




'''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()
lowerCamelCase_		       =						logging.get_logger(__name__)







@dataclass
class 		lowercase_    :


   """simple docstring"""

   lowerCamelCase_				  =						42
   lowerCamelCase_				  =						field(default_factory=UpperCAmelCase_   )
   lowerCamelCase_				  =						field(default_factory=UpperCAmelCase_   )



   def 							lowerCAmelCase_			(    self					:	Optional[Any]  ,      __lowerCamelCase					:	Optional[int]  ,      __lowerCamelCase					:	Union[str, Any]  ,      __lowerCamelCase					:	int    ):

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



   def __call__(    self					:	Optional[int]  ,      __lowerCamelCase					:	str    ):

      """simple docstring"""
      for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook    )    )
      self.module(__UpperCamelCase    )
      [x.remove() for x in self.handles]
      return self



   @property
   def 							lowerCAmelCase_			(    self					:	str    ):

      """simple docstring"""
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __lowerCamelCase    : len(list(x.state_dict().keys()    )    ) > 0  ,      self.traced    )    )







@dataclass
class 		lowercase_    :


   """simple docstring"""

   lowerCamelCase_				  =						42
   lowerCamelCase_				  =						42
   lowerCamelCase_				  =						0
   lowerCamelCase_				  =						field(default_factory=UpperCAmelCase_   )
   lowerCamelCase_				  =						field(default_factory=UpperCAmelCase_   )



   def __call__(    self					:	List[Any]  ,      __lowerCamelCase					:	Tuple    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      Tracker(self.dest    )(__UpperCamelCase    ).parametrized
      _SCREAMING_SNAKE_CASE       =      Tracker(self.src    )(__UpperCamelCase    ).parametrized

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

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

      for dest_m, src_m in zip(__UpperCamelCase  ,      __UpperCamelCase    ):
         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_ (							__A				: List[Any]       ,     __A				: Union[str, Any]  ) -> int:
   _SCREAMING_SNAKE_CASE       =      from_model.state_dict()
   _SCREAMING_SNAKE_CASE       =      our_model.state_dict()
   _SCREAMING_SNAKE_CASE       =      our_model.config
   _SCREAMING_SNAKE_CASE       =      []
   for stage_idx in range(len(config.hidden_sizes  )  ):
      for block_id in range(config.depths[stage_idx]  ):
         _SCREAMING_SNAKE_CASE       =      f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
         _SCREAMING_SNAKE_CASE       =      f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

         all_keys.append((from_key, to_key)  )
         _SCREAMING_SNAKE_CASE       =      f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
         _SCREAMING_SNAKE_CASE       =      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:
      _SCREAMING_SNAKE_CASE       =      from_state_dict.pop(lowercase_  )

   our_model.load_state_dict(lowercase_  )
   return our_model
def  SCREAMING_SNAKE_CASE_ (							__A				: str       ,     __A				: List[Any]       ,     __A				: List[str]       ,     __A				: str       ,     __A				: Optional[Any]       ,     __A				: List[str] = True       ,     ) -> Dict:
   print(f"""Downloading weights for {name}..."""  )
   _SCREAMING_SNAKE_CASE       =      cached_download(lowercase_  )
   print(f"""Converting {name}..."""  )
   _SCREAMING_SNAKE_CASE       =      torch.load(lowercase_  )["state_dict"]
   from_model.load_state_dict(lowercase_  )
   from_model.eval()
   with torch.no_grad():
      _SCREAMING_SNAKE_CASE       =      VanForImageClassification(lowercase_  ).eval()
      _SCREAMING_SNAKE_CASE       =      ModuleTransfer(src=lowercase_       ,     dest=lowercase_  )
      _SCREAMING_SNAKE_CASE       =      torch.randn((1, 3, 2_24, 2_24)  )
      module_transfer(lowercase_  )
      _SCREAMING_SNAKE_CASE       =      copy_parameters(lowercase_       ,     lowercase_  )

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

   _SCREAMING_SNAKE_CASE       =      name
   print(lowercase_  )

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

      # we can use the convnext one
      _SCREAMING_SNAKE_CASE       =      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=lowercase_       ,     )

      print(f"""Pushed {checkpoint_name}"""  )
def  SCREAMING_SNAKE_CASE_ (							__A				: List[Any]       ,     __A				: Tuple = None       ,     __A				: Optional[int] = True  ) -> Optional[int]:
   _SCREAMING_SNAKE_CASE       =      "imagenet-1k-id2label.json"
   _SCREAMING_SNAKE_CASE       =      10_00

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

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

   _SCREAMING_SNAKE_CASE       =      partial(lowercase_       ,     num_labels=lowercase_       ,     idalabel=lowercase_       ,     labelaid=lowercase_  )

   _SCREAMING_SNAKE_CASE       =      {
       "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]       ,     ),
   }

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

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


if __name__ == "__main__":
 lowerCamelCase_		       =						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.',
 )

 lowerCamelCase_		       =						parser.parse_args()
 lowerCamelCase_		       =						args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowerCamelCase_		       =						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)







'''simple docstring'''







import unittest
from timeit import timeit






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       first_num if first_num >= second_num else second_num
		_snake_case	       =       max_num
		while (common_mult % first_num > 0) or (common_mult % second_num > 0):
				common_mult += max_num
		return common_mult






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return b if a == 0 else greatest_common_divisor(b % a						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return first_num // greatest_common_divisor(lowercase_						,							lowercase_  ) * second_num






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       (
		    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
		)
		print(
		    "least_common_multiple_slow():"						,							timeit("least_common_multiple_slow(1000, 999)"						,							setup=lowercase_  )						,							)
		print(
		    "least_common_multiple_fast():"						,							timeit("least_common_multiple_fast(1000, 999)"						,							setup=lowercase_  )						,							)





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					lowercase__    = (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for i, (first_num, second_num) in enumerate(self.test_inputs ):
									_snake_case	       =       least_common_multiple_slow(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       least_common_multiple_fast(__UpperCamelCase,  __UpperCamelCase )
									with self.subTest(i=__UpperCamelCase ):
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )


if __name__ == "__main__":
						benchmark()
						unittest.main()





