



"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				SwinvaConfig()
      UpperCAmelCase_     					=				swinva_name.split("_" )

      UpperCAmelCase_     					=				name_split[1]
      if "to" in name_split[3]:
            UpperCAmelCase_     					=				int(name_split[3][-3:] )
      else:
            UpperCAmelCase_     					=				int(name_split[3] )
      if "to" in name_split[2]:
            UpperCAmelCase_     					=				int(name_split[2][-2:] )
      else:
            UpperCAmelCase_     					=				int(name_split[2][6:] )

      if model_size == "tiny":
            UpperCAmelCase_     					=				96
            UpperCAmelCase_     					=				(2, 2, 6, 2)
            UpperCAmelCase_     					=				(3, 6, 12, 24)
      elif model_size == "small":
            UpperCAmelCase_     					=				96
            UpperCAmelCase_     					=				(2, 2, 18, 2)
            UpperCAmelCase_     					=				(3, 6, 12, 24)
      elif model_size == "base":
            UpperCAmelCase_     					=				128
            UpperCAmelCase_     					=				(2, 2, 18, 2)
            UpperCAmelCase_     					=				(4, 8, 16, 32)
      else:
            UpperCAmelCase_     					=				192
            UpperCAmelCase_     					=				(2, 2, 18, 2)
            UpperCAmelCase_     					=				(6, 12, 24, 48)

      if "to" in swinva_name:
            UpperCAmelCase_     					=				(12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            UpperCAmelCase_     					=				21841
            UpperCAmelCase_     					=				'''huggingface/label-files'''
            UpperCAmelCase_     					=				'''imagenet-22k-id2label.json'''
            UpperCAmelCase_     					=				json.load(open(hf_hub_download(_A      ,     _A      ,     repo_type="dataset" )      ,     "r" ) )
            UpperCAmelCase_     					=				{int(_A ): v for k, v in idalabel.items()}
            UpperCAmelCase_     					=				idalabel
            UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}

      else:
            UpperCAmelCase_     					=				1000
            UpperCAmelCase_     					=				'''huggingface/label-files'''
            UpperCAmelCase_     					=				'''imagenet-1k-id2label.json'''
            UpperCAmelCase_     					=				json.load(open(hf_hub_download(_A      ,     _A      ,     repo_type="dataset" )      ,     "r" ) )
            UpperCAmelCase_     					=				{int(_A ): v for k, v in idalabel.items()}
            UpperCAmelCase_     					=				idalabel
            UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}

      UpperCAmelCase_     					=				img_size
      UpperCAmelCase_     					=				num_classes
      UpperCAmelCase_     					=				embed_dim
      UpperCAmelCase_     					=				depths
      UpperCAmelCase_     					=				num_heads
      UpperCAmelCase_     					=				window_size

      return config
def   a__      ( lowerCAmelCase__ ):
      if "patch_embed.proj" in name:
            UpperCAmelCase_     					=				name.replace("patch_embed.proj"      ,     "embeddings.patch_embeddings.projection" )
      if "patch_embed.norm" in name:
            UpperCAmelCase_     					=				name.replace("patch_embed.norm"      ,     "embeddings.norm" )
      if "layers" in name:
            UpperCAmelCase_     					=				'''encoder.''' + name
      if "attn.proj" in name:
            UpperCAmelCase_     					=				name.replace("attn.proj"      ,     "attention.output.dense" )
      if "attn" in name:
            UpperCAmelCase_     					=				name.replace("attn"      ,     "attention.self" )
      if "norm1" in name:
            UpperCAmelCase_     					=				name.replace("norm1"      ,     "layernorm_before" )
      if "norm2" in name:
            UpperCAmelCase_     					=				name.replace("norm2"      ,     "layernorm_after" )
      if "mlp.fc1" in name:
            UpperCAmelCase_     					=				name.replace("mlp.fc1"      ,     "intermediate.dense" )
      if "mlp.fc2" in name:
            UpperCAmelCase_     					=				name.replace("mlp.fc2"      ,     "output.dense" )
      if "q_bias" in name:
            UpperCAmelCase_     					=				name.replace("q_bias"      ,     "query.bias" )
      if "k_bias" in name:
            UpperCAmelCase_     					=				name.replace("k_bias"      ,     "key.bias" )
      if "v_bias" in name:
            UpperCAmelCase_     					=				name.replace("v_bias"      ,     "value.bias" )
      if "cpb_mlp" in name:
            UpperCAmelCase_     					=				name.replace("cpb_mlp"      ,     "continuous_position_bias_mlp" )
      if name == "norm.weight":
            UpperCAmelCase_     					=				'''layernorm.weight'''
      if name == "norm.bias":
            UpperCAmelCase_     					=				'''layernorm.bias'''

      if "head" in name:
            UpperCAmelCase_     					=				name.replace("head"      ,     "classifier" )
      else:
            UpperCAmelCase_     					=				'''swinv2.''' + name

      return name
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_     					=				orig_state_dict.pop(_A )

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  UpperCAmelCase_     					=				key.split("." )
                  UpperCAmelCase_     					=				int(key_split[1] )
                  UpperCAmelCase_     					=				int(key_split[3] )
                  UpperCAmelCase_     					=				model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        UpperCAmelCase_     					=				val[:dim, :]
                        UpperCAmelCase_     					=				val[dim : dim * 2, :]
                        UpperCAmelCase_     					=				val[-dim:, :]
                  else:
                        UpperCAmelCase_     					=				val[:dim]
                        UpperCAmelCase_     					=				val[
                            dim : dim * 2
                        ]
                        UpperCAmelCase_     					=				val[-dim:]
            else:
                  UpperCAmelCase_     					=				val

      return orig_state_dict
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				timm.create_model(_A      ,     pretrained=_A )
      timm_model.eval()

      UpperCAmelCase_     					=				get_swinva_config(_A )
      UpperCAmelCase_     					=				SwinvaForImageClassification(_A )
      model.eval()

      UpperCAmelCase_     					=				convert_state_dict(timm_model.state_dict()      ,     _A )
      model.load_state_dict(_A )

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

      UpperCAmelCase_     					=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"      ,     "-" ) ) )
      UpperCAmelCase_     					=				Image.open(requests.get(_A      ,     stream=_A ).raw )
      UpperCAmelCase_     					=				image_processor(images=_A      ,     return_tensors="pt" )

      UpperCAmelCase_     					=				timm_model(inputs["pixel_values"] )
      UpperCAmelCase_     					=				model(**_A ).logits

      assert torch.allclose(_A      ,     _A      ,     atol=1e-3 )

      print(f"""Saving model {swinva_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 )

      model.push_to_hub(
          repo_path_or_name=Path(_A      ,     _A )      ,     organization="nandwalritik"      ,     commit_message="Add model"      ,     )


if __name__ == "__main__":
  lowerCamelCase  			=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swinv2_name""",
      default="""swinv2_tiny_patch4_window8_256""",
      type=str,
      help="""Name of the Swinv2 timm model you\'d like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowerCamelCase  			=		parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





'''simple docstring'''

from math import factorial, radians







def __UpperCamelCase( _A       :					float	,      _A       :					int = 18	,      _A       :					int = 10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

   # Converting from degrees to radians
   UpperCAmelCase__				:					Dict 					=	radians(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	angle_in_radians
   UpperCAmelCase__				:					List[str] 					=	3
   UpperCAmelCase__				:					int 					=	-1

   for _ in range(_A     ):
      result += (b * (angle_in_radians**a)) / factorial(_A     )

      UpperCAmelCase__				:					List[str] 					=	-b  # One positive term and the next will be negative and so on...
      a += 2  # Increased by 2 for every term.

   return round(_A	,      _A     )


if __name__ == "__main__":
  __import__('doctest').testmod()




