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






@dataclass
class        UpperCAmelCase_       :

 '''simple docstring'''
 lowercase_				:						nn.Module
 lowercase_				:						List[nn.Module]				=       field(default_factory=lowercase_							)
 lowercase_				:						list				=       field(default_factory=lowercase_							)







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     Optional[Any] ,							snake_case__		:     Tensor ,							snake_case__		:     Tensor   ):



   '''simple docstring'''




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







 def __call__(				self		:     Optional[int] ,							snake_case__		:     Tensor   ):



   '''simple docstring'''




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







 @property
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




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






@dataclass
class        UpperCAmelCase_       :

 '''simple docstring'''
 lowercase_				:						nn.Module
 lowercase_				:						nn.Module
 lowercase_				:						int				=       0
 lowercase_				:						List				=       field(default_factory=lowercase_							)
 lowercase_				:						List				=       field(default_factory=lowercase_							)







 def __call__(				self		:     int ,							snake_case__		:     Tensor   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       Tracker(self.dest   )(snake_case__   ).parametrized
   UpperCAmelCase__			:				List[Any]												=       Tracker(self.src   )(snake_case__   ).parametrized

   UpperCAmelCase__			:				Optional[Any]												=       list(filter(lambda snake_case__   : type(snake_case__   ) not in self.src_skip ,							snake_case__   )   )
   UpperCAmelCase__			:				Tuple												=       list(filter(lambda snake_case__   : type(snake_case__   ) not in self.dest_skip ,							snake_case__   )   )

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

   for dest_m, src_m in zip(snake_case__ ,							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 snake_case_    (				lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Tuple												=       from_model.state_dict()
  UpperCAmelCase__			:				Optional[Any]												=       our_model.state_dict()
  UpperCAmelCase__			:				Tuple												=       our_model.config
  UpperCAmelCase__			:				List[Any]												=       []
  for stage_idx in range(len(config.hidden_sizes		)		):
    for block_id in range(config.depths[stage_idx]		):
      UpperCAmelCase__			:				int												=       F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
      UpperCAmelCase__			:				Optional[int]												=       F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

      all_keys.append((from_key, to_key)		)
      UpperCAmelCase__			:				List[str]												=       F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
      UpperCAmelCase__			:				str												=       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__			:				Optional[Any]												=       from_state_dict.pop(lowercase__		)

  our_model.load_state_dict(lowercase__		)
  return our_model







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__ = True      ,					):
  print(F"""Downloading weights for {name}..."""		)
  UpperCAmelCase__			:				Tuple												=       cached_download(lowercase__		)
  print(F"""Converting {name}..."""		)
  UpperCAmelCase__			:				Union[str, Any]												=       torch.load(lowercase__		)["state_dict"]
  from_model.load_state_dict(lowercase__		)
  from_model.eval()
  with torch.no_grad():
    UpperCAmelCase__			:				str												=       VanForImageClassification(lowercase__		).eval()
    UpperCAmelCase__			:				Union[str, Any]												=       ModuleTransfer(src=lowercase__      ,					dest=lowercase__		)
    UpperCAmelCase__			:				Optional[Any]												=       torch.randn((1, 3, 2_2_4, 2_2_4)		)
    module_transfer(lowercase__		)
    UpperCAmelCase__			:				Any												=       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."		)

  UpperCAmelCase__			:				List[str]												=       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
    UpperCAmelCase__			:				List[str]												=       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 snake_case_    (				lowercase__      ,					lowercase__ = None      ,					lowercase__ = True		):
  UpperCAmelCase__			:				Optional[Any]												=       "imagenet-1k-id2label.json"
  UpperCAmelCase__			:				Union[str, Any]												=       1_0_0_0

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

  UpperCAmelCase__			:				int												=       idalabel
  UpperCAmelCase__			:				str												=       {v: k for k, v in idalabel.items()}

  UpperCAmelCase__			:				Any												=       partial(lowercase__      ,					num_labels=lowercase__      ,					idalabel=lowercase__      ,					labelaid=lowercase__		)

  UpperCAmelCase__			:				Tuple												=       {
      "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]      ,					),
  }

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

  UpperCAmelCase__			:				Tuple												=       {
      "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__":
      SCREAMING_SNAKE_CASE    =     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.""",
      )

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





from doctest import testmod
from math import sqrt







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				list[int]												=       []
  if num < 1:
    return facs
  facs.append(1		)
  if num == 1:
    return facs
  facs.append(lowercase__		)
  for i in range(2      ,					int(sqrt(lowercase__		)		) + 1		):
    if num % i == 0:  # If i is a factor of num
      facs.append(lowercase__		)
      UpperCAmelCase__			:				Tuple												=       num // i  # num//i is the other factor of num
      if d != i:  # If d and i are distinct
        facs.append(lowercase__		)  # we have found another factor
  facs.sort()
  return facs


if __name__ == "__main__":
      testmod(name="""factors_of_a_number""", verbose=True)



