





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_       =		logging.get_logger(__name__)
@dataclass
class 				SCREAMING_SNAKE_CASE   :
   _UpperCamelCase    :		nn.Module
   _UpperCamelCase    :		List[nn.Module] 					=       field(default_factory=lowerCAmelCase_      )
   _UpperCamelCase    :		list 					=       field(default_factory=lowerCAmelCase_      )


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[int]			,     a			:				Union[str, Any]			,     a			:				Tensor			,     a			:				Tensor       )->   Dict:





     """simple docstring"""







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


   def __call__(    self			:				str			,     a			:				Tensor       )->   int:





     """simple docstring"""







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


   @property
   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   List[Any]:





     """simple docstring"""







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






@dataclass
class 				SCREAMING_SNAKE_CASE   :
   _UpperCamelCase    :		nn.Module
   _UpperCamelCase    :		nn.Module
   _UpperCamelCase    :		int 					=       0
   _UpperCamelCase    :		List 					=       field(default_factory=lowerCAmelCase_      )
   _UpperCamelCase    :		List 					=       field(default_factory=lowerCAmelCase_      )


   def __call__(    self			:				Any			,     a			:				Tensor       )->   Union[str, Any]:





     """simple docstring"""







     lowercase__				=  Tracker(self.dest       )(_SCREAMING_SNAKE_CASE       ).parametrized
     lowercase__				=  Tracker(self.src       )(_SCREAMING_SNAKE_CASE       ).parametrized

     lowercase__				=  list(filter(lambda a       : type(_SCREAMING_SNAKE_CASE       ) not in self.src_skip			,     _SCREAMING_SNAKE_CASE       )       )
     lowercase__				=  list(filter(lambda a       : type(_SCREAMING_SNAKE_CASE       ) not in self.dest_skip			,     _SCREAMING_SNAKE_CASE       )       )

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

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






def 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)				->   nn.Module:
  # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
  lowercase__				=  from_model.state_dict()
  lowercase__				=  our_model.state_dict()
  lowercase__				=  our_model.config
  lowercase__				=  []
  for stage_idx in range(len(config.hidden_sizes		)		):
    for block_id in range(config.depths[stage_idx]		):
      lowercase__				=  F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
      lowercase__				=  F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

      all_keys.append((from_key, to_key)		)
      lowercase__				=  F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
      lowercase__				=  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:
    lowercase__				=  from_state_dict.pop(UpperCamelCase_		)

  our_model.load_state_dict(UpperCamelCase_		)
  return our_model




def 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = True		,						)				->   int:
  print(F"""Downloading weights for {name}..."""		)
  lowercase__				=  cached_download(UpperCamelCase_		)
  print(F"""Converting {name}..."""		)
  lowercase__				=  torch.load(UpperCamelCase_		)['state_dict']
  from_model.load_state_dict(UpperCamelCase_		)
  from_model.eval()
  with torch.no_grad():
    lowercase__				=  VanForImageClassification(UpperCamelCase_		).eval()
    lowercase__				=  ModuleTransfer(src=UpperCamelCase_		,						dest=UpperCamelCase_		)
    lowercase__				=  torch.randn((1, 3, 224, 224)		)
    module_transfer(UpperCamelCase_		)
    lowercase__				=  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.'		)

  lowercase__				=  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
    lowercase__				=  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 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = None		,						_SCREAMING_SNAKE_CASE = True		)				->   List[str]:
  lowercase__				=  'imagenet-1k-id2label.json'
  lowercase__				=  1000

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

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

  lowercase__				=  partial(UpperCamelCase_		,						num_labels=UpperCamelCase_		,						idalabel=UpperCamelCase_		,						labelaid=UpperCamelCase_		)

  lowercase__				=  {
      'van-tiny': ImageNetPreTrainedConfig(
          hidden_sizes=[32, 64, 160, 256]		,						depths=[3, 3, 5, 2]		,						mlp_ratios=[8, 8, 4, 4]		,						),
      'van-small': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]		,						depths=[2, 2, 4, 2]		,						mlp_ratios=[8, 8, 4, 4]		,						),
      'van-base': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]		,						depths=[3, 3, 12, 3]		,						mlp_ratios=[8, 8, 4, 4]		,						),
      'van-large': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]		,						depths=[3, 5, 27, 3]		,						mlp_ratios=[8, 8, 4, 4]		,						),
  }

  lowercase__				=  {
      'van-tiny': van_tiny,
      'van-small': van_small,
      'van-base': van_base,
      'van-large': van_large,
  }

  lowercase__				=  {
      '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_       =		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_       =		parser.parse_args()
      lowercase_       =		args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      lowercase_       =		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 math




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5    ,						int(math.sqrt(UpperCamelCase_     ) + 1     )    ,						6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def        lowerCAmelCase_       (							UpperCamelCase_ = 600851475143     )   ->						int:

       try:
              UpperCamelCase_      				=     int(UpperCamelCase_     )
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."     )
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."     )
       UpperCamelCase_      				=     0
       if is_prime(UpperCamelCase_     ):
              return n
       while n % 2 == 0:
              n //= 2
       if is_prime(UpperCamelCase_     ):
              return n
       for i in range(3    ,						int(math.sqrt(UpperCamelCase_     )     ) + 1    ,						2     ):
              if n % i == 0:
                     if is_prime(n // i     ):
                            UpperCamelCase_      				=     n // i
                            break
                     elif is_prime(UpperCamelCase_     ):
                            UpperCamelCase_      				=     i
       return max_number


if __name__ == "__main__":
     print(f'''{solution() = }''')

