

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()
A__										=      logging.get_logger(__name__)







@dataclass
class  UpperCamelCase_      :


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			int					    =   42
  SCREAMING_SNAKE_CASE__					:			str					    =   field(default_factory=_snake_case      )
  SCREAMING_SNAKE_CASE__					:			Dict					    =   field(default_factory=_snake_case      )



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					List[Any]  ,	A_     :					Optional[Any]  ,	A_     :					Union[str, Any]						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    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     :					Optional[int]  ,	A_     :					List[str]						)       ->			List[Any]:
      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     :					Dict						)       ->			List[Any]:
      # 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_      :


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   42
  SCREAMING_SNAKE_CASE__					:			int					    =   42
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   0
  SCREAMING_SNAKE_CASE__					:			int					    =   field(default_factory=_snake_case      )
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   field(default_factory=_snake_case      )



  def __call__(						self     :					str  ,	A_     :					Optional[Any]						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    Tracker(self.dest						)(A_						).parametrized
      SCREAMING_SNAKE_CASE__         =    Tracker(self.src						)(A_						).parametrized

      SCREAMING_SNAKE_CASE__         =    list(filter(lambda A_						: type(A_						) not in self.src_skip  ,	A_						)						)
      SCREAMING_SNAKE_CASE__         =    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							(       __A			,							__A						):
    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(__A						)

    our_model.load_state_dict(__A						)
    return our_model





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A = True			,							):
    print(f"Downloading weights for {name}..."						)
    SCREAMING_SNAKE_CASE__         =    cached_download(__A						)
    print(f"Converting {name}..."						)
    SCREAMING_SNAKE_CASE__         =    torch.load(__A						)['''state_dict''']
    from_model.load_state_dict(__A						)
    from_model.eval()
    with torch.no_grad():
        SCREAMING_SNAKE_CASE__         =    VanForImageClassification(__A						).eval()
        SCREAMING_SNAKE_CASE__         =    ModuleTransfer(src=__A			,							dest=__A						)
        SCREAMING_SNAKE_CASE__         =    torch.randn((1, 3, 224, 224)						)
        module_transfer(__A						)
        SCREAMING_SNAKE_CASE__         =    copy_parameters(__A			,							__A						)

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

    SCREAMING_SNAKE_CASE__         =    name
    print(__A						)

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

        # 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=__A			,							)

        print(f"Pushed {checkpoint_name}"						)





def 				__a							(       __A			,							__A = None			,							__A = True						):
    SCREAMING_SNAKE_CASE__         =    '''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__         =    1000

    SCREAMING_SNAKE_CASE__         =    '''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__         =    num_labels
    SCREAMING_SNAKE_CASE__         =    json.load(open(hf_hub_download(__A			,							__A			,							repo_type="dataset"						)			,							"r"						)						)
    SCREAMING_SNAKE_CASE__         =    {int(__A						): 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(__A			,							num_labels=__A			,							idalabel=__A			,							labelaid=__A						)

    SCREAMING_SNAKE_CASE__         =    {
        '''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]			,							),
    }

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


if __name__ == "__main__":
  A__										=      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.",
  )

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










__lowerCamelCase						= {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase						= {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    List[Any]			=						True
							_a  :    Optional[Any]			=						[]

							for neighbour in graph[vert]:
														if not visited[neighbour]:
																					connected_verts += dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )

							return [vert, *connected_verts]






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    Optional[int]			=						len(UpperCAmelCase     )
							_a  :    Optional[Any]			=						graph_size * [False]
							_a  :    Optional[int]			=						[]

							for i in range(UpperCAmelCase     ):
														if not visited[i]:
																					_a  :    List[str]			=						dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					components_list.append(UpperCAmelCase     )

							return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()