code: 
  
  
  import inspect
  import unittest
  
  import requests
  
  from transformers import MgpstrConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  						import torch
  						from torch import nn
  
  						from transformers import MgpstrForSceneTextRecognition
  
  
  if is_vision_available():
  						from PIL import Image
  
  						from transformers import MgpstrProcessor
  
  
  
  class 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=False	,     __UpperCamelCase=13	,     __UpperCamelCase=(32, 1_28)	,     __UpperCamelCase=4	,     __UpperCamelCase=3	,     __UpperCamelCase=27	,     __UpperCamelCase=38	,     __UpperCamelCase=99	,     __UpperCamelCase=99	,     __UpperCamelCase=32	,     __UpperCamelCase=5	,     __UpperCamelCase=4	,     __UpperCamelCase=4.0	,     __UpperCamelCase=2_57	,     __UpperCamelCase=None	,     )->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=parent
  						A__     :							Optional[int]   				=is_training
  						A__     :							Dict   				=batch_size
  						A__     :							Any   				=image_size
  						A__     :							str   				=patch_size
  						A__     :							Optional[Any]   				=num_channels
  						A__     :							Optional[int]   				=max_token_length
  						A__     :							str   				=num_character_labels
  						A__     :							Optional[Any]   				=num_bpe_labels
  						A__     :							Optional[int]   				=num_wordpiece_labels
  						A__     :							Optional[Any]   				=hidden_size
  						A__     :							int   				=num_hidden_layers
  						A__     :							Tuple   				=num_attention_heads
  						A__     :							Union[str, Any]   				=mlp_ratio
  						A__     :							List[Any]   				=patch_embeds_hidden_size
  						A__     :							Tuple   				=output_hidden_states
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)
  						A__     :							Union[str, Any]   				=self.get_config()
  						return config, pixel_values
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						return MgpstrConfig(
  						    image_size=self.image_size	,     patch_size=self.patch_size	,     num_channels=self.num_channels	,     max_token_length=self.max_token_length	,     num_character_labels=self.num_character_labels	,     num_bpe_labels=self.num_bpe_labels	,     num_wordpiece_labels=self.num_wordpiece_labels	,     hidden_size=self.hidden_size	,     num_hidden_layers=self.num_hidden_layers	,     num_attention_heads=self.num_attention_heads	,     mlp_ratio=self.mlp_ratio	,     output_hidden_states=self.output_hidden_states	,     )
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=MgpstrForSceneTextRecognition(__UpperCamelCase							)
  						model.to(__UpperCamelCase							)
  						model.eval()
  						with torch.no_grad():
  										A__     :							List[Any]   				=model(__UpperCamelCase							)
  						self.parent.assertEqual(
  						    generated_ids[0][0].shape	,     (self.batch_size, self.max_token_length, self.num_character_labels)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.prepare_config_and_inputs()
  						A__				,  A__     :							Any   				=config_and_inputs
  						A__     :							Any   				={'''pixel_values''': pixel_values}
  						return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 			a      (      UpperCamelCase_      ,UpperCamelCase_      ,unittest.TestCase   ):
  		__lowercase													=		(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
  		__lowercase													=		{"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
  		__lowercase													=		False
  
  		__lowercase													=		False
  		__lowercase													=		False
  		__lowercase													=		False
  		__lowercase													=		False
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=MgpstrModelTester(self							)
  						A__     :							Union[str, Any]   				=ConfigTester(self	,     config_class=__UpperCamelCase	,     has_text_modality=__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						self.config_tester.run_common_tests()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*__UpperCamelCase							)
  
  
  
  
  
  
  		@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''							)
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Optional[Any]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							Optional[Any]   				=model_class(__UpperCamelCase							)
  										self.assertIsInstance(model.get_input_embeddings()	,     (nn.Module)							)
  										A__     :							Any   				=model.get_output_embeddings()
  										self.assertTrue(x is None or isinstance(__UpperCamelCase	,     nn.Linear							)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Any   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							Union[str, Any]   				=model_class(__UpperCamelCase							)
  										A__     :							Dict   				=inspect.signature(model.forward							)
  										# signature.parameters is an OrderedDict => so arg_names order is deterministic
  										A__     :							List[Any]   				=[*signature.parameters.keys()]
  
  										A__     :							List[str]   				=['''pixel_values''']
  										self.assertListEqual(arg_names[:1]	,     __UpperCamelCase							)
  
  
  
  
  
  
  		@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''							)
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							List[Any]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										if not model_class.supports_gradient_checkpointing:
  														continue
  
  										A__     :							Any   				=True
  										A__     :							Union[str, Any]   				=model_class(__UpperCamelCase							)
  										self.assertTrue(model.is_gradient_checkpointing							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						def check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							):
  										A__     :							List[Any]   				=model_class(__UpperCamelCase							)
  										model.to(__UpperCamelCase							)
  										model.eval()
  
  										with torch.no_grad():
  														A__     :							Any   				=model(**self._prepare_for_class(__UpperCamelCase	,     __UpperCamelCase							)							)
  
  										A__     :							Optional[int]   				=outputs.hidden_states
  
  										A__     :							int   				=getattr(
  										    self.model_tester	,     '''expected_num_hidden_layers'''	,     self.model_tester.num_hidden_layers + 1							)
  										self.assertEqual(len(__UpperCamelCase							)	,     __UpperCamelCase							)
  
  										self.assertListEqual(
  										    list(hidden_states[0].shape[-2:]							)	,     [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]	,     )
  
  						A__				,  A__     :							Optional[Any]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							List[str]   				=True
  										check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)
  
  										# check that output_hidden_states also work using config
  										del inputs_dict["output_hidden_states"]
  										A__     :							Tuple   				=True
  
  										check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							List[str]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						A__     :							Tuple   				=_config_zero_init(__UpperCamelCase							)
  						for model_class in self.all_model_classes:
  										A__     :							Optional[int]   				=model_class(config=__UpperCamelCase							)
  										for name, param in model.named_parameters():
  														if isinstance(__UpperCamelCase	,     (nn.Linear, nn.Convad, nn.LayerNorm)							):
  																		if 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'	,     )
  
  
  
  
  
  
  		@unittest.skip(reason='''Retain_grad is tested in individual model tests'''							)
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
  				A__     :							List[Any]   				='''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
  				A__     :							List[str]   				=Image.open(requests.get(snake_case_,	stream=snake_case_	).raw	).convert('''RGB'''	)
  				return im
  
  
  
  
  @require_vision
  @require_torch
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		@slow
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							int   				='''alibaba-damo/mgp-str-base'''
  						A__     :							Any   				=MgpstrForSceneTextRecognition.from_pretrained(__UpperCamelCase							).to(__UpperCamelCase							)
  						A__     :							int   				=MgpstrProcessor.from_pretrained(__UpperCamelCase							)
  
  						A__     :							Tuple   				=prepare_img()
  						A__     :							str   				=processor(images=__UpperCamelCase	,     return_tensors='''pt'''							).pixel_values.to(__UpperCamelCase							)
  
  						# forward pass
  						with torch.no_grad():
  										A__     :							Any   				=model(__UpperCamelCase							)
  
  						# verify the logits
  						self.assertEqual(outputs.logits[0].shape	,     torch.Size((1, 27, 38)							)							)
  
  						A__     :							Dict   				=processor.batch_decode(outputs.logits							)
  						A__     :							int   				='''ticket'''
  
  						self.assertEqual(out_strs['''generated_text'''][0]	,     __UpperCamelCase							)
  
  						A__     :							Tuple   				=torch.tensor(
  						    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]	,     device=__UpperCamelCase	,     )
  
  						self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]	,     __UpperCamelCase	,     atol=1E-4							)							)
  
  
  
  
style_context: 
  
  
  import argparse
  import json
  import os
  import re
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from pprint import pprint
  from typing import Dict, List, Tuple
  
  import torch
  import torch.nn as nn
  from classy_vision.models.regnet import RegNet, RegNetParams
  from huggingface_hub import cached_download, hf_hub_url
  from torch import Tensor
  from vissl.models.model_helpers import get_trunk_forward_outputs
  
  from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  __lowerCamelCase       :       Union[str, Any]				   =			logging.get_logger()
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		42
  		__lowercase													=		field(default_factory=UpperCamelCase_   )
  		__lowercase													=		field(default_factory=UpperCamelCase_   )
  		__lowercase													=		field(default_factory=UpperCamelCase_   )
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=len(list(m.modules()							)							) == 1 or isinstance(__UpperCamelCase	,     nn.Convad							) or isinstance(__UpperCamelCase	,     nn.BatchNormad							)
  						if has_not_submodules:
  										self.traced.append(__UpperCamelCase							)
  										A__     :							Union[str, Any]   				=m
  
  
  
  
  
  
  		def __call__(  self	,     __UpperCamelCase							)->			Any:
  						'''simple docstring'''
  
  
  						for name, m in self.module.named_modules():
  										self.handles.append(m.register_forward_hook(partial(self._forward_hook	,     name=__UpperCamelCase							)							)							)
  						self.module(__UpperCamelCase							)
  						[x.remove() for x in self.handles]
  						return self
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}
  
  
  
  
  
  
  
  class 			a      (      nn.Module   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						super().__init__()
  
  						A__     :							List[Tuple[str, nn.Module]]   				=[]
  						# - get the stem
  						feature_blocks.append(('''conv1''', model.stem)							)
  						# - get all the feature blocks
  						for k, v in model.trunk_output.named_children():
  										assert k.startswith('''block'''							), F'Unexpected layer name {k}'
  										A__     :							Any   				=len(__UpperCamelCase							) + 1
  										feature_blocks.append((F'res{block_index}', v)							)
  
  						A__     :							Tuple   				=nn.ModuleDict(__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						return get_trunk_forward_outputs(
  						    __UpperCamelCase	,     out_feat_keys=__UpperCamelCase	,     feature_blocks=self._feature_blocks	,     )
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict[str, str]:
  				A__     :							Any   				=RegNetConfig(depths=[2, 7, 1_7, 1],	hidden_sizes=[8, 8, 8, 8],	groups_width=8	)
  				if "in1k" in model_name:
  								A__     :							Any   				=RegNetForImageClassification(snake_case_	)
  				else:
  								A__     :							Tuple   				=RegNetModel(snake_case_	)
  				# create from model (with small weights)
  				A__     :							List[str]   				=FakeRegNetVisslWrapper(
  				    RegNet(FakeRegNetParams(depth=2_7,	group_width=1_0_1_0,	w_a=1_7_4_4,	w_a=6_2_0.8_3,	w_m=2.5_2	)	)	)
  
  				with torch.no_grad():
  								A__     :							str   				=from_model.eval()
  								A__     :							int   				=our_model.eval()
  
  								A__     :							Optional[int]   				=torch.randn((1, 3, 3_2, 3_2)	)
  								# trace both
  								A__     :							int   				=Tracker(snake_case_	)
  								A__     :							Optional[int]   				=dest_tracker(snake_case_	).parametrized
  
  								pprint(dest_tracker.nameamodule	)
  								A__     :							Optional[int]   				=Tracker(snake_case_	)
  								A__     :							Any   				=src_tracker(snake_case_	).parametrized
  
  				# convert the keys -> module dict to keys -> params
  				def to_params_dict(snake_case_	):
  								A__     :							Optional[Any]   				=OrderedDict()
  								for name, module in dict_with_modules.items():
  												for param_name, param in module.state_dict().items():
  																A__     :							Union[str, Any]   				=param
  								return params_dict
  
  				A__     :							Optional[int]   				={}
  
  				A__     :							int   				=to_params_dict(snake_case_	)
  				A__     :							int   				=to_params_dict(snake_case_	)
  
  				for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),	dst_state_dict.items()	):
  								A__     :							int   				=dest_key
  								logger.info(f'{src_key} -> {dest_key}'	)
  				# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  				if "in1k" in model_name:
  								A__     :							str   				='''classifier.1.weight'''
  								A__     :							Union[str, Any]   				='''classifier.1.bias'''
  
  				return from_to_ours_keys
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_ = None,	snake_case_ = True	)			->							int:
  				A__     :							Tuple   				='''imagenet-1k-id2label.json'''
  				A__     :							Any   				=1_0_0_0
  
  				A__     :							int   				='''huggingface/label-files'''
  				A__     :							Tuple   				=num_labels
  				A__     :							Optional[int]   				=json.load(open(cached_download(hf_hub_url(snake_case_,	snake_case_,	repo_type='''dataset'''	)	),	'''r'''	)	)
  				A__     :							str   				={int(snake_case_	): v for k, v in idalabel.items()}
  
  				A__     :							int   				=idalabel
  				A__     :							Tuple   				={v: k for k, v in idalabel.items()}
  
  				A__     :							str   				=partial(snake_case_,	num_labels=snake_case_,	idalabel=snake_case_,	labelaid=snake_case_	)
  
  				A__     :							Tuple   				={
  				    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
  				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
  				    # finetuned on imagenet
  				    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
  				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
  				}
  
  				# add seer weights logic
  				def load_using_classy_vision(snake_case_	) -> Tuple[Dict, Dict]:
  								A__     :							Optional[int]   				=torch.hub.load_state_dict_from_url(snake_case_,	model_dir=str(snake_case_	),	map_location='''cpu'''	)
  								# check if we have a head, if yes add it
  								A__     :							Dict   				=files['''classy_state_dict''']['''base_model''']['''model''']
  								return model_state_dict["trunk"], model_state_dict["heads"]
  
  				A__     :							Any   				={
  				    '''regnet-y-10b-seer''': partial(
  				        snake_case_,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''',	),
  				    '''regnet-y-10b-seer-in1k''': partial(
  				        snake_case_,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''',	),
  				}
  
  				A__     :							Tuple   				=get_from_to_our_keys(snake_case_	)
  
  				if not (save_directory / f'{model_name}.pth').exists():
  								logger.info('''Loading original state_dict.'''	)
  								A__				,  A__     :							Any   				=names_to_from_model[model_name]()
  								A__     :							int   				=from_state_dict_trunk
  								if "in1k" in model_name:
  												# add the head
  												A__     :							Union[str, Any]   				={**from_state_dict_trunk, **from_state_dict_head}
  								logger.info('''Done!'''	)
  
  								A__     :							List[str]   				={}
  
  								A__     :							int   				=list(from_state_dict.keys()	)
  								A__     :							Optional[Any]   				=R'''\.block.-part.'''
  								# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
  								for key in from_state_dict.keys():
  												# remove the weird "block[0,1]-part" from the key
  												A__     :							Tuple   				=re.sub(snake_case_,	'''''',	snake_case_	)
  												# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
  												A__     :							Any   				=from_to_ours_keys[src_key]
  												# store the parameter with our key
  												A__     :							str   				=from_state_dict[key]
  												not_used_keys.remove(snake_case_	)
  								# check that all keys have been updated
  								assert len(snake_case_	) == 0, f'Some keys where not used {",".join(snake_case_	)}'
  
  								logger.info(f'The following keys were not used: {",".join(snake_case_	)}'	)
  
  								# save our state dict to disk
  								torch.save(snake_case_,	save_directory / f'{model_name}.pth'	)
  
  								del converted_state_dict
  				else:
  								logger.info('''The state_dict was already stored on disk.'''	)
  				if push_to_hub:
  								logger.info(f'Token is {os.environ["HF_TOKEN"]}'	)
  								logger.info('''Loading our model.'''	)
  								# create our model
  								A__     :							Optional[int]   				=names_to_config[model_name]
  								A__     :							Any   				=RegNetModel
  								if "in1k" in model_name:
  												A__     :							Optional[int]   				=RegNetForImageClassification
  								A__     :							Optional[int]   				=our_model_func(snake_case_	)
  								# place our model to the meta device (so remove all the weights)
  								our_model.to(torch.device('''meta'''	)	)
  								logger.info('''Loading state_dict in our model.'''	)
  								# load state dict
  								A__     :							Any   				=our_model.state_dict().keys()
  								PreTrainedModel._load_pretrained_model_low_mem(
  								    snake_case_,	snake_case_,	[save_directory / f'{model_name}.pth']	)
  								logger.info('''Finally, pushing!'''	)
  								# push it to hub
  								our_model.push_to_hub(
  								    repo_path_or_name=save_directory / model_name,	commit_message='''Add model''',	output_dir=save_directory / model_name,	)
  								A__     :							List[Any]   				=3_8_4
  								# we can use the convnext one
  								A__     :							List[Any]   				=AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''',	size=snake_case_	)
  								image_processor.push_to_hub(
  								    repo_path_or_name=save_directory / model_name,	commit_message='''Add image processor''',	output_dir=save_directory / model_name,	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       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 regnet* architecture,"
  						        " currently: regnetx-*, regnety-*. 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(
  						    "--push_to_hub",
  						    default=True,
  						    type=bool,
  						    required=False,
  						    help="If True, push model and image processor to the hub.",
  						)
  
  						__lowerCamelCase       :       List[str]				   =			parser.parse_args()
  
  						__lowerCamelCase       :       Path				   =			args.pytorch_dump_folder_path
  						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
  
  
  
  