






"""simple docstring"""

import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import DonutSwinModel
			from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST

class     A_				:







				def __init__(				self				:    Dict    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    Any=1_3    ,     __lowerCamelCase				:    Optional[int]=3_2    ,     __lowerCamelCase				:    Tuple=2    ,     __lowerCamelCase				:    Union[str, Any]=3    ,     __lowerCamelCase				:    Optional[Any]=1_6    ,     __lowerCamelCase				:    int=[1, 2, 1]    ,     __lowerCamelCase				:    int=[2, 2, 4]    ,     __lowerCamelCase				:    Dict=2    ,     __lowerCamelCase				:    Any=2.0    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    List[str]=0.0    ,     __lowerCamelCase				:    Dict=0.0    ,     __lowerCamelCase				:    Any=0.1    ,     __lowerCamelCase				:    Optional[int]="gelu"    ,     __lowerCamelCase				:    List[Any]=False    ,     __lowerCamelCase				:    List[Any]=True    ,     __lowerCamelCase				:    int=0.02    ,     __lowerCamelCase				:    Optional[int]=1e-5    ,     __lowerCamelCase				:    Optional[int]=True    ,     __lowerCamelCase				:    str=None    ,     __lowerCamelCase				:    Tuple=True    ,     __lowerCamelCase				:    Any=1_0    ,     __lowerCamelCase				:    List[Any]=8    ,     )					->       Union[str, Any]:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  num_channels
										__magic_name__      =  embed_dim
										__magic_name__      =  depths
										__magic_name__      =  num_heads
										__magic_name__      =  window_size
										__magic_name__      =  mlp_ratio
										__magic_name__      =  qkv_bias
										__magic_name__      =  hidden_dropout_prob
										__magic_name__      =  attention_probs_dropout_prob
										__magic_name__      =  drop_path_rate
										__magic_name__      =  hidden_act
										__magic_name__      =  use_absolute_embeddings
										__magic_name__      =  patch_norm
										__magic_name__      =  layer_norm_eps
										__magic_name__      =  initializer_range
										__magic_name__      =  is_training
										__magic_name__      =  scope
										__magic_name__      =  use_labels
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  encoder_stride







				def      _snake_case       (				self				:    Any						)					->       List[Any]:
										__magic_name__      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

										__magic_name__      =  None
										if self.use_labels:
																__magic_name__      =  ids_tensor([self.batch_size]    ,     self.type_sequence_label_size						)

										__magic_name__      =  self.get_config()

										return config, pixel_values, labels







				def      _snake_case       (				self				:    Tuple						)					->       str:
										return DonutSwinConfig(
										    image_size=self.image_size    ,     patch_size=self.patch_size    ,     num_channels=self.num_channels    ,     embed_dim=self.embed_dim    ,     depths=self.depths    ,     num_heads=self.num_heads    ,     window_size=self.window_size    ,     mlp_ratio=self.mlp_ratio    ,     qkv_bias=self.qkv_bias    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     drop_path_rate=self.drop_path_rate    ,     hidden_act=self.hidden_act    ,     use_absolute_embeddings=self.use_absolute_embeddings    ,     path_norm=self.patch_norm    ,     layer_norm_eps=self.layer_norm_eps    ,     initializer_range=self.initializer_range    ,     encoder_stride=self.encoder_stride    ,     )







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    List[Any]						)					->       str:
										__magic_name__      =  DonutSwinModel(config=__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()
										__magic_name__      =  model(__lowerCamelCase						)

										__magic_name__      =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
										__magic_name__      =  int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

										self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, expected_seq_len, expected_dim)						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[int]:
										__magic_name__      =  self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     
										)      =  config_and_inputs
										__magic_name__      =  {"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class     A_				(   snake_case_       ,				snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					(DonutSwinModel,) if is_torch_available() else ()
				UpperCAmelCase__										=					{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
				UpperCAmelCase__										=					True

				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False







				def      _snake_case       (				self				:    List[Any]						)					->       Any:
										__magic_name__      =  DonutSwinModelTester(self						)
										__magic_name__      =  ConfigTester(self    ,     config_class=__lowerCamelCase    ,     embed_dim=3_7						)







				def      _snake_case       (				self				:    List[Any]						)					->       int:
										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()







				def      _snake_case       (				self				:    List[Any]						)					->       int:
										return







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[int]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Optional[Any]						)					->       Optional[int]:
										# DonutSwin does not use inputs_embeds
										pass







				def      _snake_case       (				self				:    Optional[Any]						)					->       Optional[Any]:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__      =  model_class(__lowerCamelCase						)
																self.assertIsInstance(model.get_input_embeddings()    ,     (nn.Module)						)
																__magic_name__      =  model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(__lowerCamelCase    ,     nn.Linear						)						)







				def      _snake_case       (				self				:    Optional[int]						)					->       List[str]:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

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

																__magic_name__      =  ["pixel_values"]
																self.assertListEqual(arg_names[:1]    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       Tuple:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__      =  True

										for model_class in self.all_model_classes:
																__magic_name__      =  True
																__magic_name__      =  False
																__magic_name__      =  True
																__magic_name__      =  model_class(__lowerCamelCase						)
																model.to(__lowerCamelCase						)
																model.eval()
																with torch.no_grad():
																						__magic_name__      =  model(**self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)						)
																__magic_name__      =  outputs.attentions
																__magic_name__      =  len(self.model_tester.depths						)
																self.assertEqual(len(__lowerCamelCase						)    ,     __lowerCamelCase						)

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																__magic_name__      =  True
																__magic_name__      =  config.window_size**2
																__magic_name__      =  model_class(__lowerCamelCase						)
																model.to(__lowerCamelCase						)
																model.eval()
																with torch.no_grad():
																						__magic_name__      =  model(**self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)						)
																__magic_name__      =  outputs.attentions
																self.assertEqual(len(__lowerCamelCase						)    ,     __lowerCamelCase						)

																self.assertListEqual(
																    list(attentions[0].shape[-3:]						)    ,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,     )
																__magic_name__      =  len(__lowerCamelCase						)

																# Check attention is always last and order is fine
																__magic_name__      =  True
																__magic_name__      =  True
																__magic_name__      =  model_class(__lowerCamelCase						)
																model.to(__lowerCamelCase						)
																model.eval()
																with torch.no_grad():
																						__magic_name__      =  model(**self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)						)

																if hasattr(self.model_tester    ,     "num_hidden_states_types"						):
																						__magic_name__      =  self.model_tester.num_hidden_states_types
																else:
																						# also another +1 for reshaped_hidden_states
																						__magic_name__      =  2
																self.assertEqual(out_len + added_hidden_states    ,     len(__lowerCamelCase						)						)

																__magic_name__      =  outputs.attentions

																self.assertEqual(len(__lowerCamelCase						)    ,     __lowerCamelCase						)

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







				def      _snake_case       (				self				:    List[str]    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    str						)					->       Any:
										__magic_name__      =  model_class(__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()

										with torch.no_grad():
																__magic_name__      =  model(**self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)						)

										__magic_name__      =  outputs.hidden_states

										__magic_name__      =  getattr(
										    self.model_tester    ,     "expected_num_hidden_layers"    ,     len(self.model_tester.depths						) + 1						)
										self.assertEqual(len(__lowerCamelCase						)    ,     __lowerCamelCase						)

										# DonutSwin has a different seq_length
										__magic_name__      =  (
										    config.patch_size
										    if isinstance(config.patch_size    ,     collections.abc.Iterable						)
										    else (config.patch_size, config.patch_size)
										)

										__magic_name__      =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

										__magic_name__      =  outputs.reshaped_hidden_states
										self.assertEqual(len(__lowerCamelCase						)    ,     __lowerCamelCase						)

										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  reshaped_hidden_states[0].shape
										__magic_name__      =  (
										    reshaped_hidden_states[0].view(__lowerCamelCase    ,     __lowerCamelCase    ,     height * width						).permute(0    ,     2    ,     1						)
										)
										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:]						)    ,     [num_patches, self.model_tester.embed_dim]    ,     )







				def      _snake_case       (				self				:    Optional[int]						)					->       List[Any]:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__      =  (
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size    ,     collections.abc.Iterable						)
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
																__magic_name__      =  True
																self.check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__magic_name__      =  True

																self.check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       Tuple:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__      =  3

										__magic_name__      =  (
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size    ,     collections.abc.Iterable						)
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)
										__magic_name__      =  (
										    config.patch_size
										    if isinstance(config.patch_size    ,     collections.abc.Iterable						)
										    else (config.patch_size, config.patch_size)
										)

										__magic_name__      =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										__magic_name__      =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
																__magic_name__      =  True
																self.check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     (padded_height, padded_width)						)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__magic_name__      =  True
																self.check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     (padded_height, padded_width)						)







				@slow
				def      _snake_case       (				self				:    Any						)					->       Any:
										for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__      =  DonutSwinModel.from_pretrained(__lowerCamelCase						)
																self.assertIsNotNone(__lowerCamelCase						)







				def      _snake_case       (				self				:    Any						)					->       Union[str, Any]:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__      =  _config_zero_init(__lowerCamelCase						)
										for model_class in self.all_model_classes:
																__magic_name__      =  model_class(config=__lowerCamelCase						)
																for name, param in model.named_parameters():
																						if "embeddings" not in name and param.requires_grad:
																												self.assertIn(
																												    ((param.data.mean() * 1e9).round() / 1e9).item()    ,     [0.0, 1.0]    ,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,     )











"""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()
lowercase           =							logging.get_logger(__name__)

@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Tensor    ,     __lowerCamelCase				:    Tensor						)					->       Dict:
										__magic_name__      =  len(list(m.modules()						)						) == 1 or isinstance(__lowerCamelCase    ,     nn.Convad						) or isinstance(__lowerCamelCase    ,     nn.BatchNormad						)
										if has_not_submodules:
																if not isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																						self.traced.append(__lowerCamelCase						)







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







				@property
				def      _snake_case       (				self				:    List[Any]						)					->       int:
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda __lowerCamelCase						: len(list(x.state_dict().keys()						)						) > 0    ,     self.traced						)						)





@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					0
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def __call__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    Tensor						)					->       str:
										__magic_name__      =  Tracker(self.dest						)(__lowerCamelCase						).parametrized
										__magic_name__      =  Tracker(self.src						)(__lowerCamelCase						).parametrized

										__magic_name__      =  list(filter(lambda __lowerCamelCase						: type(__lowerCamelCase						) not in self.src_skip    ,     __lowerCamelCase						)						)
										__magic_name__      =  list(filter(lambda __lowerCamelCase						: type(__lowerCamelCase						) not in self.dest_skip    ,     __lowerCamelCase						)						)

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

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






def _lowerCAmelCase						(	__lowerCamelCase:nn.Module			,    __lowerCamelCase:nn.Module ):

						'''simple docstring'''






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

																		all_keys.append((from_key, to_key) )
																		__magic_name__      =  f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																		__magic_name__      =  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:
												__magic_name__      =  from_state_dict.pop(__lowerCamelCase )

						our_model.load_state_dict(__lowerCamelCase )
						return our_model






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:VanConfig			,    __lowerCamelCase:str			,    __lowerCamelCase:nn.Module			,    __lowerCamelCase:Path			,    __lowerCamelCase:bool = True			,    ):

						'''simple docstring'''






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

						if not torch.allclose(from_model(__lowerCamelCase )			,    our_model(__lowerCamelCase ).logits ):
												raise ValueError("The model logits don't match the original one." )

						__magic_name__      =  name
						print(__lowerCamelCase )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name			,    commit_message="Add model"			,    use_temp_dir=__lowerCamelCase			,    )

												# we can use the convnext one
												__magic_name__      =  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=__lowerCamelCase			,    )

												print(f'''Pushed {checkpoint_name}''' )






def _lowerCAmelCase						(	__lowerCamelCase:Path			,    __lowerCamelCase:str = None			,    __lowerCamelCase:bool = True ):

						'''simple docstring'''






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

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

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

						__magic_name__      =  partial(__lowerCamelCase			,    num_labels=__lowerCamelCase			,    idalabel=__lowerCamelCase			,    labelaid=__lowerCamelCase )

						__magic_name__      =  {
						    "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]			,    ),
						}

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

						__magic_name__      =  {
						    "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(
												    __lowerCamelCase			,    names_to_config[model_name]			,    checkpoint=names_to_original_checkpoints[model_name]			,    from_model=names_to_original_models[model_name]()			,    save_directory=__lowerCamelCase			,    push_to_hub=__lowerCamelCase			,    )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    __lowerCamelCase			,    __lowerCamelCase			,    checkpoint=names_to_original_checkpoints[model_name]			,    from_model=names_to_original_models[model_name]()			,    save_directory=__lowerCamelCase			,    push_to_hub=__lowerCamelCase			,    )


if __name__ == "__main__":
			lowercase           =							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.''',
			)

			lowercase           =							parser.parse_args()
			lowercase           =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			lowercase           =							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)



