





"""simple docstring"""


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a__    :   Optional[int]    						= logging.get_logger(__name__)  # pylint: disable=invalid-name





def 							a__	(     self		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= image.to(device=_UpperCAmelCase		)
 snake_case__										= image  # take image as image_embeddings
 snake_case__										= image_embeddings.unsqueeze(1		)

 # duplicate image embeddings for each generation per prompt, using mps friendly method
 snake_case__							,      snake_case__							,      snake_case__										= image_embeddings.shape
 snake_case__										= image_embeddings.repeat(1		,   _UpperCAmelCase		,   1		)
 snake_case__										= image_embeddings.view(bs_embed * num_images_per_prompt		,   _UpperCAmelCase		,   -1		)

 if do_classifier_free_guidance:
  snake_case__										= torch.zeros_like(_UpperCAmelCase		)

  # For classifier free guidance, we need to do two forward passes.
  # Here we concatenate the unconditional and text embeddings into a single batch
  # to avoid doing two forward passes
  snake_case__										= torch.cat([uncond_embeddings, image_embeddings]		)

 return image_embeddings



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        snake_case__										= decoder_pipe_kwargs.get("torch_dtype"      ,    __SCREAMING_SNAKE_CASE   ) or prior.dtype

        snake_case__										= StableDiffusionImageVariationPipeline.from_pretrained(
            "lambdalabs/sd-image-variations-diffusers"      ,    **__SCREAMING_SNAKE_CASE   )

        # replace `_encode_image` method
        snake_case__										= types.MethodType(_encode_image      ,    self.decoder_pipe   )

        self.register_modules(
            prior=__SCREAMING_SNAKE_CASE      ,    tokenizer=__SCREAMING_SNAKE_CASE      ,    text_encoder=__SCREAMING_SNAKE_CASE      ,    prior_scheduler=__SCREAMING_SNAKE_CASE      ,    )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        if text_model_output is None:
         snake_case__										= len(__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else 1
         # get prompt text embeddings
         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors="pt"      ,    )
         snake_case__										= text_inputs.input_ids
         snake_case__										= text_inputs.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          snake_case__										= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
          logger.warning(
              "The following part of your input was truncated because CLIP can only handle sequences up to"
              F" {self.tokenizer.model_max_length} tokens: {removed_text}"   )
          snake_case__										= text_input_ids[:, : self.tokenizer.model_max_length]

         snake_case__										= self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= text_encoder_output.text_embeds
         snake_case__										= text_encoder_output.last_hidden_state

        else:
         snake_case__										= text_model_output[0].shape[0]
         snake_case__							,      snake_case__										= text_model_output[0], text_model_output[1]
         snake_case__										= text_attention_mask

        snake_case__										= text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        if do_classifier_free_guidance:
         snake_case__										= [""] * batch_size

         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    truncation=__SCREAMING_SNAKE_CASE      ,    return_tensors="pt"      ,    )
         snake_case__										= uncond_input.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )
         snake_case__										= self.text_encoder(uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= uncond_embeddings_text_encoder_output.text_embeds
         snake_case__										= uncond_embeddings_text_encoder_output.last_hidden_state

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

         snake_case__										= uncond_embeddings.shape[1]
         snake_case__										= uncond_embeddings.repeat(1      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= uncond_embeddings.view(batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE   )

         snake_case__										= uncond_text_encoder_hidden_states.shape[1]
         snake_case__										= uncond_text_encoder_hidden_states.repeat(1      ,    __SCREAMING_SNAKE_CASE      ,    1   )
         snake_case__										= uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE      ,    -1   )
         snake_case__										= uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

         # done duplicates

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         snake_case__										= torch.cat([uncond_embeddings, text_embeddings]   )
         snake_case__										= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

         snake_case__										= torch.cat([uncond_text_mask, text_mask]   )

        return text_embeddings, text_encoder_hidden_states, text_mask

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        if self.device != torch.device("meta"   ) or not hasattr(self.prior      ,    "_hf_hook"   ):
         return self.device
        for module in self.prior.modules():
         if (
             hasattr(__SCREAMING_SNAKE_CASE      ,    "_hf_hook"   )
             and hasattr(module._hf_hook      ,    "execution_device"   )
             and module._hf_hook.execution_device is not None
         ):
          return torch.device(module._hf_hook.execution_device   )
        return self.device

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






        '''simple docstring'''

        if latents is None:
         snake_case__										= randn_tensor(__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )
        else:
         if latents.shape != shape:
          raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
         snake_case__										= latents.to(__SCREAMING_SNAKE_CASE   )

        snake_case__										= latents * scheduler.init_noise_sigma
        return latents

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        self.decoder_pipe.to(__SCREAMING_SNAKE_CASE   )
        super().to(__SCREAMING_SNAKE_CASE   )

       @torch.no_grad()
       def __call__(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 25      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 4.0      ,    __SCREAMING_SNAKE_CASE = 8.0      ,    __SCREAMING_SNAKE_CASE = 50      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = "pil"      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if prompt is not None:
         if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= 1
         elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= len(__SCREAMING_SNAKE_CASE   )
         else:
          raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE   )}"   )
        else:
         snake_case__										= text_model_output[0].shape[0]

        snake_case__										= self._execution_device

        snake_case__										= batch_size * num_images_per_prompt

        snake_case__										= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        snake_case__							,      snake_case__							,      snake_case__										= self._encode_prompt(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        # prior

        self.prior_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.prior_scheduler.timesteps

        snake_case__										= self.prior.config.embedding_dim

        snake_case__										= self.prepare_latents(
            (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    self.prior_scheduler      ,    )

        for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE   )   ):
         # expand the latents if we are doing classifier free guidance
         snake_case__										= torch.cat([prior_latents] * 2   ) if do_classifier_free_guidance else prior_latents

         snake_case__										= self.prior(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    proj_embedding=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    ).predicted_image_embedding

         if do_classifier_free_guidance:
          snake_case__							,      snake_case__										= predicted_image_embedding.chunk(2   )
          snake_case__										= predicted_image_embedding_uncond + prior_guidance_scale * (
              predicted_image_embedding_text - predicted_image_embedding_uncond
          )

         if i + 1 == prior_timesteps_tensor.shape[0]:
          snake_case__										= None
         else:
          snake_case__										= prior_timesteps_tensor[i + 1]

         snake_case__										= self.prior_scheduler.step(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    sample=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    prev_timestep=__SCREAMING_SNAKE_CASE      ,    ).prev_sample

        snake_case__										= self.prior.post_process_latents(__SCREAMING_SNAKE_CASE   )

        snake_case__										= prior_latents

        snake_case__										= self.decoder_pipe(
            image=__SCREAMING_SNAKE_CASE      ,    height=__SCREAMING_SNAKE_CASE      ,    width=__SCREAMING_SNAKE_CASE      ,    num_inference_steps=__SCREAMING_SNAKE_CASE      ,    guidance_scale=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    output_type=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    num_images_per_prompt=__SCREAMING_SNAKE_CASE      ,    eta=__SCREAMING_SNAKE_CASE      ,    )
        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()
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)

