




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__a  :		List[str]			=    []
for i in range(6):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
						)
						rename_keys.append(
						    (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
						)
						rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
						rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
						rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
						rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
						rename_keys.append(
						    (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
						)
						rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
						rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
						rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
						# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
						rename_keys.append(
						    (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
						)
						rename_keys.append(
						    (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
						)
						rename_keys.append(
						    (
						        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
						        f'decoder.layers.{i}.encoder_attn.out_proj.weight',
						    )
						)
						rename_keys.append(
						    (
						        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
						        f'decoder.layers.{i}.encoder_attn.out_proj.bias',
						    )
						)
						rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
						rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
						rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
						rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
						rename_keys.append(
						    (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
						)
						rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
						rename_keys.append(
						    (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
						)
						rename_keys.append(
						    (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
						)
						rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
						rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Any:
							lowercase__  :       Optional[int]   					=							state_dict.pop(_UpperCAmelCase				)
							lowercase__  :       Optional[int]   					=							val
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				)			-> Any:
							lowercase__  :       Union[str, Any]   					=							OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowercase__  :       List[Any]   					=							key.replace("backbone.0.body"				,"backbone.conv_encoder.model"				)
																					lowercase__  :       Any   					=							value
														else:
																					lowercase__  :       Optional[Any]   					=							value

							return new_state_dict
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_=False				)			-> List[Any]:
							lowercase__  :       List[str]   					=							""
							if is_panoptic:
														lowercase__  :       Optional[int]   					=							"detr."

							# first: transformer encoder
							for i in range(6				):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														lowercase__  :       List[str]   					=							state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
														lowercase__  :       Any   					=							state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
														# next, add query, keys and values (in that order) to the state dict
														lowercase__  :       List[str]   					=							in_proj_weight[:2_56, :]
														lowercase__  :       Optional[Any]   					=							in_proj_bias[:2_56]
														lowercase__  :       str   					=							in_proj_weight[2_56:5_12, :]
														lowercase__  :       int   					=							in_proj_bias[2_56:5_12]
														lowercase__  :       Optional[int]   					=							in_proj_weight[-2_56:, :]
														lowercase__  :       Tuple   					=							in_proj_bias[-2_56:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6				):
														# read in weights + bias of input projection layer of self-attention
														lowercase__  :       Dict   					=							state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
														lowercase__  :       Optional[int]   					=							state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
														# next, add query, keys and values (in that order) to the state dict
														lowercase__  :       Optional[Any]   					=							in_proj_weight[:2_56, :]
														lowercase__  :       List[str]   					=							in_proj_bias[:2_56]
														lowercase__  :       List[str]   					=							in_proj_weight[2_56:5_12, :]
														lowercase__  :       Any   					=							in_proj_bias[2_56:5_12]
														lowercase__  :       Tuple   					=							in_proj_weight[-2_56:, :]
														lowercase__  :       List[str]   					=							in_proj_bias[-2_56:]
														# read in weights + bias of input projection layer of cross-attention
														lowercase__  :       Optional[Any]   					=							state_dict.pop(
														    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
														lowercase__  :       List[str]   					=							state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														lowercase__  :       Dict   					=							in_proj_weight_cross_attn[:2_56, :]
														lowercase__  :       List[Any]   					=							in_proj_bias_cross_attn[:2_56]
														lowercase__  :       Any   					=							in_proj_weight_cross_attn[2_56:5_12, :]
														lowercase__  :       List[str]   					=							in_proj_bias_cross_attn[2_56:5_12]
														lowercase__  :       int   					=							in_proj_weight_cross_attn[-2_56:, :]
														lowercase__  :       List[str]   					=							in_proj_bias_cross_attn[-2_56:]
def 					snake_case_     (		)			-> Optional[int]:
							lowercase__  :       Any   					=							"http://images.cocodataset.org/val2017/000000039769.jpg"
							lowercase__  :       Any   					=							Image.open(requests.get(_UpperCAmelCase				,stream=_UpperCAmelCase				).raw				)

							return im
@torch.no_grad()
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Any:
							lowercase__  :       List[str]   					=							DetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														lowercase__  :       Union[str, Any]   					=							"resnet101"
							if "dc5" in model_name:
														lowercase__  :       str   					=							True
							lowercase__  :       str   					=							"panoptic" in model_name
							if is_panoptic:
														lowercase__  :       Dict   					=							2_50
							else:
														lowercase__  :       Dict   					=							91
														lowercase__  :       List[str]   					=							"huggingface/label-files"
														lowercase__  :       Optional[Any]   					=							"coco-detection-id2label.json"
														lowercase__  :       Optional[int]   					=							json.load(open(hf_hub_download(_UpperCAmelCase				,_UpperCAmelCase				,repo_type="dataset"				)				,"r"				)				)
														lowercase__  :       int   					=							{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
														lowercase__  :       Tuple   					=							idalabel
														lowercase__  :       List[Any]   					=							{v: k for k, v in idalabel.items()}

							# load image processor
							lowercase__  :       int   					=							"coco_panoptic" if is_panoptic else "coco_detection"
							lowercase__  :       List[Any]   					=							DetrImageProcessor(format=_UpperCAmelCase				)

							# prepare image
							lowercase__  :       Any   					=							prepare_img()
							lowercase__  :       Union[str, Any]   					=							image_processor(images=_UpperCAmelCase				,return_tensors="pt"				)
							lowercase__  :       Union[str, Any]   					=							encoding["pixel_values"]

							logger.info(F"""Converting model {model_name}..."""				)

							# load original model from torch hub
							lowercase__  :       List[str]   					=							torch.hub.load("facebookresearch/detr"				,_UpperCAmelCase				,pretrained=_UpperCAmelCase				).eval()
							lowercase__  :       Union[str, Any]   					=							detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					lowercase__  :       Union[str, Any]   					=							"detr." + src
														rename_key(_UpperCAmelCase				,_UpperCAmelCase				,_UpperCAmelCase				)
							lowercase__  :       Any   					=							rename_backbone_keys(_UpperCAmelCase				)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_UpperCAmelCase				,is_panoptic=_UpperCAmelCase				)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowercase__  :       str   					=							"detr.model." if is_panoptic else "model."
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("detr"				)
																					    and not key.startswith("class_labels_classifier"				)
																					    and not key.startswith("bbox_predictor"				)
																					):
																												lowercase__  :       str   					=							state_dict.pop(_UpperCAmelCase				)
																												lowercase__  :       Any   					=							val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												lowercase__  :       str   					=							state_dict.pop(_UpperCAmelCase				)
																												lowercase__  :       Optional[int]   					=							val
																					elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
																												continue
																					else:
																												lowercase__  :       int   					=							state_dict.pop(_UpperCAmelCase				)
																												lowercase__  :       int   					=							val
														else:
																					if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
																												lowercase__  :       Optional[int]   					=							state_dict.pop(_UpperCAmelCase				)
																												lowercase__  :       Tuple   					=							val
																												# finally, create HuggingFace model and load state dict
							lowercase__  :       Dict   					=							DetrForSegmentation(_UpperCAmelCase				) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase				)
							model.load_state_dict(_UpperCAmelCase				)
							model.eval()
							# verify our conversion
							lowercase__  :       Optional[Any]   					=							detr(_UpperCAmelCase				)
							lowercase__  :       Optional[Any]   					=							model(_UpperCAmelCase				)
							assert torch.allclose(outputs.logits				,original_outputs["pred_logits"]				,atol=1E-4				)
							assert torch.allclose(outputs.pred_boxes				,original_outputs["pred_boxes"]				,atol=1E-4				)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks				,original_outputs["pred_masks"]				,atol=1E-4				)

							# Save model and image processor
							logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
							Path(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
							model.save_pretrained(_UpperCAmelCase				)
							image_processor.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
						__a  :		List[Any]			=    argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the DETR model you\'d like to convert.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
						)
						__a  :		Optional[int]			=    parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






"""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()
a__    :   Any    						= logging.get_logger(__name__)



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							List[nn.Module] 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							list 							=   field(default_factory=__lowerCAmelCase							)

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							nn.Module
       snake_case__ :							int 							=   0
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= Tracker(self.dest   )(__SCREAMING_SNAKE_CASE   ).parametrized
        snake_case__										= Tracker(self.src   )(__SCREAMING_SNAKE_CASE   ).parametrized

        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.src_skip      ,    __SCREAMING_SNAKE_CASE   )   )
        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.dest_skip      ,    __SCREAMING_SNAKE_CASE   )   )

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

        for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_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 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     nn.Module:

 '''simple docstring'''






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

   all_keys.append((from_key, to_key)		)
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_2"
   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:
  snake_case__										= from_state_dict.pop(_UpperCAmelCase		)

 our_model.load_state_dict(_UpperCAmelCase		)
 return our_model





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase = True		,   )			->     Union[str, Any]:

 '''simple docstring'''






 print(f"Downloading weights for {name}..."		)
 snake_case__										= cached_download(_UpperCAmelCase		)
 print(f"Converting {name}..."		)
 snake_case__										= torch.load(_UpperCAmelCase		)["state_dict"]
 from_model.load_state_dict(_UpperCAmelCase		)
 from_model.eval()
 with torch.no_grad():
  snake_case__										= VanForImageClassification(_UpperCAmelCase		).eval()
  snake_case__										= ModuleTransfer(src=_UpperCAmelCase		,   dest=_UpperCAmelCase		)
  snake_case__										= torch.randn((1, 3, 2_2_4, 2_2_4)		)
  module_transfer(_UpperCAmelCase		)
  snake_case__										= 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."		)

 snake_case__										= 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
  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=_UpperCAmelCase		,   )

  print(f"Pushed {checkpoint_name}"		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase = None		,   _UpperCAmelCase = True		)			->     Union[str, Any]:

 '''simple docstring'''






 snake_case__										= "imagenet-1k-id2label.json"
 snake_case__										= 1_0_0_0

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

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

 snake_case__										= partial(_UpperCAmelCase		,   num_labels=_UpperCAmelCase		,   idalabel=_UpperCAmelCase		,   labelaid=_UpperCAmelCase		)

 snake_case__										= {
     "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]		,   ),
 }

 snake_case__										= {
     "van-tiny": van_tiny,
     "van-small": van_small,
     "van-base": van_base,
     "van-large": van_large,
 }

 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(
      _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__":
   a__    :   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.""",
   )

   a__    :   Any    						= parser.parse_args()
   a__    :   Path    						= args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   a__    :   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)

