





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_ :     Optional[Any]      ):
   __lowerCAmelCase				      =      SwinvaConfig()
   __lowerCAmelCase				      =      swinva_name.split('_'      )

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

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

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

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      __lowerCAmelCase				      =      2_1841
      __lowerCAmelCase				      =      'huggingface/label-files'
      __lowerCAmelCase				      =      'imagenet-22k-id2label.json'
      __lowerCAmelCase				      =      json.load(open(hf_hub_download(lowerCAmelCase_,      lowerCAmelCase_,      repo_type='dataset'      ),      'r'      )      )
      __lowerCAmelCase				      =      {int(lowerCAmelCase_      ): v for k, v in idalabel.items()}
      __lowerCAmelCase				      =      idalabel
      __lowerCAmelCase				      =      {v: k for k, v in idalabel.items()}

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

   __lowerCAmelCase				      =      img_size
   __lowerCAmelCase				      =      num_classes
   __lowerCAmelCase				      =      embed_dim
   __lowerCAmelCase				      =      depths
   __lowerCAmelCase				      =      num_heads
   __lowerCAmelCase				      =      window_size

   return config

def     a_       (					lowerCAmelCase_ :     Any      ):






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

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

   return name

def     a_       (					lowerCAmelCase_ :     int,      lowerCAmelCase_ :     Union[str, Any]      ):






   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase				      =      orig_state_dict.pop(lowerCAmelCase_      )

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

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

   return orig_state_dict

def     a_       (					lowerCAmelCase_ :     List[Any],      lowerCAmelCase_ :     str      ):
   __lowerCAmelCase				      =      timm.create_model(lowerCAmelCase_,      pretrained=lowerCAmelCase_      )
   timm_model.eval()

   __lowerCAmelCase				      =      get_swinva_config(lowerCAmelCase_      )
   __lowerCAmelCase				      =      SwinvaForImageClassification(lowerCAmelCase_      )
   model.eval()

   __lowerCAmelCase				      =      convert_state_dict(timm_model.state_dict(),      lowerCAmelCase_      )
   model.load_state_dict(lowerCAmelCase_      )

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

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

   __lowerCAmelCase				      =      timm_model(inputs['pixel_values']      )
   __lowerCAmelCase				      =      model(**lowerCAmelCase_      ).logits

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

   print(F"""Saving model {swinva_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_      )

   model.push_to_hub(
       repo_path_or_name=Path(lowerCAmelCase_,      lowerCAmelCase_      ),      organization='nandwalritik',      commit_message='Add model',      )


if __name__ == "__main__":
     _snake_case    :       int		       =    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.'
     )

     _snake_case    :       List[Any]		       =    parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							None



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Any:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[int]:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     if not self.is_available():
        raise RuntimeError(
            F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''	)



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict	) ->			Tuple:




     """simple docstring"""
     return F'''`pip install {cls.pip_package or cls.name}`'''






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'optuna'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_optuna_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Optional[int]	) ->			Optional[int]:




     """simple docstring"""
     return run_hp_search_optuna(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     return default_hp_space_optuna(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'ray'
  _SCREAMING_SNAKE_CASE		     =							'\'ray[tune]\''



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_ray_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     return run_hp_search_ray(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			Any:




     """simple docstring"""
     return default_hp_space_ray(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'sigopt'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[str]:




     """simple docstring"""
     return is_sigopt_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Tuple	) ->			str:




     """simple docstring"""
     return run_hp_search_sigopt(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Tuple	) ->			Union[str, Any]:




     """simple docstring"""
     return default_hp_space_sigopt(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wandb'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[Any]:




     """simple docstring"""
     return is_wandb_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			Dict:




     """simple docstring"""
     return run_hp_search_wandb(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Any:




     """simple docstring"""
     return default_hp_space_wandb(_lowerCAmelCase	)






SCREAMING_SNAKE_CASE						:Union[str, Any]			=  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def       _lowerCAmelCase    (				)->str:
   '''simple docstring'''






   snake_case_            =       [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
   if len(lowerCAmelCase_				) > 0:
      snake_case_            =       available_backends[0].name
      if len(lowerCAmelCase_				) > 1:
         logger.info(
             F'''{len(lowerCAmelCase_				)} hyperparameter search backends available. Using {name} as the default.'''				)
      return name
   raise RuntimeError(
       "No hyperparameter search backend available.\n"
       + "\n".join(
           F''' - To install {backend.name} run {backend.pip_install()}'''
           for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)




