code: 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()
  UpperCAmelCase     :   Optional[int]					=				logging.get_logger()
  
  
  
  
  @dataclass
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	List[str]				    = 42
   _lowerCamelCase						:	Any				    = field(default_factory=__lowerCamelCase						)
   _lowerCamelCase						:	str				    = field(default_factory=__lowerCamelCase						)
   _lowerCamelCase						:	Tuple				    = field(default_factory=__lowerCamelCase						)
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Tuple       ,			lowerCamelCase       :		int       ,			lowerCamelCase       :		int       ,			lowerCamelCase       :		str  ) ->							Any:
          _UpperCamelCase     :       List[str]  						=  len(list(m.modules()  )  ) == 1 or isinstance(_lowerCAmelCase       ,			nn.Convad  ) or isinstance(_lowerCAmelCase       ,			nn.BatchNormad  )
          if has_not_submodules:
                 self.traced.append(_lowerCAmelCase  )
                 _UpperCamelCase     :       Optional[int]  						=  m
  
   def __call__(			self       :		List[str]       ,			lowerCamelCase       :		Dict  ) ->							Tuple:
          for name, m in self.module.named_modules():
                 self.handles.append(m.register_forward_hook(partial(self._forward_hook       ,			name=_lowerCAmelCase  )  )  )
          self.module(_lowerCAmelCase  )
          [x.remove() for x in self.handles]
          return self
  
   @property
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							int:
          # check the len of the state_dict keys to see if we have learnable params
          return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()  )  ) > 0}
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       nn.Module						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		List[Any]       ,			lowerCamelCase       :		Union[str, Any]  ) ->							Union[str, Any]:
          super().__init__()
  
          _UpperCamelCase     :       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}"
                 _UpperCamelCase     :       Dict  						=  len(_lowerCAmelCase  ) + 1
                 feature_blocks.append((f"res{block_index}", v)  )
  
          _UpperCamelCase     :       Optional[int]  						=  nn.ModuleDict(_lowerCAmelCase  )
  
   def 					__snake_case					(			self       :		List[Any]       ,			lowerCamelCase       :		Optional[int]  ) ->							Dict:
          return get_trunk_forward_outputs(
              _lowerCAmelCase       ,			out_feat_keys=_lowerCAmelCase       ,			feature_blocks=self._feature_blocks       ,			)
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       __lowerCamelCase						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		Optional[int]  ) ->							Union[str, Any]:
          return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]
  
  
  
  
  
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str				):
         _UpperCamelCase     :       Dict  						=  RegNetConfig(depths=[2, 7, 17, 1]		,						hidden_sizes=[8, 8, 8, 8]		,						groups_width=8				)
         if "in1k" in model_name:
                _UpperCamelCase     :       Optional[Any]  						=  RegNetForImageClassification(__lowerCamelCase				)
         else:
                _UpperCamelCase     :       Optional[Any]  						=  RegNetModel(__lowerCamelCase				)
         # create from model (with small weights)
         _UpperCamelCase     :       str  						=  FakeRegNetVisslWrapper(
             RegNet(FakeRegNetParams(depth=27		,						group_width=1010		,						w_a=1744		,						w_a=620.83		,						w_m=2.52				)				)				)
  
         with torch.no_grad():
                _UpperCamelCase     :       List[Any]  						=  from_model.eval()
                _UpperCamelCase     :       Dict  						=  our_model.eval()
  
                _UpperCamelCase     :       Any  						=  torch.randn((1, 3, 32, 32)				)
                # trace both
                _UpperCamelCase     :       str  						=  Tracker(__lowerCamelCase				)
                _UpperCamelCase     :       Tuple  						=  dest_tracker(__lowerCamelCase				).parametrized
  
                pprint(dest_tracker.nameamodule				)
                _UpperCamelCase     :       List[str]  						=  Tracker(__lowerCamelCase				)
                _UpperCamelCase     :       List[str]  						=  src_tracker(__lowerCamelCase				).parametrized
  
         # convert the keys -> module dict to keys -> params
         def to_params_dict(lowercase_:	str				):
                _UpperCamelCase     :       Optional[Any]  						=  OrderedDict()
                for name, module in dict_with_modules.items():
                       for param_name, param in module.state_dict().items():
                              _UpperCamelCase     :       Optional[int]  						=  param
                return params_dict
  
         _UpperCamelCase     :       Optional[int]  						=  {}
  
         _UpperCamelCase     :       int  						=  to_params_dict(__lowerCamelCase				)
         _UpperCamelCase     :       Optional[int]  						=  to_params_dict(__lowerCamelCase				)
  
         for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		,						dst_state_dict.items()				):
                _UpperCamelCase     :       Optional[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:
                _UpperCamelCase     :       str  						=  """classifier.1.weight"""
                _UpperCamelCase     :       Any  						=  """classifier.1.bias"""
  
         return from_to_ours_keys
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	int = None		,						lowercase_:	Dict = True				):
         _UpperCamelCase     :       Dict  						=  """imagenet-1k-id2label.json"""
         _UpperCamelCase     :       Optional[int]  						=  1000
  
         _UpperCamelCase     :       Tuple  						=  """huggingface/label-files"""
         _UpperCamelCase     :       Union[str, Any]  						=  num_labels
         _UpperCamelCase     :       Tuple  						=  json.load(open(cached_download(hf_hub_url(__lowerCamelCase		,						__lowerCamelCase		,						repo_type='dataset'				)				)		,						'r'				)				)
         _UpperCamelCase     :       int  						=  {int(__lowerCamelCase				): v for k, v in idalabel.items()}
  
         _UpperCamelCase     :       Optional[int]  						=  idalabel
         _UpperCamelCase     :       str  						=  {v: k for k, v in idalabel.items()}
  
         _UpperCamelCase     :       Optional[int]  						=  partial(__lowerCamelCase		,						num_labels=__lowerCamelCase		,						idalabel=__lowerCamelCase		,						labelaid=__lowerCamelCase				)
  
         _UpperCamelCase     :       List[str]  						=  {
             """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
                 depths=[2, 7, 17, 1]		,						hidden_sizes=[2020, 4040, 11110, 28280]		,						groups_width=1010				),
             # finetuned on imagenet
             """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
                 depths=[2, 7, 17, 1]		,						hidden_sizes=[2020, 4040, 11110, 28280]		,						groups_width=1010				),
         }
  
         # add seer weights logic
         def load_using_classy_vision(lowercase_:	Optional[int]				) -> Tuple[Dict, Dict]:
                _UpperCamelCase     :       Optional[int]  						=  torch.hub.load_state_dict_from_url(__lowerCamelCase		,						model_dir=str(__lowerCamelCase				)		,						map_location='cpu'				)
                # check if we have a head, if yes add it
                _UpperCamelCase     :       Optional[int]  						=  files["""classy_state_dict"""]["""base_model"""]["""model"""]
                return model_state_dict["trunk"], model_state_dict["heads"]
  
         _UpperCamelCase     :       int  						=  {
             """regnet-y-10b-seer""": partial(
                 __lowerCamelCase		,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'		,						),
             """regnet-y-10b-seer-in1k""": partial(
                 __lowerCamelCase		,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'		,						),
         }
  
         _UpperCamelCase     :       Union[str, Any]  						=  get_from_to_our_keys(__lowerCamelCase				)
  
         if not (save_directory / F"{model_name}.pth").exists():
                logger.info('Loading original state_dict.'				)
                _UpperCamelCase     :       List[str]  						=  names_to_from_model[model_name]()
                _UpperCamelCase     :       Dict  						=  from_state_dict_trunk
                if "in1k" in model_name:
                       # add the head
                       _UpperCamelCase     :       Tuple  						=  {**from_state_dict_trunk, **from_state_dict_head}
                logger.info('Done!'				)
  
                _UpperCamelCase     :       int  						=  {}
  
                _UpperCamelCase     :       Dict  						=  list(from_state_dict.keys()				)
                _UpperCamelCase     :       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
                       _UpperCamelCase     :       int  						=  re.sub(__lowerCamelCase		,						''		,						__lowerCamelCase				)
                       # 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     :       Optional[Any]  						=  from_to_ours_keys[src_key]
                       # store the parameter with our key
                       _UpperCamelCase     :       Union[str, Any]  						=  from_state_dict[key]
                       not_used_keys.remove(__lowerCamelCase				)
                # check that all keys have been updated
                assert len(__lowerCamelCase				) == 0, F"Some keys where not used {','.join(__lowerCamelCase				)}"
  
                logger.info(F"The following keys were not used: {','.join(__lowerCamelCase				)}"				)
  
                # save our state dict to disk
                torch.save(__lowerCamelCase		,						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     :       Union[str, Any]  						=  names_to_config[model_name]
                _UpperCamelCase     :       Tuple  						=  RegNetModel
                if "in1k" in model_name:
                       _UpperCamelCase     :       Optional[Any]  						=  RegNetForImageClassification
                _UpperCamelCase     :       Optional[int]  						=  our_model_func(__lowerCamelCase				)
                # 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     :       int  						=  our_model.state_dict().keys()
                PreTrainedModel._load_pretrained_model_low_mem(
                    __lowerCamelCase		,						__lowerCamelCase		,						[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     :       Optional[int]  						=  384
                # we can use the convnext one
                _UpperCamelCase     :       Tuple  						=  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'		,						size=__lowerCamelCase				)
                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__":
   UpperCAmelCase     :   Tuple					=				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.''',
   )
  
   UpperCAmelCase     :   str					=				parser.parse_args()
  
   UpperCAmelCase     :   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)
  
  
  
  
style_context: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
  
         def remove_articles(lowercase_:	Dict				):
                return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)
  
         def white_space_fix(lowercase_:	Optional[Any]				):
                return " ".join(text.split()				)
  
         def remove_punc(lowercase_:	List[str]				):
                _UpperCamelCase     :       str  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[str]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
         _UpperCamelCase     :       Any  						=  []
         for ground_truth in ground_truths:
                _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
                scores_for_ground_truths.append(lowercase_				)
         return max(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
         _UpperCamelCase     :       List[Any]  						=  0
         for article in dataset:
                for paragraph in article["paragraphs"]:
                       for qa in paragraph["qas"]:
                              total += 1
                              if qa["id"] not in predictions:
                                     _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                     print(lowercase_		,						file=sys.stderr				)
                                     continue
                              _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                              _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                              exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                              fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
         _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total
  
         return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.1'''
   UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   UpperCAmelCase					=				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
    UpperCAmelCase					=				json.load(dataset_file)
    if dataset_json["version"] != expected_version:
     print(
         '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
         file=sys.stderr,
     )
    UpperCAmelCase					=				dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
    UpperCAmelCase					=				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  