






"""simple docstring"""


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
   from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from transformers import VivitImageProcessor






class __a   :



  def __init__(self  :		int				,			lowercase_  :		Union[str, Any]				,			lowercase_  :		int=2				,			lowercase_  :		int=True				,			lowercase_  :		Tuple=True				,			lowercase_  :		List[Any]=10				,			lowercase_  :		Optional[Any]=10				,			lowercase_  :		Tuple=8				,			lowercase_  :		List[str]=[2, 4, 4]				,			lowercase_  :		Any=3				,			lowercase_  :		List[str]=768				,			lowercase_  :		int=5				,			lowercase_  :		Union[str, Any]=4				,			lowercase_  :		Union[str, Any]=37				,			lowercase_  :		List[Any]="gelu_fast"				,			lowercase_  :		List[Any]=0.0				,			lowercase_  :		List[Any]=0.0				,			lowercase_  :		str=0.02				,			lowercase_  :		List[str]=1E-06				,			lowercase_  :		Tuple=True				,			lowercase_  :		List[Any]=None				,			)		->							Tuple:
   __A	:       Tuple       =	parent
   __A	:       Optional[int]       =	batch_size
   __A	:       Optional[int]       =	is_training
   __A	:       Dict       =	use_labels
   __A	:       int       =	num_labels
   __A	:       Optional[Any]       =	image_size
   __A	:       int       =	num_frames
   __A	:       Optional[Any]       =	tubelet_size
   __A	:       int       =	num_channels
   __A	:       List[str]       =	hidden_size
   __A	:       Tuple       =	num_hidden_layers
   __A	:       Dict       =	num_attention_heads
   __A	:       List[str]       =	intermediate_size
   __A	:       int       =	hidden_act
   __A	:       Optional[Any]       =	hidden_dropout_prob
   __A	:       str       =	attention_probs_dropout_prob
   __A	:       str       =	initializer_range
   __A	:       Dict       =	layer_norm_eps
   __A	:       Optional[int]       =	qkv_bias
   __A	:       List[Any]       =	scope

   __A	:       Optional[int]       =	(
       (self.image_size // self.tubelet_size[2])
       * (self.image_size // self.tubelet_size[1])
       * (self.num_frames // self.tubelet_size[0])
   ) + 1  # CLS token



  def 						A_				(self  :		Optional[int]   )		->							Dict:
   __A	:       Tuple       =	floats_tensor(
       [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]   )

   __A	:       Dict       =	None
   if self.use_labels:
    __A	:       Optional[int]       =	ids_tensor([self.batch_size]				,			self.num_labels   )

   __A	:       int       =	self.get_config()

   return config, pixel_values, labels



  def 						A_				(self  :		int   )		->							List[str]:
   __A	:       Dict       =	VivitConfig(
       num_frames=self.num_frames				,			image_size=self.image_size				,			tubelet_size=self.tubelet_size				,			num_channels=self.num_channels				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			initializer_range=self.initializer_range				,			layer_norm_eps=self.layer_norm_eps				,			qkv_bias=self.qkv_bias				,			)
   __A	:       Union[str, Any]       =	self.num_labels
   return config



  def 						A_				(self  :		Any				,			lowercase_  :		str				,			lowercase_  :		str				,			lowercase_  :		Optional[Any]   )		->							int:
   __A	:       Tuple       =	VivitModel(config=lowercase_   )
   model.to(lowercase_   )
   model.eval()
   __A	:       Any       =	model(lowercase_   )
   self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)   )



  def 						A_				(self  :		str				,			lowercase_  :		Optional[Any]				,			lowercase_  :		int				,			lowercase_  :		Tuple   )		->							str:
   __A	:       List[str]       =	VivitForVideoClassification(lowercase_   )
   model.to(lowercase_   )
   model.eval()

   __A	:       Any       =	model(lowercase_   )

   # verify the logits shape
   __A	:       Optional[Any]       =	torch.Size((self.batch_size, self.num_labels)   )
   self.parent.assertEqual(result.logits.shape				,			lowercase_   )



  def 						A_				(self  :		Tuple   )		->							Any:
   __A	:       str       =	self.prepare_config_and_inputs()
   __A    ,					__A    ,					__A	:       List[str]       =	config_and_inputs
   __A	:       List[str]       =	{'pixel_values': pixel_values}
   return config, inputs_dict






@require_torch
class __a   (   snake_case_ ,unittest.TestCase				):
  __lowercase    :    Optional[int]						=					(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

  __lowercase    :    Union[str, Any]						=					False
  __lowercase    :    List[Any]						=					False
  __lowercase    :    List[str]						=					False
  __lowercase    :    Union[str, Any]						=					False



  def 						A_				(self  :		Tuple   )		->							Optional[Any]:
   __A	:       Any       =	VivitModelTester(self   )
   __A	:       Dict       =	ConfigTester(self				,			config_class=lowercase_				,			has_text_modality=lowercase_				,			hidden_size=37   )



  def 						A_				(self  :		List[Any]				,			lowercase_  :		Optional[int]				,			lowercase_  :		Dict				,			lowercase_  :		str=False   )		->							List[Any]:
   __A	:       Tuple       =	copy.deepcopy(lowercase_   )

   if return_labels:
    if model_class in get_values(lowercase_   ):
     __A	:       Tuple       =	torch.zeros(
         self.model_tester.batch_size				,			dtype=torch.long				,			device=lowercase_   )

   return inputs_dict



  def 						A_				(self  :		str   )		->							str:
   self.config_tester.run_common_tests()



  @unittest.skip(reason='Vivit does not use inputs_embeds'   )
  def 						A_				(self  :		Any   )		->							Union[str, Any]:
   pass



  def 						A_				(self  :		Tuple   )		->							int:
   __A    ,					__A	:       Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A	:       List[str]       =	model_class(lowercase_   )
    self.assertIsInstance(model.get_input_embeddings()				,			(nn.Module)   )
    __A	:       Dict       =	model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase_				,			nn.Linear   )   )



  def 						A_				(self  :		Optional[Any]   )		->							Tuple:
   __A    ,					__A	:       Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A	:       Optional[Any]       =	model_class(lowercase_   )
    __A	:       List[Any]       =	inspect.signature(model.forward   )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __A	:       List[Any]       =	[*signature.parameters.keys()]

    __A	:       Any       =	['pixel_values', 'head_mask']
    self.assertListEqual(arg_names[:2]				,			lowercase_   )



  def 						A_				(self  :		Optional[int]   )		->							List[str]:
   __A	:       Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase_   )



  def 						A_				(self  :		str   )		->							Optional[int]:
   __A	:       Any       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_video_classification(*lowercase_   )



  @slow
  def 						A_				(self  :		Union[str, Any]   )		->							Union[str, Any]:
   for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A	:       List[str]       =	VivitModel.from_pretrained(lowercase_   )
    self.assertIsNotNone(lowercase_   )



  def 						A_				(self  :		List[Any]   )		->							str:
   __A    ,					__A	:       Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()
   __A	:       Any       =	True

   for model_class in self.all_model_classes:
    __A	:       Optional[Any]       =	self.model_tester.seq_length

    __A	:       Dict       =	True
    __A	:       Tuple       =	False
    __A	:       Tuple       =	True
    __A	:       Dict       =	model_class(lowercase_   )
    model.to(lowercase_   )
    model.eval()
    with torch.no_grad():
     __A	:       Union[str, Any]       =	model(**self._prepare_for_class(lowercase_				,			lowercase_   )   )
    __A	:       Tuple       =	outputs.attentions
    self.assertEqual(len(lowercase_   )				,			self.model_tester.num_hidden_layers   )

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A	:       Union[str, Any]       =	True
    __A	:       Dict       =	model_class(lowercase_   )
    model.to(lowercase_   )
    model.eval()
    with torch.no_grad():
     __A	:       Tuple       =	model(**self._prepare_for_class(lowercase_				,			lowercase_   )   )
    __A	:       List[Any]       =	outputs.attentions
    self.assertEqual(len(lowercase_   )				,			self.model_tester.num_hidden_layers   )

    self.assertListEqual(
        list(attentions[0].shape[-3:]   )				,			[self.model_tester.num_attention_heads, seq_len, seq_len]				,			)
    __A	:       Union[str, Any]       =	len(lowercase_   )

    # Check attention is always last and order is fine
    __A	:       Tuple       =	True
    __A	:       Any       =	True
    __A	:       List[Any]       =	model_class(lowercase_   )
    model.to(lowercase_   )
    model.eval()
    with torch.no_grad():
     __A	:       List[Any]       =	model(**self._prepare_for_class(lowercase_				,			lowercase_   )   )

    self.assertEqual(out_len + 1				,			len(lowercase_   )   )

    __A	:       Optional[Any]       =	outputs.attentions

    self.assertEqual(len(lowercase_   )				,			self.model_tester.num_hidden_layers   )

    self.assertListEqual(
        list(self_attentions[0].shape[-3:]   )				,			[self.model_tester.num_attention_heads, seq_len, seq_len]				,			)



  def 						A_				(self  :		Dict   )		->							List[str]:
   def check_hidden_states_output(lowercase_  :		int				,			lowercase_  :		List[str]				,			lowercase_  :		Union[str, Any]   ):
    __A	:       str       =	model_class(lowercase_   )
    model.to(lowercase_   )
    model.eval()

    with torch.no_grad():
     __A	:       Any       =	model(**self._prepare_for_class(lowercase_				,			lowercase_   )   )

    __A	:       Any       =	outputs.hidden_states
    __A	:       Optional[int]       =	self.model_tester.num_hidden_layers + 1
    self.assertEqual(len(lowercase_   )				,			lowercase_   )

    __A	:       List[str]       =	self.model_tester.seq_length

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]   )				,			[seq_length, self.model_tester.hidden_size]				,			)

   __A    ,					__A	:       Any       =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A	:       Optional[int]       =	True
    check_hidden_states_output(lowercase_				,			lowercase_				,			lowercase_   )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __A	:       Optional[int]       =	True

    check_hidden_states_output(lowercase_				,			lowercase_				,			lowercase_   )



  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
  def 						A_				(self  :		List[Any]   )		->							str:
   pass






def 				a (     )  ->		List[str]:
 __A	:       List[str]       =	hf_hub_download(
     repo_id='hf-internal-testing/spaghetti-video'     ,	filename='eating_spaghetti_32_frames.npy'     ,	repo_type='dataset'		)
 __A	:       Dict       =	np.load(UpperCamelCase_		)
 return list(UpperCamelCase_		)






@require_torch
@require_vision
class __a   (   unittest.TestCase				):



  @cached_property
  def 						A_				(self  :		Dict   )		->							int:
   return VivitImageProcessor() if is_vision_available() else None



  @slow
  def 						A_				(self  :		Optional[int]   )		->							str:
   __A	:       List[Any]       =	VivitForVideoClassification.from_pretrained('google/vivit-b-16x2-kinetics400'   ).to(lowercase_   )

   __A	:       str       =	self.default_image_processor
   __A	:       int       =	prepare_video()
   __A	:       Dict       =	image_processor(lowercase_				,			return_tensors='pt'   ).to(lowercase_   )

   # forward pass
   with torch.no_grad():
    __A	:       Union[str, Any]       =	model(**lowercase_   )

   # verify the logits
   __A	:       str       =	torch.Size((1, 400)   )
   self.assertEqual(outputs.logits.shape				,			lowercase_   )

   # taken from original model
   __A	:       Optional[int]       =	torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]   ).to(lowercase_   )

   self.assertTrue(torch.allclose(outputs.logits[0, :5]				,			lowercase_				,			atol=1E-4   )   )













"""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)





