





"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_UpperCamelCase   :						int     						= logging.get_logger(__name__)

_UpperCamelCase   :						int     						= {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class __a   (   snake_case_				):
  __lowercase    :    int						=					'vit-hybrid'



  def __init__(self  :		Union[str, Any]				,			lowercase_  :		Tuple=None				,			lowercase_  :		List[Any]=768				,			lowercase_  :		Dict=12				,			lowercase_  :		Tuple=12				,			lowercase_  :		str=3_072				,			lowercase_  :		List[str]="gelu"				,			lowercase_  :		Optional[int]=0.0				,			lowercase_  :		str=0.0				,			lowercase_  :		str=0.02				,			lowercase_  :		Union[str, Any]=1E-12				,			lowercase_  :		Tuple=224				,			lowercase_  :		Tuple=1				,			lowercase_  :		str=3				,			lowercase_  :		List[Any]=[1, 1_024, 24, 24]				,			lowercase_  :		Optional[Any]=True				,			**lowercase_  :		List[str]				,			)		->							Union[str, Any]:
   super().__init__(**lowercase_   )

   if backbone_config is None:
    logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'   )
    __A	:       Optional[int]       =	{
        'global_padding': 'same',
        'layer_type': 'bottleneck',
        'depths': [3, 4, 9],
        'out_features': ['stage3'],
        'embedding_dynamic_padding': True,
    }

   if isinstance(lowercase_				,			lowercase_   ):
    if "model_type" in backbone_config:
     __A	:       List[Any]       =	CONFIG_MAPPING[backbone_config['model_type']]
    else:
     logger.info(
         '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'   )
     __A	:       int       =	BitConfig
    __A	:       str       =	backbone_config_class(**lowercase_   )

   __A	:       Union[str, Any]       =	backbone_featmap_shape
   __A	:       Tuple       =	backbone_config
   __A	:       List[Any]       =	hidden_size
   __A	:       str       =	num_hidden_layers
   __A	:       int       =	num_attention_heads
   __A	:       Tuple       =	intermediate_size
   __A	:       Optional[Any]       =	hidden_act
   __A	:       List[str]       =	hidden_dropout_prob
   __A	:       Tuple       =	attention_probs_dropout_prob
   __A	:       Optional[Any]       =	initializer_range
   __A	:       Any       =	layer_norm_eps
   __A	:       Any       =	image_size
   __A	:       Tuple       =	patch_size
   __A	:       Optional[Any]       =	num_channels
   __A	:       Tuple       =	qkv_bias



  def 						A_				(self  :		int   )		->							Dict[str, any]:
   __A	:       List[str]       =	copy.deepcopy(self.__dict__   )
   __A	:       Optional[int]       =	self.backbone_config.to_dict()
   __A	:       Tuple       =	self.__class__.model_type
   return output













"""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   :						Union[str, Any]     						= logging.get_logger(__name__)






@dataclass
class __a   :
  __lowercase    :    nn.Module
  __lowercase    :    List[nn.Module]						=					field(default_factory=snake_case_				)
  __lowercase    :    list						=					field(default_factory=snake_case_				)



  def 						A_				(self  :		List[Any]				,			lowercase_  :		List[str]				,			lowercase_  :		Tensor				,			lowercase_  :		Tensor   )		->							Any:
   __A	:       Optional[int]       =	len(list(m.modules()   )   ) == 1 or isinstance(lowercase_				,			nn.Convad   ) or isinstance(lowercase_				,			nn.BatchNormad   )
   if has_not_submodules:
    if not isinstance(lowercase_				,			lowercase_   ):
     self.traced.append(lowercase_   )



  def __call__(self  :		Tuple				,			lowercase_  :		Tensor   )		->							List[Any]:
   for m in self.module.modules():
    self.handles.append(m.register_forward_hook(self._forward_hook   )   )
   self.module(lowercase_   )
   [x.remove() for x in self.handles]
   return self



  @property
  def 						A_				(self  :		str   )		->							Dict:
   # check the len of the state_dict keys to see if we have learnable params
   return list(filter(lambda lowercase_   : len(list(x.state_dict().keys()   )   ) > 0				,			self.traced   )   )






@dataclass
class __a   :
  __lowercase    :    nn.Module
  __lowercase    :    nn.Module
  __lowercase    :    int						=					0
  __lowercase    :    List						=					field(default_factory=snake_case_				)
  __lowercase    :    List						=					field(default_factory=snake_case_				)



  def __call__(self  :		List[str]				,			lowercase_  :		Tensor   )		->							Optional[Any]:
   __A	:       List[Any]       =	Tracker(self.dest   )(lowercase_   ).parametrized
   __A	:       Dict       =	Tracker(self.src   )(lowercase_   ).parametrized

   __A	:       Dict       =	list(filter(lambda lowercase_   : type(lowercase_   ) not in self.src_skip				,			lowercase_   )   )
   __A	:       Any       =	list(filter(lambda lowercase_   : type(lowercase_   ) not in self.dest_skip				,			lowercase_   )   )

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

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






def 				a (     UpperCamelCase_							:				nn.Module     ,	UpperCamelCase_							:				nn.Module		)  ->		nn.Module:
 # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
 __A	:       Tuple       =	from_model.state_dict()
 __A	:       str       =	our_model.state_dict()
 __A	:       Optional[Any]       =	our_model.config
 __A	:       Any       =	[]
 for stage_idx in range(len(config.hidden_sizes		)		):
  for block_id in range(config.depths[stage_idx]		):
   __A	:       int       =	F"block{stage_idx + 1}.{block_id}.layer_scale_1"
   __A	:       str       =	F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

   all_keys.append((from_key, to_key)		)
   __A	:       str       =	F"block{stage_idx + 1}.{block_id}.layer_scale_2"
   __A	:       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:
  __A	:       int       =	from_state_dict.pop(UpperCamelCase_		)

 our_model.load_state_dict(UpperCamelCase_		)
 return our_model




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				VanConfig     ,	UpperCamelCase_							:				str     ,	UpperCamelCase_							:				nn.Module     ,	UpperCamelCase_							:				Path     ,	UpperCamelCase_							:				bool = True     ,	)  ->		int:
 print(F"Downloading weights for {name}..."		)
 __A	:       Dict       =	cached_download(UpperCamelCase_		)
 print(F"Converting {name}..."		)
 __A	:       Union[str, Any]       =	torch.load(UpperCamelCase_		)['state_dict']
 from_model.load_state_dict(UpperCamelCase_		)
 from_model.eval()
 with torch.no_grad():
  __A	:       Any       =	VanForImageClassification(UpperCamelCase_		).eval()
  __A	:       List[str]       =	ModuleTransfer(src=UpperCamelCase_     ,	dest=UpperCamelCase_		)
  __A	:       List[str]       =	torch.randn((1, 3, 2_2_4, 2_2_4)		)
  module_transfer(UpperCamelCase_		)
  __A	:       Union[str, Any]       =	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.'		)

 __A	:       str       =	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
  __A	:       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=UpperCamelCase_     ,	)

  print(F"Pushed {checkpoint_name}"		)




def 				a (     UpperCamelCase_							:				Path     ,	UpperCamelCase_							:				str = None     ,	UpperCamelCase_							:				bool = True		)  ->		Optional[int]:
 __A	:       Union[str, Any]       =	'imagenet-1k-id2label.json'
 __A	:       List[str]       =	1_0_0_0

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

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

 __A	:       List[str]       =	partial(UpperCamelCase_     ,	num_labels=UpperCamelCase_     ,	idalabel=UpperCamelCase_     ,	labelaid=UpperCamelCase_		)

 __A	:       Dict       =	{
     '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]     ,	),
 }

 __A	:       List[str]       =	{
     'van-tiny': van_tiny,
     'van-small': van_small,
     'van-base': van_base,
     'van-large': van_large,
 }

 __A	:       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(
      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__":
   _UpperCamelCase   :						List[str]     						= 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   :						List[str]     						= 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)





