"""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()
_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)


@dataclass
class 					UpperCAmelCase_     :
      lowerCamelCase__		:   nn.Module
      lowerCamelCase__		:   List[nn.Module]			  =  field(default_factory=_a)
      lowerCamelCase__		:   list			  =  field(default_factory=_a)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> List[Any]:
             lowercase__      :		Any          =     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		,      a							)	-> str:
             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 		_UpperCAmelCase	(       self							)	-> int:
             # check the len of the state_dict keys to see if we have learnable params
             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=_a)
      lowerCamelCase__		:   List			  =  field(default_factory=_a)


      def __call__(       self		,      a							)	-> Any:
             lowercase__      :		Dict          =     Tracker(self.dest							)(a							).parametrized
             lowercase__      :		Optional[Any]          =     Tracker(self.src							)(a							).parametrized

             lowercase__      :		Optional[Any]          =     list(filter(lambda a							: type(a							) not in self.src_skip		,      a							)							)
             lowercase__      :		List[Any]          =     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     a_					(				_lowerCAmelCase  :			nn.Module       ,			_lowerCAmelCase  :			nn.Module					):





       '''simple docstring'''

       lowercase__      :		List[str]          =     from_model.state_dict()
       lowercase__      :		Any          =     our_model.state_dict()
       lowercase__      :		str          =     our_model.config
       lowercase__      :		Any          =     []
       for stage_idx in range(len(config.hidden_sizes					)					):
              for block_id in range(config.depths[stage_idx]					):
                     lowercase__      :		str          =     f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
                     lowercase__      :		List[str]          =     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

                     all_keys.append((from_key, to_key)					)
                     lowercase__      :		Tuple          =     f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
                     lowercase__      :		Any          =     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__      :		Optional[int]          =     from_state_dict.pop(_lowerCAmelCase					)

       our_model.load_state_dict(_lowerCAmelCase					)
       return our_model





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			VanConfig       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			nn.Module       ,			_lowerCAmelCase  :			Path       ,			_lowerCAmelCase  :			bool = True       ,			):





       '''simple docstring'''

       print(f"""Downloading weights for {name}..."""					)
       lowercase__      :		Tuple          =     cached_download(_lowerCAmelCase					)
       print(f"""Converting {name}..."""					)
       lowercase__      :		str          =     torch.load(_lowerCAmelCase					)['state_dict']
       from_model.load_state_dict(_lowerCAmelCase					)
       from_model.eval()
       with torch.no_grad():
              lowercase__      :		List[str]          =     VanForImageClassification(_lowerCAmelCase					).eval()
              lowercase__      :		int          =     ModuleTransfer(src=_lowerCAmelCase       ,			dest=_lowerCAmelCase					)
              lowercase__      :		Tuple          =     torch.randn((1, 3, 224, 224)					)
              module_transfer(_lowerCAmelCase					)
              lowercase__      :		Tuple          =     copy_parameters(_lowerCAmelCase       ,			_lowerCAmelCase					)

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

       lowercase__      :		Dict          =     name
       print(_lowerCAmelCase					)

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

              # we can use the convnext one
              lowercase__      :		Dict          =     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=_lowerCAmelCase       ,			)

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





def     a_					(				_lowerCAmelCase  :			Path       ,			_lowerCAmelCase  :			str = None       ,			_lowerCAmelCase  :			bool = True					):





       '''simple docstring'''

       lowercase__      :		Dict          =     'imagenet-1k-id2label.json'
       lowercase__      :		Optional[int]          =     1000

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

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

       lowercase__      :		Union[str, Any]          =     partial(_lowerCAmelCase       ,			num_labels=_lowerCAmelCase       ,			idalabel=_lowerCAmelCase       ,			labelaid=_lowerCAmelCase					)

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

       lowercase__      :		Optional[int]          =     {
           '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(
                  _lowerCAmelCase       ,			names_to_config[model_name]       ,			checkpoint=names_to_original_checkpoints[model_name]       ,			from_model=names_to_original_models[model_name]()       ,			save_directory=_lowerCAmelCase       ,			push_to_hub=_lowerCAmelCase       ,			)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         _lowerCAmelCase       ,			_lowerCAmelCase       ,			checkpoint=names_to_original_checkpoints[model_name]       ,			from_model=names_to_original_models[model_name]()       ,			save_directory=_lowerCAmelCase       ,			push_to_hub=_lowerCAmelCase       ,			)


if __name__ == "__main__":
       _UpperCamelCase       :						List[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.",
       )

       _UpperCamelCase       :						List[Any]     =   parser.parse_args()
       _UpperCamelCase       :						Path     =   args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       _UpperCamelCase       :						Union[str, Any]     =   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 ..utils import DummyObject, requires_backends


class 					UpperCAmelCase_     (   metaclass=_a):
      lowerCamelCase__		:   List[str]			  =  ["torch", "transformers", "k_diffusion"]


      def __init__(       self		,      *a		,      **a							)	-> Dict:
             requires_backends(self		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Tuple:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Any:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)

