamelCase    					=	self.backbone_config.to_dict()
						__lowerCamelCase    					=	self.__class__.model_type
						return output



