
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			:   List[Any]         =     logging.get_logger(__name__)


@dataclass
class 		__magic_name__   :
  _lowerCAmelCase		=	42
  _lowerCAmelCase		=	field(default_factory=UpperCamelCase__    )
  _lowerCAmelCase		=	field(default_factory=UpperCamelCase__    )





  def       _A							(							self    :	Dict						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Any						,  lowerCamelCase__    :	Dict				):
       lowerCAmelCase		:					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]						,  lowerCamelCase__    :	Optional[int]				):
       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       _A							(							self    :	List[Any]				):
       # 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 		__magic_name__   :
  _lowerCAmelCase		=	42
  _lowerCAmelCase		=	42
  _lowerCAmelCase		=	0
  _lowerCAmelCase		=	field(default_factory=UpperCamelCase__    )
  _lowerCAmelCase		=	field(default_factory=UpperCamelCase__    )





  def __call__(							self    :	List[str]						,  lowerCamelCase__    :	int				):
       lowerCAmelCase		:					List[Any]  	=     Tracker(self.dest				)(snake_case_				).parametrized
       lowerCAmelCase		:					Optional[Any]  	=     Tracker(self.src				)(snake_case_				).parametrized

       lowerCAmelCase		:					Union[str, Any]  	=     list(filter(lambda lowerCamelCase__				: type(snake_case_				) not in self.src_skip						,  snake_case_				)				)
       lowerCAmelCase		:					Dict  	=     list(filter(lambda lowerCamelCase__				: 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     UpperCAmelCase__							(       __magic_name__						:     Optional[int]							,  __magic_name__						:     List[Any]  ):


     '''simple docstring'''

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

               all_keys.append((from_key, to_key)  )
               lowerCAmelCase		:					int  	=     f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
               lowerCAmelCase		:					List[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:
          lowerCAmelCase		:					List[Any]  	=     from_state_dict.pop(__a  )

     our_model.load_state_dict(__a  )
     return our_model
def     UpperCAmelCase__							(       __magic_name__						:     Union[str, Any]							,  __magic_name__						:     Optional[int]							,  __magic_name__						:     Any							,  __magic_name__						:     Dict							,  __magic_name__						:     Tuple							,  __magic_name__						:     str = True							,  ):


     '''simple docstring'''

     print(f'''Downloading weights for {name}...'''  )
     lowerCAmelCase		:					Dict  	=     cached_download(__a  )
     print(f'''Converting {name}...'''  )
     lowerCAmelCase		:					Dict  	=     torch.load(__a  )['''state_dict''']
     from_model.load_state_dict(__a  )
     from_model.eval()
     with torch.no_grad():
          lowerCAmelCase		:					int  	=     VanForImageClassification(__a  ).eval()
          lowerCAmelCase		:					Tuple  	=     ModuleTransfer(src=__a							,  dest=__a  )
          lowerCAmelCase		:					Any  	=     torch.randn((1, 3, 2_24, 2_24)  )
          module_transfer(__a  )
          lowerCAmelCase		:					str  	=     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.'''  )

     lowerCAmelCase		:					Tuple  	=     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
          lowerCAmelCase		:					Tuple  	=     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     UpperCAmelCase__							(       __magic_name__						:     str							,  __magic_name__						:     Optional[Any] = None							,  __magic_name__						:     Optional[Any] = True  ):


     '''simple docstring'''

     lowerCAmelCase		:					Tuple  	=     '''imagenet-1k-id2label.json'''
     lowerCAmelCase		:					Tuple  	=     10_00

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

     lowerCAmelCase		:					Optional[int]  	=     idalabel
     lowerCAmelCase		:					Tuple  	=     {v: k for k, v in idalabel.items()}

     lowerCAmelCase		:					Tuple  	=     partial(__a							,  num_labels=__a							,  idalabel=__a							,  labelaid=__a  )

     lowerCAmelCase		:					int  	=     {
         '''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]							,  ),
     }

     lowerCAmelCase		:					Union[str, Any]  	=     {
         '''van-tiny''': van_tiny,
         '''van-small''': van_small,
         '''van-base''': van_base,
         '''van-large''': van_large,
     }

     lowerCAmelCase		:					List[Any]  	=     {
         '''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__":
  __SCREAMING_SNAKE_CASE			:   Optional[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.',
  )

  __SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     parser.parse_args()
  __SCREAMING_SNAKE_CASE			:   Dict         =     args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  __SCREAMING_SNAKE_CASE			:   Optional[int]         =     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 ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
