


import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				SwinConfig()
  lowerCamelCase :					Optional[int]    =				swin_name.split("_"	)

  lowerCamelCase :					Any    =				name_split[1]
  lowerCamelCase :					int    =				int(name_split[4]	)
  lowerCamelCase :					Any    =				int(name_split[3][-1]	)

  if model_size == "tiny":
    lowerCamelCase :					Tuple    =				96
    lowerCamelCase :					List[Any]    =				(2, 2, 6, 2)
    lowerCamelCase :					Any    =				(3, 6, 12, 24)
  elif model_size == "small":
    lowerCamelCase :					List[Any]    =				96
    lowerCamelCase :					List[str]    =				(2, 2, 18, 2)
    lowerCamelCase :					Dict    =				(3, 6, 12, 24)
  elif model_size == "base":
    lowerCamelCase :					Optional[int]    =				128
    lowerCamelCase :					str    =				(2, 2, 18, 2)
    lowerCamelCase :					List[Any]    =				(4, 8, 16, 32)
  else:
    lowerCamelCase :					Dict    =				192
    lowerCamelCase :					Union[str, Any]    =				(2, 2, 18, 2)
    lowerCamelCase :					List[Any]    =				(6, 12, 24, 48)

  if "in22k" in swin_name:
    lowerCamelCase :					Any    =				21841
  else:
    lowerCamelCase :					int    =				1000
    lowerCamelCase :					Tuple    =				"huggingface/label-files"
    lowerCamelCase :					List[Any]    =				"imagenet-1k-id2label.json"
    lowerCamelCase :					Tuple    =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						repo_type="dataset"	)      ,						"r"	)	)
    lowerCamelCase :					List[Any]    =				{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
    lowerCamelCase :					str    =				idalabel
    lowerCamelCase :					Dict    =				{v: k for k, v in idalabel.items()}

  lowerCamelCase :					str    =				img_size
  lowerCamelCase :					Optional[int]    =				num_classes
  lowerCamelCase :					str    =				embed_dim
  lowerCamelCase :					Tuple    =				depths
  lowerCamelCase :					Union[str, Any]    =				num_heads
  lowerCamelCase :					Union[str, Any]    =				window_size

  return config






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "patch_embed.proj" in name:
    lowerCamelCase :					Any    =				name.replace("patch_embed.proj"      ,						"embeddings.patch_embeddings.projection"	)
  if "patch_embed.norm" in name:
    lowerCamelCase :					Tuple    =				name.replace("patch_embed.norm"      ,						"embeddings.norm"	)
  if "layers" in name:
    lowerCamelCase :					Any    =				"encoder." + name
  if "attn.proj" in name:
    lowerCamelCase :					int    =				name.replace("attn.proj"      ,						"attention.output.dense"	)
  if "attn" in name:
    lowerCamelCase :					Optional[Any]    =				name.replace("attn"      ,						"attention.self"	)
  if "norm1" in name:
    lowerCamelCase :					Any    =				name.replace("norm1"      ,						"layernorm_before"	)
  if "norm2" in name:
    lowerCamelCase :					List[Any]    =				name.replace("norm2"      ,						"layernorm_after"	)
  if "mlp.fc1" in name:
    lowerCamelCase :					Any    =				name.replace("mlp.fc1"      ,						"intermediate.dense"	)
  if "mlp.fc2" in name:
    lowerCamelCase :					Optional[int]    =				name.replace("mlp.fc2"      ,						"output.dense"	)

  if name == "norm.weight":
    lowerCamelCase :					Any    =				"layernorm.weight"
  if name == "norm.bias":
    lowerCamelCase :					Union[str, Any]    =				"layernorm.bias"

  if "head" in name:
    lowerCamelCase :					Dict    =				name.replace("head"      ,						"classifier"	)
  else:
    lowerCamelCase :					Optional[Any]    =				"swin." + name

  return name






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if "mask" in key:
      continue
    elif "qkv" in key:
      lowerCamelCase :					int    =				key.split("."	)
      lowerCamelCase :					Dict    =				int(key_split[1]	)
      lowerCamelCase :					Union[str, Any]    =				int(key_split[3]	)
      lowerCamelCase :					Any    =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				timm.create_model(SCREAMING_SNAKE_CASE_      ,						pretrained=SCREAMING_SNAKE_CASE_	)
  timm_model.eval()

  lowerCamelCase :					Optional[int]    =				get_swin_config(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Union[str, Any]    =				SwinForImageClassification(SCREAMING_SNAKE_CASE_	)
  model.eval()

  lowerCamelCase :					Optional[int]    =				convert_state_dict(timm_model.state_dict()      ,						SCREAMING_SNAKE_CASE_	)
  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Dict    =				"http://images.cocodataset.org/val2017/000000039769.jpg"

  lowerCamelCase :					Dict    =				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"      ,						"-"	)	)	)
  lowerCamelCase :					Any    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  lowerCamelCase :					Tuple    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)

  lowerCamelCase :					int    =				timm_model(inputs["pixel_values"]	)
  lowerCamelCase :					Union[str, Any]    =				model(**SCREAMING_SNAKE_CASE_	).logits

  assert torch.allclose(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						atol=1E-3	)

  print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--swin_name''',
       default='''swin_tiny_patch4_window7_224''',
       type=str,
       help='''Name of the Swin 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.'''
   )

   _snake_case             =     parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



