

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      = logging.get_logger(__name__)



@dataclass
class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  lowerCamelCase    :			nn.Module
  lowerCamelCase    :			List[nn.Module]						      =field(default_factory=a_ )
  lowerCamelCase    :			list						      =field(default_factory=a_ )







  def        SCREAMING_SNAKE_CASE    (			self :    Tuple       ,							lowerCAmelCase :    List[str]       ,							lowerCAmelCase :    Tensor       ,							lowerCAmelCase :    Tensor    )							->	List[str]:






     """simple docstring"""







     __lowerCAmelCase  :			List[str]					    =						len(list(m.modules()    )    ) == 1 or isinstance(lowerCAmelCase       ,							nn.Convad    ) or isinstance(lowerCAmelCase       ,							nn.BatchNormad    )
     if has_not_submodules:
        if not isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
           self.traced.append(lowerCAmelCase    )







  def __call__(			self :    int       ,							lowerCAmelCase :    Tensor    )							->	List[str]:






     """simple docstring"""







     for m in self.module.modules():
        self.handles.append(m.register_forward_hook(self._forward_hook    )    )
     self.module(lowerCAmelCase    )
     [x.remove() for x in self.handles]
     return self







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	int:






     """simple docstring"""







     return list(filter(lambda lowerCAmelCase    : len(list(x.state_dict().keys()    )    ) > 0       ,							self.traced    )    )






@dataclass
class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  lowerCamelCase    :			nn.Module
  lowerCamelCase    :			nn.Module
  lowerCamelCase    :			int						      =0
  lowerCamelCase    :			List						      =field(default_factory=a_ )
  lowerCamelCase    :			List						      =field(default_factory=a_ )







  def __call__(			self :    Any       ,							lowerCAmelCase :    Tensor    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :			List[str]					    =						Tracker(self.dest    )(lowerCAmelCase    ).parametrized
     __lowerCAmelCase  :			int					    =						Tracker(self.src    )(lowerCAmelCase    ).parametrized

     __lowerCAmelCase  :			List[str]					    =						list(filter(lambda lowerCAmelCase    : type(lowerCAmelCase    ) not in self.src_skip       ,							lowerCAmelCase    )    )
     __lowerCAmelCase  :			Dict					    =						list(filter(lambda lowerCAmelCase    : type(lowerCAmelCase    ) not in self.dest_skip       ,							lowerCAmelCase    )    )

     if len(lowerCAmelCase    ) != len(lowerCAmelCase    ):
        raise Exception(
            f'''Numbers of operations are different. Source module has {len(lowerCAmelCase    )} operations while'''
            f''' destination module has {len(lowerCAmelCase    )}.'''    )

     for dest_m, src_m in zip(lowerCAmelCase       ,							lowerCAmelCase    ):
        dest_m.load_state_dict(src_m.state_dict()    )
        if self.verbose == 1:
           print(f'''Transfered from={src_m} to={dest_m}'''    )






def 	snake_case_      (__A     :						nn.Module					,   __A     :						nn.Module     )			->	nn.Module:
   # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
   __lowerCAmelCase  :			int					    =						from_model.state_dict()
   __lowerCAmelCase  :			int					    =						our_model.state_dict()
   __lowerCAmelCase  :			Optional[Any]					    =						our_model.config
   __lowerCAmelCase  :			str					    =						[]
   for stage_idx in range(len(config.hidden_sizes     )     ):
      for block_id in range(config.depths[stage_idx]     ):
         __lowerCAmelCase  :			List[Any]					    =						f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
         __lowerCAmelCase  :			Union[str, Any]					    =						f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

         all_keys.append((from_key, to_key)     )
         __lowerCAmelCase  :			Tuple					    =						f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
         __lowerCAmelCase  :			int					    =						f'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

         all_keys.append((from_key, to_key)     )

   for from_key, to_key in all_keys:
      __lowerCAmelCase  :			int					    =						from_state_dict.pop(__A     )

   our_model.load_state_dict(__A     )
   return our_model





def 	snake_case_      (__A     :						str					,   __A     :						VanConfig					,   __A     :						str					,   __A     :						nn.Module					,   __A     :						Path					,   __A     :						bool = True					,   )			->	Optional[int]:
   print(f'''Downloading weights for {name}...'''     )
   __lowerCAmelCase  :			str					    =						cached_download(__A     )
   print(f'''Converting {name}...'''     )
   __lowerCAmelCase  :			Optional[Any]					    =						torch.load(__A     )["""state_dict"""]
   from_model.load_state_dict(__A     )
   from_model.eval()
   with torch.no_grad():
      __lowerCAmelCase  :			Any					    =						VanForImageClassification(__A     ).eval()
      __lowerCAmelCase  :			Tuple					    =						ModuleTransfer(src=__A					,   dest=__A     )
      __lowerCAmelCase  :			Optional[Any]					    =						torch.randn((1, 3, 2_2_4, 2_2_4)     )
      module_transfer(__A     )
      __lowerCAmelCase  :			List[Any]					    =						copy_parameters(__A					,   __A     )

   if not torch.allclose(from_model(__A     )					,   our_model(__A     ).logits     ):
      raise ValueError("""The model logits don't match the original one."""     )

   __lowerCAmelCase  :			Optional[Any]					    =						name
   print(__A     )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name					,   commit_message="""Add model"""					,   use_temp_dir=__A					,   )

      # we can use the convnext one
      __lowerCAmelCase  :			int					    =						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""     )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name					,   commit_message="""Add image processor"""					,   use_temp_dir=__A					,   )

      print(f'''Pushed {checkpoint_name}'''     )





def 	snake_case_      (__A     :						Path					,   __A     :						str = None					,   __A     :						bool = True     )			->	int:
   __lowerCAmelCase  :			str					    =						"""imagenet-1k-id2label.json"""
   __lowerCAmelCase  :			Optional[int]					    =						1_0_0_0

   __lowerCAmelCase  :			str					    =						"""huggingface/label-files"""
   __lowerCAmelCase  :			Optional[int]					    =						num_labels
   __lowerCAmelCase  :			int					    =						json.load(open(hf_hub_download(__A					,   __A					,   repo_type="""dataset"""     )					,   """r"""     )     )
   __lowerCAmelCase  :			Optional[int]					    =						{int(__A     ): v for k, v in idalabel.items()}

   __lowerCAmelCase  :			Union[str, Any]					    =						idalabel
   __lowerCAmelCase  :			List[Any]					    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :			List[str]					    =						partial(__A					,   num_labels=__A					,   idalabel=__A					,   labelaid=__A     )

   __lowerCAmelCase  :			int					    =						{
       """van-tiny""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]					,   depths=[3, 3, 5, 2]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
       """van-small""": ImageNetPreTrainedConfig(
           hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]					,   depths=[2, 2, 4, 2]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
       """van-base""": ImageNetPreTrainedConfig(
           hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]					,   depths=[3, 3, 1_2, 3]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
       """van-large""": ImageNetPreTrainedConfig(
           hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]					,   depths=[3, 5, 2_7, 3]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
   }

   __lowerCAmelCase  :			List[Any]					    =						{
       """van-tiny""": van_tiny,
       """van-small""": van_small,
       """van-base""": van_base,
       """van-large""": van_large,
   }

   __lowerCAmelCase  :			Optional[Any]					    =						{
       """van-tiny""": (
           """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
       ),
       """van-small""": (
           """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
       ),
       """van-base""": (
           """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
       ),
       """van-large""": (
           """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
       ),
   }

   if model_name:
      convert_weight_and_push(
          __A					,   names_to_config[model_name]					,   checkpoint=names_to_original_checkpoints[model_name]					,   from_model=names_to_original_models[model_name]()					,   save_directory=__A					,   push_to_hub=__A					,   )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             __A					,   __A					,   checkpoint=names_to_original_checkpoints[model_name]					,   from_model=names_to_original_models[model_name]()					,   save_directory=__A					,   push_to_hub=__A					,   )


if __name__ == "__main__":
   __UpperCAmelCase      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model-name""",
       default=None,
       type=str,
       help=(
           """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
           """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=Path,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--van_dir""",
       required=True,
       type=Path,
       help=(
           """A path to VAN's original implementation directory. You can download from here:"""
           """ https://github.com/Visual-Attention-Network/VAN-Classification"""
       ),
   )
   parser.add_argument(
       """--push_to_hub""",
       default=True,
       type=bool,
       required=False,
       help="""If True, push model and image processor to the hub.""",
   )

   __UpperCAmelCase      = parser.parse_args()
   __UpperCAmelCase      = args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   __UpperCAmelCase      = args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





import importlib.metadata
import subprocess
import sys





def 	snake_case_      (__A     :						bool = False     )			->	str:
   __lowerCAmelCase  :			Any					    =						False
   if "IPython" in sys.modules:
      __lowerCAmelCase  :			Optional[int]					    =						"""google.colab""" in str(sys.modules["""IPython"""].get_ipython()     )

   if in_colab:
      if upgrade:
         __lowerCAmelCase  :			int					    =						["""pip""", """install""", """-U""", """torch"""]
         subprocess.run(__A					,   check=__A     )
      # get the current version of torch
      __lowerCAmelCase  :			List[Any]					    =						importlib.metadata.version("""torch"""     )
      __lowerCAmelCase  :			List[Any]					    =						torch_version[: torch_version.rindex("""."""     )]
      __lowerCAmelCase  :			int					    =						f'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
      __lowerCAmelCase  :			List[Any]					    =						["""pip""", """install""", xla_wheel]
      subprocess.run(__A					,   check=__A     )
   else:
      raise RuntimeError("""`install_xla` utility works only on google colab."""     )


