import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor




class  _UpperCAmelCase       :



 '''simple docstring'''




 def __init__(						self							:					Optional[int]				,      lowercase_							:					str				,      lowercase_							:					Any=3				,      lowercase_							:					List[Any]=32				,      lowercase_							:					List[str]=3				,      lowercase_							:					str=10				,      lowercase_							:					Any=[10, 20, 30, 40]				,      lowercase_							:					List[str]=[1, 1, 2, 1]				,      lowercase_							:					str=True				,      lowercase_							:					List[Any]=True				,      lowercase_							:					Tuple="relu"				,      lowercase_							:					str=3				,      lowercase_							:					Optional[int]=None				,      lowercase_							:					Union[str, Any]=["stage2", "stage3", "stage4"]				,      lowercase_							:					Optional[int]=[2, 3, 4]				,      )		->     int:


   """simple docstring"""







   _UpperCamelCase				=  parent
   _UpperCamelCase				=  batch_size
   _UpperCamelCase				=  image_size
   _UpperCamelCase				=  num_channels
   _UpperCamelCase				=  embeddings_size
   _UpperCamelCase				=  hidden_sizes
   _UpperCamelCase				=  depths
   _UpperCamelCase				=  is_training
   _UpperCamelCase				=  use_labels
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  num_labels
   _UpperCamelCase				=  scope
   _UpperCamelCase				=  len(lowercase_)
   _UpperCamelCase				=  out_features
   _UpperCamelCase				=  out_indices







 def 						__UpperCAmelCase							(						self							:					str)		->     Any:


   """simple docstring"""







   _UpperCamelCase				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   _UpperCamelCase				=  None
   if self.use_labels:
     _UpperCamelCase				=  ids_tensor([self.batch_size]				,      self.num_labels)

   _UpperCamelCase				=  self.get_config()

   return config, pixel_values, labels







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[Any]:


   """simple docstring"""







   return ResNetConfig(
       num_channels=self.num_channels				,      embeddings_size=self.embeddings_size				,      hidden_sizes=self.hidden_sizes				,      depths=self.depths				,      hidden_act=self.hidden_act				,      num_labels=self.num_labels				,      out_features=self.out_features				,      out_indices=self.out_indices				,      )







 def 						__UpperCAmelCase							(						self							:					str				,      lowercase_							:					int				,      lowercase_							:					Union[str, Any]				,      lowercase_							:					Optional[Any])		->     Optional[Any]:


   """simple docstring"""







   _UpperCamelCase				=  ResNetModel(config=lowercase_)
   model.to(lowercase_)
   model.eval()
   _UpperCamelCase				=  model(lowercase_)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape				,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,      )







 def 						__UpperCAmelCase							(						self							:					List[Any]				,      lowercase_							:					str				,      lowercase_							:					List[str]				,      lowercase_							:					Union[str, Any])		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.num_labels
   _UpperCamelCase				=  ResNetForImageClassification(lowercase_)
   model.to(lowercase_)
   model.eval()
   _UpperCamelCase				=  model(lowercase_				,      labels=lowercase_)
   self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels))







 def 						__UpperCAmelCase							(						self							:					Any				,      lowercase_							:					Tuple				,      lowercase_							:					Optional[int]				,      lowercase_							:					Union[str, Any])		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  ResNetBackbone(config=lowercase_)
   model.to(lowercase_)
   model.eval()
   _UpperCamelCase				=  model(lowercase_)

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps)				,      len(config.out_features))
   self.parent.assertListEqual(list(result.feature_maps[0].shape)				,      [self.batch_size, self.hidden_sizes[1], 4, 4])

   # verify channels
   self.parent.assertEqual(len(model.channels)				,      len(config.out_features))
   self.parent.assertListEqual(model.channels				,      config.hidden_sizes[1:])

   # verify backbone works with out_features=None
   _UpperCamelCase				=  None
   _UpperCamelCase				=  ResNetBackbone(config=lowercase_)
   model.to(lowercase_)
   model.eval()
   _UpperCamelCase				=  model(lowercase_)

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps)				,      1)
   self.parent.assertListEqual(list(result.feature_maps[0].shape)				,      [self.batch_size, self.hidden_sizes[-1], 1, 1])

   # verify channels
   self.parent.assertEqual(len(model.channels)				,      1)
   self.parent.assertListEqual(model.channels				,      [config.hidden_sizes[-1]])







 def 						__UpperCAmelCase							(						self							:					Union[str, Any])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  self.prepare_config_and_inputs()
   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  config_and_inputs
   _UpperCamelCase				=  {"pixel_values": pixel_values}
   return config, inputs_dict




@require_torch
class  _UpperCAmelCase       (			lowerCAmelCase,       lowerCAmelCase,       unittest.TestCase   ):



 '''simple docstring'''




 __A           = (
     (
         ResNetModel,
         ResNetForImageClassification,
         ResNetBackbone,
     )
     if is_torch_available()
     else ()
 )
 __A           = (
     {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
     if is_torch_available()
     else {}
 )

 __A           = True
 __A           = False
 __A           = False
 __A           = False
 __A           = False







 def 						__UpperCAmelCase							(						self							:					Dict)		->     str:


   """simple docstring"""







   _UpperCamelCase				=  ResNetModelTester(self)
   _UpperCamelCase				=  ConfigTester(self				,      config_class=lowercase_				,      has_text_modality=lowercase_)







 def 						__UpperCAmelCase							(						self							:					Union[str, Any])		->     Optional[int]:


   """simple docstring"""







   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 						__UpperCAmelCase							(						self							:					Optional[Any])		->     str:


   """simple docstring"""







   return







 @unittest.skip(reason="ResNet does not use inputs_embeds")
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[str]:


   """simple docstring"""







   pass







 @unittest.skip(reason="ResNet does not support input and output embeddings")
 def 						__UpperCAmelCase							(						self							:					Dict)		->     Union[str, Any]:


   """simple docstring"""







   pass







 def 						__UpperCAmelCase							(						self							:					str)		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.model_tester.prepare_config_and_inputs_for_common()

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

     _UpperCamelCase				=  ["pixel_values"]
     self.assertListEqual(arg_names[:1]				,      lowercase_)







 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase_)







 def 						__UpperCAmelCase							(						self							:					Any)		->     Dict:


   """simple docstring"""







   _UpperCamelCase				=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*lowercase_)







 def 						__UpperCAmelCase							(						self							:					Tuple)		->     Optional[Any]:


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _UpperCamelCase				=  model_class(config=lowercase_)
     for name, module in model.named_modules():
       if isinstance(lowercase_				,      (nn.BatchNormad, nn.GroupNorm)):
         self.assertTrue(
             torch.all(module.weight == 1)				,      msg=f'Parameter {name} of model {model_class} seems not properly initialized'				,      )
         self.assertTrue(
             torch.all(module.bias == 0)				,      msg=f'Parameter {name} of model {model_class} seems not properly initialized'				,      )







 def 						__UpperCAmelCase							(						self							:					int)		->     str:


   """simple docstring"""







   def check_hidden_states_output(lowercase_							:					int				,      lowercase_							:					int				,      lowercase_							:					str):
     _UpperCamelCase				=  model_class(lowercase_)
     model.to(lowercase_)
     model.eval()

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

     _UpperCamelCase				=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _UpperCamelCase				=  self.model_tester.num_stages
     self.assertEqual(len(lowercase_)				,      expected_num_stages + 1)

     # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:])				,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,      )

   _UpperCamelCase	,		_UpperCamelCase				=  self.model_tester.prepare_config_and_inputs_for_common()
   _UpperCamelCase				=  ["basic", "bottleneck"]
   for model_class in self.all_model_classes:
     for layer_type in layers_type:
       _UpperCamelCase				=  layer_type
       _UpperCamelCase				=  True
       check_hidden_states_output(lowercase_				,      lowercase_				,      lowercase_)

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

       check_hidden_states_output(lowercase_				,      lowercase_				,      lowercase_)







 @unittest.skip(reason="ResNet does not use feedforward chunking")
 def 						__UpperCAmelCase							(						self							:					Dict)		->     Optional[int]:


   """simple docstring"""







   pass







 def 						__UpperCAmelCase							(						self							:					List[str])		->     int:


   """simple docstring"""







   _UpperCamelCase				=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase_)







 @slow
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Any:


   """simple docstring"""







   for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _UpperCamelCase				=  ResNetModel.from_pretrained(lowercase_)
     self.assertIsNotNone(lowercase_)


def 							lowerCAmelCase__    (       )	->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
  return image




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



 '''simple docstring'''




 @cached_property
 def 						__UpperCAmelCase							(						self							:					List[str])		->     str:


   """simple docstring"""







   return (
       AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
       if is_vision_available()
       else None
   )







 @slow
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     str:


   """simple docstring"""







   _UpperCamelCase				=  ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase_)

   _UpperCamelCase				=  self.default_image_processor
   _UpperCamelCase				=  prepare_img()
   _UpperCamelCase				=  image_processor(images=lowercase_				,      return_tensors="pt").to(lowercase_)

   # forward pass
   with torch.no_grad():
     _UpperCamelCase				=  model(**lowercase_)

   # verify the logits
   _UpperCamelCase				=  torch.Size((1, 1000))
   self.assertEqual(outputs.logits.shape				,      lowercase_)

   _UpperCamelCase				=  torch.tensor([-11.10_69, -9.78_77, -8.37_77]).to(lowercase_)

   self.assertTrue(torch.allclose(outputs.logits[0, :3]				,      lowercase_				,      atol=1e-4))




@require_torch
class  _UpperCAmelCase       (			lowerCAmelCase,       unittest.TestCase   ):



 '''simple docstring'''




 __A           = (ResNetBackbone,) if is_torch_available() else ()
 __A           = False
 __A           = ResNetConfig







 def 						__UpperCAmelCase							(						self							:					Union[str, Any])		->     Dict:


   """simple docstring"""







   _UpperCamelCase				=  ResNetModelTester(self)





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__   = logging.get_logger()




@dataclass
class  _UpperCAmelCase       :



 '''simple docstring'''




 __A           = 42
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Union[str, Any]				,      lowercase_							:					Tensor				,      lowercase_							:					Tensor				,      lowercase_							:					str)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  len(list(m.modules())) == 1 or isinstance(lowercase_				,      nn.Convad) or isinstance(lowercase_				,      nn.BatchNormad)
   if has_not_submodules:
     self.traced.append(lowercase_)
     _UpperCamelCase				=  m







 def __call__(						self							:					Optional[Any]				,      lowercase_							:					Tensor)		->     List[str]:


   """simple docstring"""







   for name, m in self.module.named_modules():
     self.handles.append(m.register_forward_hook(partial(self._forward_hook				,      name=lowercase_)))
   self.module(lowercase_)
   [x.remove() for x in self.handles]
   return self







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







   return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}




class  _UpperCAmelCase       (			nn.Module   ):



 '''simple docstring'''




 def __init__(						self							:					Tuple				,      lowercase_							:					nn.Module)		->     List[str]:


   """simple docstring"""







   super().__init__()

   _UpperCamelCase				=  []
   # - 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}'
     _UpperCamelCase				=  len(lowercase_) + 1
     feature_blocks.append((f'res{block_index}', v))

   _UpperCamelCase				=  nn.ModuleDict(lowercase_)







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					Tensor)		->     int:


   """simple docstring"""







   return get_trunk_forward_outputs(
       lowercase_				,      out_feat_keys=lowercase_				,      feature_blocks=self._feature_blocks				,      )




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[Any]:


   """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 							lowerCAmelCase__    (       a__      )	->Dict[str, str]:




  '''simple docstring'''

  _UpperCamelCase				=  RegNetConfig(depths=[2, 7, 17, 1]  ,			hidden_sizes=[8, 8, 8, 8]  ,			groups_width=8      )
  if "in1k" in model_name:
    _UpperCamelCase				=  RegNetForImageClassification(a__      )
  else:
    _UpperCamelCase				=  RegNetModel(a__      )
  # create from model (with small weights)
  _UpperCamelCase				=  FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27  ,			group_width=1_010  ,			w_a=1_744  ,			w_a=620.83  ,			w_m=2.52      )      )      )

  with torch.no_grad():
    _UpperCamelCase				=  from_model.eval()
    _UpperCamelCase				=  our_model.eval()

    _UpperCamelCase				=  torch.randn((1, 3, 32, 32)      )
    # trace both
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  dest_tracker(a__      ).parametrized

    pprint(dest_tracker.nameamodule      )
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  src_tracker(a__      ).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(a__      ):
    _UpperCamelCase				=  OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        _UpperCamelCase				=  param
    return params_dict

  _UpperCamelCase				=  {}

  _UpperCamelCase				=  to_params_dict(a__      )
  _UpperCamelCase				=  to_params_dict(a__      )

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,			dst_state_dict.items()      ):
    _UpperCamelCase				=  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:
    _UpperCamelCase				=  "classifier.1.weight"
    _UpperCamelCase				=  "classifier.1.bias"

  return from_to_ours_keys


def 							lowerCAmelCase__    (       a__  ,			a__ = None  ,			a__ = True      )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  "imagenet-1k-id2label.json"
  _UpperCamelCase				=  1_000

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

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

  _UpperCamelCase				=  partial(a__  ,			num_labels=a__  ,			idalabel=a__  ,			labelaid=a__      )

  _UpperCamelCase				=  {
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
      # finetuned on imagenet
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
  }

  # add seer weights logic
  def load_using_classy_vision(a__      ) -> Tuple[Dict, Dict]:
    _UpperCamelCase				=  torch.hub.load_state_dict_from_url(a__  ,			model_dir=str(a__      )  ,			map_location="cpu"      )
    # check if we have a head, if yes add it
    _UpperCamelCase				=  files["classy_state_dict"]["base_model"]["model"]
    return model_state_dict["trunk"], model_state_dict["heads"]

  _UpperCamelCase				=  {
      "regnet-y-10b-seer": partial(
          a__  ,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"  ,			),
      "regnet-y-10b-seer-in1k": partial(
          a__  ,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"  ,			),
  }

  _UpperCamelCase				=  get_from_to_our_keys(a__      )

  if not (save_directory / f'{model_name}.pth').exists():
    logger.info("Loading original state_dict."      )
    _UpperCamelCase	,		_UpperCamelCase				=  names_to_from_model[model_name]()
    _UpperCamelCase				=  from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      _UpperCamelCase				=  {**from_state_dict_trunk, **from_state_dict_head}
    logger.info("Done!"      )

    _UpperCamelCase				=  {}

    _UpperCamelCase				=  list(from_state_dict.keys()      )
    _UpperCamelCase				=  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
      _UpperCamelCase				=  re.sub(a__  ,			""  ,			a__      )
      # 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
      _UpperCamelCase				=  from_to_ours_keys[src_key]
      # store the parameter with our key
      _UpperCamelCase				=  from_state_dict[key]
      not_used_keys.remove(a__      )
    # check that all keys have been updated
    assert len(a__      ) == 0, f'Some keys where not used {",".join(a__      )}'

    logger.info(f'The following keys were not used: {",".join(a__      )}'      )

    # save our state dict to disk
    torch.save(a__  ,			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
    _UpperCamelCase				=  names_to_config[model_name]
    _UpperCamelCase				=  RegNetModel
    if "in1k" in model_name:
      _UpperCamelCase				=  RegNetForImageClassification
    _UpperCamelCase				=  our_model_func(a__      )
    # 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
    _UpperCamelCase				=  our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        a__  ,			a__  ,			[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  ,			)
    _UpperCamelCase				=  384
    # we can use the convnext one
    _UpperCamelCase				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,			size=a__      )
    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__   = 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__   = parser.parse_args()

   lowerCamelCase__   = 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)




