






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__				   =		logging.get_logger(__name__)






class 		lowercase_					(	lowercase ,  lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''maskformer-swin'''

      __snake_case       =  {
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						Tuple=224 ,					__UpperCAmelCase      :						Tuple=4 ,					__UpperCAmelCase      :						int=3 ,					__UpperCAmelCase      :						Optional[Any]=96 ,					__UpperCAmelCase      :						Tuple=[2, 2, 6, 2] ,					__UpperCAmelCase      :						Optional[Any]=[3, 6, 12, 24] ,					__UpperCAmelCase      :						Dict=7 ,					__UpperCAmelCase      :						Tuple=4.0 ,					__UpperCAmelCase      :						Tuple=True ,					__UpperCAmelCase      :						Tuple=0.0 ,					__UpperCAmelCase      :						Optional[Any]=0.0 ,					__UpperCAmelCase      :						Union[str, Any]=0.1 ,					__UpperCAmelCase      :						str="gelu" ,					__UpperCAmelCase      :						Tuple=False ,					__UpperCAmelCase      :						List[str]=0.02 ,					__UpperCAmelCase      :						List[Any]=1e-5 ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					**__UpperCAmelCase      :						str ,					)    ->List[str]:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase  )

       a		  =      image_size
       a		  =      patch_size
       a		  =      num_channels
       a		  =      embed_dim
       a		  =      depths
       a		  =      len(__UpperCAmelCase  )
       a		  =      num_heads
       a		  =      window_size
       a		  =      mlp_ratio
       a		  =      qkv_bias
       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      drop_path_rate
       a		  =      hidden_act
       a		  =      use_absolute_embeddings
       a		  =      layer_norm_eps
       a		  =      initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       a		  =      int(embed_dim * 2 ** (len(__UpperCAmelCase  ) - 1)  )
       a		  =      ['''stem'''] + [F"""stage{idx}""" for idx in range(1 ,					len(__UpperCAmelCase  ) + 1  )]
       a  ,     a		  =      get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase ,					out_indices=__UpperCAmelCase ,					stage_names=self.stage_names  )







"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A:   Optional[int]								=		logging.get_logger(__name__)

A:   Optional[int]								=		torch.device("cpu")

def       _snake_case						(						):
 UpperCAmelCase	:					List[Any]	=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase	:					Tuple	=	Image.open(requests.get(UpperCamelCase			,		stream=UpperCamelCase						).raw						)
 return im

def       _snake_case						(						UpperCamelCase						:		int						):
 if swiftformer_name == "swiftformer_xs":
  return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]						)

 elif swiftformer_name == "swiftformer_s":
  return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]						)

 elif swiftformer_name == "swiftformer_l1":
  return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]						)

 elif swiftformer_name == "swiftformer_l3":
  return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]						)

def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str						):
 UpperCAmelCase	:					int	=	dct.pop(UpperCamelCase						)
 UpperCAmelCase	:					Any	=	val

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]						):
 UpperCAmelCase	:					Optional[int]	=	[]
 for k in state_dict.keys():
  UpperCAmelCase	:					Optional[Any]	=	k
  if ".pwconv" in k:
   UpperCAmelCase	:					int	=	k_new.replace(""".pwconv"""			,		""".point_wise_conv"""						)
  if ".dwconv" in k:
   UpperCAmelCase	:					Tuple	=	k_new.replace(""".dwconv"""			,		""".depth_wise_conv"""						)
  if ".Proj." in k:
   UpperCAmelCase	:					List[Any]	=	k_new.replace(""".Proj."""			,		""".proj."""						)
  if "patch_embed" in k_new:
   UpperCAmelCase	:					Any	=	k_new.replace("""patch_embed"""			,		"""swiftformer.patch_embed.patch_embedding"""						)
  if "network" in k_new:
   UpperCAmelCase	:					int	=	k_new.split("""."""						)
   if ls[2].isdigit():
    UpperCAmelCase	:					List[Any]	=	"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]						)
   else:
    UpperCAmelCase	:					Any	=	k_new.replace("""network"""			,		"""swiftformer.encoder.network"""						)
  rename_keys.append((k, k_new)						)
 return rename_keys

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					List[Any]	=	SwiftFormerConfig()

 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 UpperCAmelCase	:					Optional[Any]	=	1000
 UpperCAmelCase	:					Tuple	=	"""huggingface/label-files"""
 UpperCAmelCase	:					List[str]	=	"""imagenet-1k-id2label.json"""
 UpperCAmelCase	:					Dict	=	json.load(open(hf_hub_download(UpperCamelCase			,		UpperCamelCase			,		repo_type="""dataset"""						)			,		"""r"""						)						)
 UpperCAmelCase	:					Dict	=	{int(UpperCamelCase						): v for k, v in idalabel.items()}
 UpperCAmelCase	:					str	=	idalabel
 UpperCAmelCase	:					Dict	=	{v: k for k, v in idalabel.items()}

 # size of the architecture
 if swiftformer_name == "swiftformer_xs":
  UpperCAmelCase	:					Any	=	[3, 3, 6, 4]
  UpperCAmelCase	:					List[str]	=	[48, 56, 112, 220]

 elif swiftformer_name == "swiftformer_s":
  UpperCAmelCase	:					Dict	=	[3, 3, 9, 6]
  UpperCAmelCase	:					Union[str, Any]	=	[48, 64, 168, 224]

 elif swiftformer_name == "swiftformer_l1":
  UpperCAmelCase	:					int	=	[4, 3, 10, 5]
  UpperCAmelCase	:					Optional[int]	=	[48, 96, 192, 384]

 elif swiftformer_name == "swiftformer_l3":
  UpperCAmelCase	:					Union[str, Any]	=	[4, 4, 12, 6]
  UpperCAmelCase	:					List[Any]	=	[64, 128, 320, 512]

 # load state_dict of original model, remove and rename some keys
 if original_ckpt:
  if original_ckpt.startswith("""https"""						):
   UpperCAmelCase	:					List[str]	=	torch.hub.load_state_dict_from_url(UpperCamelCase			,		map_location="""cpu"""			,		check_hash=UpperCamelCase						)
  else:
   UpperCAmelCase	:					Any	=	torch.load(UpperCamelCase			,		map_location="""cpu"""						)
 UpperCAmelCase	:					Optional[Any]	=	checkpoint

 UpperCAmelCase	:					Dict	=	create_rename_keys(UpperCamelCase						)
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

 # load HuggingFace model
 UpperCAmelCase	:					List[Any]	=	SwiftFormerForImageClassification(UpperCamelCase						).eval()
 hf_model.load_state_dict(UpperCamelCase						)

 # prepare test inputs
 UpperCAmelCase	:					Dict	=	prepare_img()
 UpperCAmelCase	:					Tuple	=	ViTImageProcessor.from_pretrained("""preprocessor_config"""						)
 UpperCAmelCase	:					Optional[int]	=	processor(images=UpperCamelCase			,		return_tensors="""pt"""						)

 # compare outputs from both models
 UpperCAmelCase	:					Optional[int]	=	get_expected_output(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	hf_model(inputs["""pixel_values"""]						).logits

 assert hf_logits.shape == torch.Size([1, 1000]						)
 assert torch.allclose(hf_logits[0, 0:5]			,		UpperCamelCase			,		atol=1e-3						)

 Path(UpperCamelCase						).mkdir(exist_ok=UpperCamelCase						)
 print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"						)
 hf_model.save_pretrained(UpperCamelCase						)


if __name__ == "__main__":
      A:   List[str]								=		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swiftformer_name",
          default="swiftformer_xs",
          choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
          type=str,
          help="Name of the SwiftFormer model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="./converted_outputs/",
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

      A:   str								=		parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
