


'''simple docstring'''

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()
__lowercase:		Optional[Any]    						=  logging.get_logger(__name__)







@dataclass
class 			UpperCAmelCase			:
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       List[nn.Module]            =       field(default_factory=SCREAMING_SNAKE_CASE__)
 _lowerCamelCase			:       list            =       field(default_factory=SCREAMING_SNAKE_CASE__)

 def 		lowercase_    (			self			:						int,       a_			:						Dict,       a_			:						Tensor,       a_			:						Tensor       ):





    """simple docstring"""



    UpperCamelCase__			    =    len(list(m.modules()       )       ) == 1 or isinstance(a_,       nn.Convad       ) or isinstance(a_,       nn.BatchNormad       )
    if has_not_submodules:
       if not isinstance(a_,       a_       ):
          self.traced.append(a_       )

 def __call__(			self			:						Tuple,       a_			:						Tensor       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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







@dataclass
class 			UpperCAmelCase			:
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       int            =       0
 _lowerCamelCase			:       List            =       field(default_factory=SCREAMING_SNAKE_CASE__)
 _lowerCamelCase			:       List            =       field(default_factory=SCREAMING_SNAKE_CASE__)

 def __call__(			self			:						List[str],       a_			:						Tensor       ):





    """simple docstring"""



    UpperCamelCase__			    =    Tracker(self.dest       )(a_       ).parametrized
    UpperCamelCase__			    =    Tracker(self.src       )(a_       ).parametrized

    UpperCamelCase__			    =    list(filter(lambda a_       : type(a_       ) not in self.src_skip,       a_       )       )
    UpperCamelCase__			    =    list(filter(lambda a_       : type(a_       ) not in self.dest_skip,       a_       )       )

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

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







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					nn.Module     ,		_UpperCamelCase   :					nn.Module )		->       nn.Module:





   '''simple docstring'''







   UpperCamelCase__			    =    from_model.state_dict()
   UpperCamelCase__			    =    our_model.state_dict()
   UpperCamelCase__			    =    our_model.config
   UpperCamelCase__			    =    []
   for stage_idx in range(len(config.hidden_sizes ) ):
      for block_id in range(config.depths[stage_idx] ):
         UpperCamelCase__			    =    F'block{stage_idx + 1}.{block_id}.layer_scale_1'
         UpperCamelCase__			    =    F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

         all_keys.append((from_key, to_key) )
         UpperCamelCase__			    =    F'block{stage_idx + 1}.{block_id}.layer_scale_2'
         UpperCamelCase__			    =    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:
      UpperCamelCase__			    =    from_state_dict.pop(_UpperCamelCase )

   our_model.load_state_dict(_UpperCamelCase )
   return our_model



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					VanConfig     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					nn.Module     ,		_UpperCamelCase   :					Path     ,		_UpperCamelCase   :					bool = True     ,		)		->       Tuple:





   '''simple docstring'''







   print(F'Downloading weights for {name}...' )
   UpperCamelCase__			    =    cached_download(_UpperCamelCase )
   print(F'Converting {name}...' )
   UpperCamelCase__			    =    torch.load(_UpperCamelCase )["state_dict"]
   from_model.load_state_dict(_UpperCamelCase )
   from_model.eval()
   with torch.no_grad():
      UpperCamelCase__			    =    VanForImageClassification(_UpperCamelCase ).eval()
      UpperCamelCase__			    =    ModuleTransfer(src=_UpperCamelCase     ,		dest=_UpperCamelCase )
      UpperCamelCase__			    =    torch.randn((1, 3, 2_24, 2_24) )
      module_transfer(_UpperCamelCase )
      UpperCamelCase__			    =    copy_parameters(_UpperCamelCase     ,		_UpperCamelCase )

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

   UpperCamelCase__			    =    name
   print(_UpperCamelCase )

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

      # we can use the convnext one
      UpperCamelCase__			    =    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=_UpperCamelCase     ,		)

      print(F'Pushed {checkpoint_name}' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Path     ,		_UpperCamelCase   :					str = None     ,		_UpperCamelCase   :					bool = True )		->       Union[str, Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    "imagenet-1k-id2label.json"
   UpperCamelCase__			    =    10_00

   UpperCamelCase__			    =    "huggingface/label-files"
   UpperCamelCase__			    =    num_labels
   UpperCamelCase__			    =    json.load(open(hf_hub_download(_UpperCamelCase     ,		_UpperCamelCase     ,		repo_type="dataset" )     ,		"r" ) )
   UpperCamelCase__			    =    {int(_UpperCamelCase ): v for k, v in idalabel.items()}

   UpperCamelCase__			    =    idalabel
   UpperCamelCase__			    =    {v: k for k, v in idalabel.items()}

   UpperCamelCase__			    =    partial(_UpperCamelCase     ,		num_labels=_UpperCamelCase     ,		idalabel=_UpperCamelCase     ,		labelaid=_UpperCamelCase )

   UpperCamelCase__			    =    {
       "van-tiny": ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]     ,		depths=[3, 3, 5, 2]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-small": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[2, 2, 4, 2]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-base": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[3, 3, 12, 3]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-large": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[3, 5, 27, 3]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
   }

   UpperCamelCase__			    =    {
       "van-tiny": van_tiny,
       "van-small": van_small,
       "van-base": van_base,
       "van-large": van_large,
   }

   UpperCamelCase__			    =    {
       "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(
          _UpperCamelCase     ,		names_to_config[model_name]     ,		checkpoint=names_to_original_checkpoints[model_name]     ,		from_model=names_to_original_models[model_name]()     ,		save_directory=_UpperCamelCase     ,		push_to_hub=_UpperCamelCase     ,		)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             _UpperCamelCase     ,		_UpperCamelCase     ,		checkpoint=names_to_original_checkpoints[model_name]     ,		from_model=names_to_original_models[model_name]()     ,		save_directory=_UpperCamelCase     ,		push_to_hub=_UpperCamelCase     ,		)


if __name__ == "__main__":
       __lowercase:		Optional[Any]    						=  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.",
       )

       __lowercase:		Tuple    						=  parser.parse_args()
       __lowercase:		Path    						=  args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       __lowercase:		Dict    						=  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)





from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      list[int | float]      )->			int | float:

       """simple docstring"""




       if len(UpperCAmelCase_      ) == 0:
              raise ValueError("""find_min() arg is an empty sequence"""      )
       a			   =nums[0]
       for num in nums:
              a			   =min(UpperCAmelCase_   ,       UpperCAmelCase_      )
       return min_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
