






"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase   :						int     						= logging.get_logger(__name__)

_UpperCamelCase   :						Any     						= {
    """shi-labs/oneformer_ade20k_swin_tiny""": (
        """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}






class __a   (   snake_case_				):
  __lowercase    :    Dict						=					'oneformer'
  __lowercase    :    List[str]						=					{'hidden_size': 'hidden_dim'}



  def __init__(self  :		int				,			lowercase_  :		Optional[Dict] = None				,			lowercase_  :		int = 255				,			lowercase_  :		int = 150				,			lowercase_  :		int = 0.1				,			lowercase_  :		float = 2.0				,			lowercase_  :		float = 5.0				,			lowercase_  :		float = 5.0				,			lowercase_  :		float = 0.5				,			lowercase_  :		float = 0.07				,			lowercase_  :		int = 12_544				,			lowercase_  :		float = 3.0				,			lowercase_  :		float = 0.75				,			lowercase_  :		float = 0.02				,			lowercase_  :		float = 1.0				,			lowercase_  :		float = 1E-05				,			lowercase_  :		bool = False				,			lowercase_  :		bool = True				,			lowercase_  :		bool = True				,			lowercase_  :		Optional[list] = [4, 8, 16, 32]				,			lowercase_  :		int = 77				,			lowercase_  :		int = 256				,			lowercase_  :		int = 77				,			lowercase_  :		int = 6				,			lowercase_  :		int = 49_408				,			lowercase_  :		int = 2				,			lowercase_  :		int = 16				,			lowercase_  :		int = 256				,			lowercase_  :		int = 256				,			lowercase_  :		int = 256				,			lowercase_  :		int = 1_024				,			lowercase_  :		str = "GN"				,			lowercase_  :		int = 6				,			lowercase_  :		int = 10				,			lowercase_  :		bool = True				,			lowercase_  :		int = 8				,			lowercase_  :		float = 0.1				,			lowercase_  :		int = 2_048				,			lowercase_  :		bool = False				,			lowercase_  :		bool = False				,			lowercase_  :		int = 2				,			lowercase_  :		int = 4				,			**lowercase_  :		Any				,			)		->							str:
   if backbone_config is None:
    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'   )
    __A	:       Any       =	CONFIG_MAPPING['swin'](
        image_size=224				,			in_channels=3				,			patch_size=4				,			embed_dim=96				,			depths=[2, 2, 6, 2]				,			num_heads=[3, 6, 12, 24]				,			window_size=7				,			drop_path_rate=0.3				,			use_absolute_embeddings=lowercase_				,			out_features=['stage1', 'stage2', 'stage3', 'stage4']				,			)
   elif isinstance(lowercase_				,			lowercase_   ):
    __A	:       List[str]       =	backbone_config.get('model_type'   )
    __A	:       Tuple       =	CONFIG_MAPPING[backbone_model_type]
    __A	:       int       =	config_class.from_dict(lowercase_   )

   __A	:       Union[str, Any]       =	backbone_config

   __A	:       Any       =	ignore_value
   __A	:       Dict       =	num_queries
   __A	:       List[Any]       =	no_object_weight
   __A	:       List[Any]       =	class_weight
   __A	:       Union[str, Any]       =	mask_weight
   __A	:       Tuple       =	dice_weight
   __A	:       Optional[int]       =	contrastive_weight
   __A	:       str       =	contrastive_temperature
   __A	:       Union[str, Any]       =	train_num_points
   __A	:       Tuple       =	oversample_ratio
   __A	:       List[str]       =	importance_sample_ratio
   __A	:       List[str]       =	init_std
   __A	:       int       =	init_xavier_std
   __A	:       List[str]       =	layer_norm_eps
   __A	:       List[str]       =	is_training
   __A	:       Optional[Any]       =	use_auxiliary_loss
   __A	:       Optional[Any]       =	output_auxiliary_logits
   __A	:       Optional[int]       =	strides
   __A	:       Union[str, Any]       =	task_seq_len
   __A	:       str       =	text_encoder_width
   __A	:       str       =	text_encoder_context_length
   __A	:       Any       =	text_encoder_num_layers
   __A	:       Dict       =	text_encoder_vocab_size
   __A	:       int       =	text_encoder_proj_layers
   __A	:       int       =	text_encoder_n_ctx
   __A	:       str       =	conv_dim
   __A	:       Tuple       =	mask_dim
   __A	:       str       =	hidden_dim
   __A	:       Any       =	encoder_feedforward_dim
   __A	:       Union[str, Any]       =	norm
   __A	:       Optional[Any]       =	encoder_layers
   __A	:       List[str]       =	decoder_layers
   __A	:       Tuple       =	use_task_norm
   __A	:       Optional[Any]       =	num_attention_heads
   __A	:       Tuple       =	dropout
   __A	:       Any       =	dim_feedforward
   __A	:       Union[str, Any]       =	pre_norm
   __A	:       int       =	enforce_input_proj
   __A	:       Dict       =	query_dec_layers
   __A	:       List[str]       =	common_stride
   __A	:       List[Any]       =	decoder_layers

   super().__init__(**lowercase_   )



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





