






"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
   import torch






class __a   :



  def __init__(self  :		List[str]				,			lowercase_  :		Optional[int]				,			lowercase_  :		Optional[int]=14				,			lowercase_  :		List[Any]=7				,			lowercase_  :		Dict=True				,			lowercase_  :		Tuple=True				,			lowercase_  :		Any=False				,			lowercase_  :		List[str]=True				,			lowercase_  :		List[Any]=99				,			lowercase_  :		Any=32				,			lowercase_  :		Tuple=5				,			lowercase_  :		str=4				,			lowercase_  :		Optional[Any]=37				,			lowercase_  :		str="gelu"				,			lowercase_  :		Dict=0.1				,			lowercase_  :		Any=0.1				,			lowercase_  :		List[str]=512				,			lowercase_  :		Union[str, Any]=0.02				,			)		->							Any:
   __A	:       Tuple       =	parent
   __A	:       Union[str, Any]       =	batch_size
   __A	:       Union[str, Any]       =	seq_length
   __A	:       List[str]       =	is_training
   __A	:       Dict       =	use_input_mask
   __A	:       List[str]       =	use_token_type_ids
   __A	:       List[Any]       =	use_labels
   __A	:       int       =	vocab_size
   __A	:       Union[str, Any]       =	hidden_size
   __A	:       Union[str, Any]       =	num_hidden_layers
   __A	:       int       =	num_attention_heads
   __A	:       Optional[Any]       =	intermediate_size
   __A	:       Optional[Any]       =	hidden_act
   __A	:       Any       =	hidden_dropout_prob
   __A	:       Any       =	attention_probs_dropout_prob
   __A	:       int       =	max_position_embeddings
   __A	:       Union[str, Any]       =	initializer_range
   __A	:       List[Any]       =	None
   __A	:       Tuple       =	vocab_size - 1
   __A	:       Any       =	vocab_size - 1
   __A	:       str       =	vocab_size - 1



  def 						A_				(self  :		Dict   )		->							Tuple:
   __A	:       Tuple       =	ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

   __A	:       List[str]       =	None
   if self.use_input_mask:
    __A	:       Tuple       =	random_attention_mask([self.batch_size, self.seq_length]   )

   __A	:       Any       =	GPTaConfig(
       vocab_size=self.vocab_size				,			n_embd=self.hidden_size				,			n_layer=self.num_hidden_layers				,			n_head=self.num_attention_heads				,			n_positions=self.max_position_embeddings				,			use_cache=lowercase_				,			bos_token_id=self.bos_token_id				,			eos_token_id=self.eos_token_id				,			pad_token_id=self.pad_token_id				,			)

   return (config, input_ids, input_mask)



  def 						A_				(self  :		int   )		->							Any:
   __A	:       Any       =	self.prepare_config_and_inputs()
   __A    ,					__A    ,					__A	:       Union[str, Any]       =	config_and_inputs
   __A	:       Any       =	{'input_ids': input_ids, 'attention_mask': attention_mask}
   return config, inputs_dict



  def 						A_				(self  :		Union[str, Any]   )		->							Tuple:
   __A    ,					__A    ,					__A	:       List[Any]       =	self.prepare_config_and_inputs()

   __A	:       List[Any]       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
   __A	:       Union[str, Any]       =	ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2   )

   return (
       config,
       input_ids,
       attention_mask,
       encoder_hidden_states,
       encoder_attention_mask,
   )



  def 						A_				(self  :		Dict				,			lowercase_  :		List[Any]				,			lowercase_  :		str				,			lowercase_  :		List[Any]				,			lowercase_  :		Tuple   )		->							Optional[Any]:
   __A	:       str       =	20
   __A	:       str       =	model_class_name(lowercase_   )

   __A	:       List[str]       =	model.init_cache(input_ids.shape[0]				,			lowercase_   )
   __A	:       Any       =	jnp.ones((input_ids.shape[0], max_decoder_length)				,			dtype='i4'   )

   __A	:       Union[str, Any]       =	jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1   )[None, :]				,			(input_ids.shape[0], input_ids.shape[-1] - 1)   )
   __A	:       Optional[int]       =	model(
       input_ids[:, :-1]				,			attention_mask=lowercase_				,			past_key_values=lowercase_				,			position_ids=lowercase_				,			)

   __A	:       int       =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,			dtype='i4'   )
   __A	:       List[Any]       =	model(
       input_ids[:, -1:]				,			attention_mask=lowercase_				,			past_key_values=outputs_cache.past_key_values				,			position_ids=lowercase_				,			)

   __A	:       Union[str, Any]       =	model(lowercase_   )

   __A	:       str       =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
   self.parent.assertTrue(diff < 1E-3				,			msg=f"Max diff is {diff}"   )



  def 						A_				(self  :		str				,			lowercase_  :		str				,			lowercase_  :		Optional[int]				,			lowercase_  :		int				,			lowercase_  :		List[Any]   )		->							List[str]:
   __A	:       Dict       =	20
   __A	:       Union[str, Any]       =	model_class_name(lowercase_   )

   __A	:       Tuple       =	jnp.concatenate(
       [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]				,			axis=-1				,			)

   __A	:       int       =	model.init_cache(input_ids.shape[0]				,			lowercase_   )
   __A	:       List[Any]       =	jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1   )[None, :]				,			(input_ids.shape[0], input_ids.shape[-1] - 1)   )

   __A	:       int       =	model(
       input_ids[:, :-1]				,			attention_mask=lowercase_				,			past_key_values=lowercase_				,			position_ids=lowercase_				,			)
   __A	:       str       =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,			dtype='i4'   )
   __A	:       List[str]       =	model(
       input_ids[:, -1:]				,			past_key_values=outputs_cache.past_key_values				,			attention_mask=lowercase_				,			position_ids=lowercase_				,			)

   __A	:       str       =	model(lowercase_				,			attention_mask=lowercase_   )

   __A	:       Tuple       =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
   self.parent.assertTrue(diff < 1E-3				,			msg=f"Max diff is {diff}"   )






@require_flax
class __a   (   snake_case_ ,snake_case_ ,unittest.TestCase				):
  __lowercase    :    Union[str, Any]						=					(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  __lowercase    :    Dict						=					(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



  def 						A_				(self  :		Optional[Any]   )		->							Optional[int]:
   __A	:       str       =	FlaxGPTaModelTester(self   )



  def 						A_				(self  :		Optional[Any]   )		->							Dict:
   for model_class_name in self.all_model_classes:
    __A    ,					__A    ,					__A	:       Any       =	self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )



  def 						A_				(self  :		str   )		->							Union[str, Any]:
   for model_class_name in self.all_model_classes:
    __A    ,					__A    ,					__A	:       List[str]       =	self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward_with_attn_mask(
        lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )



  @slow
  def 						A_				(self  :		Optional[Any]   )		->							str:
   __A	:       Union[str, Any]       =	GPTaTokenizer.from_pretrained('gpt2'				,			pad_token='</s>'				,			padding_side='left'   )
   __A	:       List[str]       =	tokenizer(['Hello this is a long string', 'Hey']				,			return_tensors='np'				,			padding=lowercase_				,			truncation=lowercase_   )

   __A	:       Dict       =	FlaxGPTaLMHeadModel.from_pretrained('gpt2'   )
   __A	:       List[Any]       =	False
   __A	:       Optional[int]       =	model.config.eos_token_id

   __A	:       List[str]       =	jax.jit(model.generate   )

   __A	:       Tuple       =	jit_generate(inputs['input_ids']				,			attention_mask=inputs['attention_mask']   ).sequences

   __A	:       Union[str, Any]       =	tokenizer.batch_decode(lowercase_				,			skip_special_tokens=lowercase_   )

   __A	:       Optional[Any]       =	[
       'Hello this is a long string of words. I\'m going to start with the first one.\n',
       'Hey, I\'m not sure if I\'m going to be able to do',
   ]

   self.assertListEqual(lowercase_				,			lowercase_   )



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

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__   ):
     # prepare inputs
     __A	:       Union[str, Any]       =	self._prepare_for_class(lowercase_				,			lowercase_   )
     __A	:       Union[str, Any]       =	{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

     # load corresponding PyTorch class
     __A	:       List[str]       =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
     __A	:       Tuple       =	getattr(lowercase_				,			lowercase_   )

     __A    ,					__A	:       Tuple       =	pt_inputs['input_ids'].shape
     __A	:       Optional[Any]       =	np.random.randint(0				,			seq_length - 1				,			size=(batch_size,)   )
     for batch_idx, start_index in enumerate(lowercase_   ):
      __A	:       List[Any]       =	0
      __A	:       Any       =	1
      __A	:       Any       =	0
      __A	:       str       =	1
     __A	:       Tuple       =	pt_model_class(lowercase_   ).eval()
     __A	:       str       =	model_class(lowercase_				,			dtype=jnp.floataa   )

     __A	:       Optional[Any]       =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,			lowercase_   )
     __A	:       List[Any]       =	fx_state

     with torch.no_grad():
      __A	:       Union[str, Any]       =	pt_model(**lowercase_   ).to_tuple()

     __A	:       Optional[Any]       =	fx_model(**lowercase_   ).to_tuple()
     self.assertEqual(len(lowercase_   )				,			len(lowercase_   )				,			'Output lengths differ between Flax and PyTorch'   )
     for fx_output, pt_output in zip(lowercase_				,			lowercase_   ):
      self.assert_almost_equals(fx_output[:, -1]				,			pt_output[:, -1].numpy()				,			4E-2   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      pt_model.save_pretrained(lowercase_   )
      __A	:       List[str]       =	model_class.from_pretrained(lowercase_				,			from_pt=lowercase_   )

     __A	:       int       =	fx_model_loaded(**lowercase_   ).to_tuple()
     self.assertEqual(
         len(lowercase_   )				,			len(lowercase_   )				,			'Output lengths differ between Flax and PyTorch'   )
     for fx_output_loaded, pt_output in zip(lowercase_				,			lowercase_   ):
      self.assert_almost_equals(fx_output_loaded[:, -1]				,			pt_output[:, -1].numpy()				,			4E-2   )



  @is_pt_flax_cross_test
  def 						A_				(self  :		str   )		->							List[Any]:
   __A    ,					__A	:       Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__   ):
     # prepare inputs
     __A	:       Dict       =	self._prepare_for_class(lowercase_				,			lowercase_   )
     __A	:       str       =	{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

     # load corresponding PyTorch class
     __A	:       Optional[int]       =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
     __A	:       Dict       =	getattr(lowercase_				,			lowercase_   )

     __A	:       Dict       =	pt_model_class(lowercase_   ).eval()
     __A	:       Tuple       =	model_class(lowercase_				,			dtype=jnp.floataa   )

     __A	:       str       =	load_flax_weights_in_pytorch_model(lowercase_				,			fx_model.params   )
     __A    ,					__A	:       Optional[Any]       =	pt_inputs['input_ids'].shape
     __A	:       Dict       =	np.random.randint(0				,			seq_length - 1				,			size=(batch_size,)   )
     for batch_idx, start_index in enumerate(lowercase_   ):
      __A	:       Dict       =	0
      __A	:       int       =	1
      __A	:       str       =	0
      __A	:       Tuple       =	1

     # make sure weights are tied in PyTorch
     pt_model.tie_weights()

     with torch.no_grad():
      __A	:       Any       =	pt_model(**lowercase_   ).to_tuple()

     __A	:       Union[str, Any]       =	fx_model(**lowercase_   ).to_tuple()
     self.assertEqual(len(lowercase_   )				,			len(lowercase_   )				,			'Output lengths differ between Flax and PyTorch'   )
     for fx_output, pt_output in zip(lowercase_				,			lowercase_   ):
      self.assert_almost_equals(fx_output[:, -1]				,			pt_output[:, -1].numpy()				,			4E-2   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      fx_model.save_pretrained(lowercase_   )
      __A	:       int       =	pt_model_class.from_pretrained(lowercase_				,			from_flax=lowercase_   )

     with torch.no_grad():
      __A	:       Optional[Any]       =	pt_model_loaded(**lowercase_   ).to_tuple()

     self.assertEqual(
         len(lowercase_   )				,			len(lowercase_   )				,			'Output lengths differ between Flax and PyTorch'   )
     for fx_output, pt_output in zip(lowercase_				,			lowercase_   ):
      self.assert_almost_equals(fx_output[:, -1]				,			pt_output[:, -1].numpy()				,			4E-2   )



  @slow
  def 						A_				(self  :		List[Any]   )		->							Tuple:
   for model_class_name in self.all_model_classes:
    __A	:       Optional[int]       =	model_class_name.from_pretrained('gpt2'				,			from_pt=lowercase_   )
    __A	:       Tuple       =	model(np.ones((1, 1)   )   )
    self.assertIsNotNone(lowercase_   )













"""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)





