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 							a_					:






 '''simple docstring'''






 UpperCAmelCase_      					=   42
 UpperCAmelCase_      					=   field(default_factory=UpperCAmelCase__		)
 UpperCAmelCase_      					=   field(default_factory=UpperCAmelCase__		)




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    str     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



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




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Dict):







        '''simple docstring'''



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




 @property
 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



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






@dataclass
class 							a_					:






 '''simple docstring'''






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




 def __call__(     self   :    List[str]     ,       lowercase__   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   Tracker(self.dest)(_lowerCAmelCase).parametrized
        lowerCAmelCase__	      =   Tracker(self.src)(_lowerCAmelCase).parametrized

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

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

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






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

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

       our_model.load_state_dict(UpperCAmelCase_      )
       return our_model






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = True      ,    ):
       print(F"""Downloading weights for {name}..."""      )
       lowerCAmelCase__	      =   cached_download(UpperCAmelCase_      )
       print(F"""Converting {name}..."""      )
       lowerCAmelCase__	      =   torch.load(UpperCAmelCase_      )['state_dict']
       from_model.load_state_dict(UpperCAmelCase_      )
       from_model.eval()
       with torch.no_grad():
              lowerCAmelCase__	      =   VanForImageClassification(UpperCAmelCase_      ).eval()
              lowerCAmelCase__	      =   ModuleTransfer(src=UpperCAmelCase_      ,    dest=UpperCAmelCase_      )
              lowerCAmelCase__	      =   torch.randn((1, 3, 2_2_4, 2_2_4)      )
              module_transfer(UpperCAmelCase_      )
              lowerCAmelCase__	      =   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.'      )

       lowerCAmelCase__	      =   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
              lowerCAmelCase__	      =   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  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__ = None      ,    lowerCAmelCase__ = True      ):
       lowerCAmelCase__	      =   'imagenet-1k-id2label.json'
       lowerCAmelCase__	      =   1_0_0_0

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

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

       lowerCAmelCase__	      =   partial(UpperCAmelCase_      ,    num_labels=UpperCAmelCase_      ,    idalabel=UpperCAmelCase_      ,    labelaid=UpperCAmelCase_      )

       lowerCAmelCase__	      =   {
           '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]      ,    ),
       }

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

       lowerCAmelCase__	      =   {
           '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__":
    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)






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)
