



"""simple docstring"""
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
			import torch


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_4 ,				lowerCamelCase__=7 ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=False ,				lowerCamelCase__=True ,				lowerCamelCase__=9_9 ,				lowerCamelCase__=3_2 ,				lowerCamelCase__=4 ,				lowerCamelCase__=4 ,				lowerCamelCase__=[[["global", "local"], 2]] ,				lowerCamelCase__=3_7 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=5_1_2 ,				lowerCamelCase__=7 ,				lowerCamelCase__=0.0_2 ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = seq_length
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = use_input_mask
									_lowerCamelCase					     = use_token_type_ids
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = vocab_size
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = attention_types
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = max_position_embeddings
									_lowerCamelCase					     = window_size
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = None
									_lowerCamelCase					     = vocab_size - 1
									_lowerCamelCase					     = vocab_size - 1
									_lowerCamelCase					     = vocab_size - 1

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

									_lowerCamelCase					     = None
									if self.use_input_mask:
													_lowerCamelCase					     = random_attention_mask([self.batch_size, self.seq_length]      )

									_lowerCamelCase					     = GPTNeoConfig(
									    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_layers=self.num_hidden_layers ,				num_heads=self.num_attention_heads ,				max_position_embeddings=self.max_position_embeddings ,				use_cache=lowerCamelCase__ ,				bos_token_id=self.bos_token_id ,				eos_token_id=self.eos_token_id ,				pad_token_id=self.pad_token_id ,				window_size=self.window_size ,				attention_types=self.attention_types ,				)

									return (config, input_ids, input_mask)

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.prepare_config_and_inputs()
									_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = config_and_inputs
									_lowerCamelCase					     = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 2_0
									_lowerCamelCase					     = model_class_name(lowerCamelCase__      )

									_lowerCamelCase					     = model.init_cache(input_ids.shape[0] ,				lowerCamelCase__      )
									_lowerCamelCase					     = jnp.ones((input_ids.shape[0], max_decoder_length) ,				dtype='''i4'''      )

									_lowerCamelCase					     = jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1      )[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1)      )
									_lowerCamelCase					     = model(
									    input_ids[:, :-1] ,				attention_mask=lowerCamelCase__ ,				past_key_values=lowerCamelCase__ ,				position_ids=lowerCamelCase__ ,				)

									_lowerCamelCase					     = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype='''i4'''      )
									_lowerCamelCase					     = model(
									    input_ids[:, -1:] ,				attention_mask=lowerCamelCase__ ,				past_key_values=outputs_cache.past_key_values ,				position_ids=lowerCamelCase__ ,				)

									_lowerCamelCase					     = model(lowerCamelCase__      )

									_lowerCamelCase					     = 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 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 2_0
									_lowerCamelCase					     = model_class_name(lowerCamelCase__      )

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

									_lowerCamelCase					     = model.init_cache(input_ids.shape[0] ,				lowerCamelCase__      )
									_lowerCamelCase					     = jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1      )[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1)      )

									_lowerCamelCase					     = model(
									    input_ids[:, :-1] ,				attention_mask=lowerCamelCase__ ,				past_key_values=lowerCamelCase__ ,				position_ids=lowerCamelCase__ ,				)
									_lowerCamelCase					     = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype='''i4'''      )
									_lowerCamelCase					     = model(
									    input_ids[:, -1:] ,				past_key_values=outputs_cache.past_key_values ,				attention_mask=lowerCamelCase__ ,				position_ids=lowerCamelCase__ ,				)

									_lowerCamelCase					     = model(lowerCamelCase__ ,				attention_mask=lowerCamelCase__      )

									_lowerCamelCase					     = 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 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				str        =       (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					lowercase__      :				Dict        =       (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = FlaxGPTNeoModelTester(self      )

					def 	snake_case__					(			self      ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )

					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = GPTaTokenizer.from_pretrained('''gpt2''' ,				pad_token='''<|endoftext|>''' ,				padding_side='''left'''      )
									_lowerCamelCase					     = tokenizer(['''Hello this is a long string''', '''Hey'''] ,				return_tensors='''np''' ,				padding=lowerCamelCase__ ,				truncation=lowerCamelCase__      )

									_lowerCamelCase					     = FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''      )
									_lowerCamelCase					     = False
									_lowerCamelCase					     = model.config.eos_token_id

									_lowerCamelCase					     = jax.jit(model.generate      )

									_lowerCamelCase					     = jit_generate(
									    inputs['''input_ids'''] ,				attention_mask=inputs['''attention_mask'''] ,				pad_token_id=tokenizer.pad_token_id      ).sequences

									_lowerCamelCase					     = tokenizer.batch_decode(lowerCamelCase__ ,				skip_special_tokens=lowerCamelCase__      )

									_lowerCamelCase					     = [
									    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
									    '''Hey, I\'m a little late to the party. I\'m going to''',
									]

									self.assertListEqual(lowerCamelCase__ ,				lowerCamelCase__      )

					@is_pt_flax_cross_test
					def 	snake_case__					(			self      ):
									_lowerCamelCase     ,   _lowerCamelCase					     = 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
																	_lowerCamelCase					     = self._prepare_for_class(lowerCamelCase__ ,				lowerCamelCase__      )
																	_lowerCamelCase					     = {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																	# load corresponding PyTorch class
																	_lowerCamelCase					     = model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	_lowerCamelCase					     = getattr(lowerCamelCase__ ,				lowerCamelCase__      )

																	_lowerCamelCase     ,   _lowerCamelCase					     = pt_inputs['''input_ids'''].shape
																	_lowerCamelCase					     = np.random.randint(0 ,				seq_length - 1 ,				size=(batch_size,)      )
																	for batch_idx, start_index in enumerate(lowerCamelCase__      ):
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = 1
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = 1
																	_lowerCamelCase					     = pt_model_class(lowerCamelCase__      ).eval()
																	_lowerCamelCase					     = model_class(lowerCamelCase__ ,				dtype=jnp.floataa      )

																	_lowerCamelCase					     = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,				lowerCamelCase__      )
																	_lowerCamelCase					     = fx_state

																	with torch.no_grad():
																					_lowerCamelCase					     = pt_model(**lowerCamelCase__      ).to_tuple()

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																					pt_model.save_pretrained(lowerCamelCase__      )
																					_lowerCamelCase					     = model_class.from_pretrained(lowerCamelCase__ ,				from_pt=lowerCamelCase__      )

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

					@is_pt_flax_cross_test
					def 	snake_case__					(			self      ):
									_lowerCamelCase     ,   _lowerCamelCase					     = 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
																	_lowerCamelCase					     = self._prepare_for_class(lowerCamelCase__ ,				lowerCamelCase__      )
																	_lowerCamelCase					     = {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																	# load corresponding PyTorch class
																	_lowerCamelCase					     = model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	_lowerCamelCase					     = getattr(lowerCamelCase__ ,				lowerCamelCase__      )

																	_lowerCamelCase					     = pt_model_class(lowerCamelCase__      ).eval()
																	_lowerCamelCase					     = model_class(lowerCamelCase__ ,				dtype=jnp.floataa      )

																	_lowerCamelCase					     = load_flax_weights_in_pytorch_model(lowerCamelCase__ ,				fx_model.params      )
																	_lowerCamelCase     ,   _lowerCamelCase					     = pt_inputs['''input_ids'''].shape
																	_lowerCamelCase					     = np.random.randint(0 ,				seq_length - 1 ,				size=(batch_size,)      )
																	for batch_idx, start_index in enumerate(lowerCamelCase__      ):
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = 1
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = 1

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

																	with torch.no_grad():
																					_lowerCamelCase					     = pt_model(**lowerCamelCase__      ).to_tuple()

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																					fx_model.save_pretrained(lowerCamelCase__      )
																					_lowerCamelCase					     = pt_model_class.from_pretrained(lowerCamelCase__ ,				from_flax=lowerCamelCase__      )

																	with torch.no_grad():
																					_lowerCamelCase					     = pt_model_loaded(**lowerCamelCase__      ).to_tuple()

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





					@slow
					def 	snake_case__					(			self      ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase					     = model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''      )
													_lowerCamelCase					     = model(np.ones((1, 1)      )      )
													self.assertIsNotNone(lowerCamelCase__      )





"""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()
__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				List[nn.Module]        =       field(default_factory=A__			)
					lowercase__      :				list        =       field(default_factory=A__			)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 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 ,				lowerCamelCase__      ):
									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      ):
									# 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 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				nn.Module
					lowercase__      :				int        =       0
					lowercase__      :				List        =       field(default_factory=A__			)
					lowercase__      :				List        =       field(default_factory=A__			)

					def __call__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = Tracker(self.dest      )(lowerCamelCase__      ).parametrized
									_lowerCamelCase					     = Tracker(self.src      )(lowerCamelCase__      ).parametrized

									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.src_skip ,				lowerCamelCase__      )      )
									_lowerCamelCase					     = 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_(      lowercase_   :							nn.Module  ,      lowercase_   :							nn.Module							)		->				nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				_lowerCamelCase					     = from_model.state_dict()
				_lowerCamelCase					     = our_model.state_dict()
				_lowerCamelCase					     = our_model.config
				_lowerCamelCase					     = []
				for stage_idx in range(len(config.hidden_sizes							)							):
								for block_id in range(config.depths[stage_idx]							):
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)							)
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_lowerCamelCase					     = 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:
								_lowerCamelCase					     = from_state_dict.pop(lowercase_							)

				our_model.load_state_dict(lowercase_							)
				return our_model



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							VanConfig  ,      lowercase_   :							str  ,      lowercase_   :							nn.Module  ,      lowercase_   :							Path  ,      lowercase_   :							bool = True  ,      )		->				Union[str, Any]:
				print(F"""Downloading weights for {name}..."""							)
				_lowerCamelCase					     = cached_download(lowercase_							)
				print(F"""Converting {name}..."""							)
				_lowerCamelCase					     = torch.load(lowercase_							)['''state_dict''']
				from_model.load_state_dict(lowercase_							)
				from_model.eval()
				with torch.no_grad():
								_lowerCamelCase					     = VanForImageClassification(lowercase_							).eval()
								_lowerCamelCase					     = ModuleTransfer(src=lowercase_  ,      dest=lowercase_							)
								_lowerCamelCase					     = torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(lowercase_							)
								_lowerCamelCase					     = copy_parameters(lowercase_  ,      lowercase_							)

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

				_lowerCamelCase					     = name
				print(lowercase_							)

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

								# we can use the convnext one
								_lowerCamelCase					     = 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=lowercase_  ,      )

								print(F"""Pushed {checkpoint_name}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Path  ,      lowercase_   :							str = None  ,      lowercase_   :							bool = True							)		->				List[str]:
				_lowerCamelCase					     = '''imagenet-1k-id2label.json'''
				_lowerCamelCase					     = 10_00

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

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

				_lowerCamelCase					     = partial(lowercase_  ,      num_labels=lowercase_  ,      idalabel=lowercase_  ,      labelaid=lowercase_							)

				_lowerCamelCase					     = {
				    '''van-tiny''': ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 1_60, 2_56]  ,      depths=[3, 3, 5, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-small''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[2, 2, 4, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-base''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 3, 12, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-large''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 5, 27, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': van_tiny,
				    '''van-small''': van_small,
				    '''van-base''': van_base,
				    '''van-large''': van_large,
				}

				_lowerCamelCase					     = {
				    '''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(
								    lowercase_  ,      names_to_config[model_name]  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    lowercase_  ,      lowercase_  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )


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

			__SCREAMING_SNAKE_CASE     :  int										=					parser.parse_args()
			__SCREAMING_SNAKE_CASE     :  Path										=					args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			__SCREAMING_SNAKE_CASE     :  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)
