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)




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)))



