


'''simple docstring'''


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          =     logging.get_logger()







@dataclass
class        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							field(default_factory=lowerCAmelCase   )
     lowerCamelCase__						=							field(default_factory=lowerCAmelCase   )
     lowerCamelCase__						=							field(default_factory=lowerCAmelCase   )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					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		)
               __A  :					Union[str, Any]						     =       m


     def __call__(      self ,    lowerCamelCase		):


          '''simple docstring'''





          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 lowerCAmelCase__     (      self		):


          '''simple docstring'''





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







class        __magic_name__  (					nn.Module   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase		):


          '''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  :					Union[str, Any]						     =       len(lowerCamelCase		) + 1
               feature_blocks.append((f"res{block_index}", v)		)

          __A  :					str						     =       nn.ModuleDict(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





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







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def lowerCAmelCase__     (      self		):


          '''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 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       RegNetConfig(depths=[2, 7, 17, 1]					,				hidden_sizes=[8, 8, 8, 8]					,				groups_width=8       )
     if "in1k" in model_name:
          __A  :					Optional[int]						     =       RegNetForImageClassification(SCREAMING_SNAKE_CASE       )
     else:
          __A  :					List[Any]						     =       RegNetModel(SCREAMING_SNAKE_CASE       )
     # create from model (with small weights)
     __A  :					Tuple						     =       FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27					,				group_width=1010					,				w_a=1744					,				w_a=620.83					,				w_m=2.52       )       )       )

     with torch.no_grad():
          __A  :					str						     =       from_model.eval()
          __A  :					Optional[int]						     =       our_model.eval()

          __A  :					str						     =       torch.randn((1, 3, 32, 32)       )
          # trace both
          __A  :					Any						     =       Tracker(SCREAMING_SNAKE_CASE       )
          __A  :					Optional[Any]						     =       dest_tracker(SCREAMING_SNAKE_CASE       ).parametrized

          pprint(dest_tracker.nameamodule       )
          __A  :					Optional[int]						     =       Tracker(SCREAMING_SNAKE_CASE       )
          __A  :					Any						     =       src_tracker(SCREAMING_SNAKE_CASE       ).parametrized

     # convert the keys -> module dict to keys -> params
     def to_params_dict(SCREAMING_SNAKE_CASE       ):
          __A  :					Union[str, Any]						     =       OrderedDict()
          for name, module in dict_with_modules.items():
               for param_name, param in module.state_dict().items():
                    __A  :					Optional[int]						     =       param
          return params_dict

     __A  :					Union[str, Any]						     =       {}

     __A  :					Tuple						     =       to_params_dict(SCREAMING_SNAKE_CASE       )
     __A  :					Union[str, Any]						     =       to_params_dict(SCREAMING_SNAKE_CASE       )

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()					,				dst_state_dict.items()       ):
          __A  :					str						     =       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  :					Union[str, Any]						     =       "classifier.1.weight"
          __A  :					str						     =       "classifier.1.bias"

     return from_to_ours_keys


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = None					,				SCREAMING_SNAKE_CASE = True       ):




     '''simple docstring'''





     __A  :					Any						     =       "imagenet-1k-id2label.json"
     __A  :					Union[str, Any]						     =       1000

     __A  :					List[Any]						     =       "huggingface/label-files"
     __A  :					str						     =       num_labels
     __A  :					Optional[int]						     =       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				repo_type="dataset"       )       )					,				"r"       )       )
     __A  :					int						     =       {int(SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}

     __A  :					Optional[int]						     =       idalabel
     __A  :					Dict						     =       {v: k for k, v in idalabel.items()}

     __A  :					Any						     =       partial(SCREAMING_SNAKE_CASE					,				num_labels=SCREAMING_SNAKE_CASE					,				idalabel=SCREAMING_SNAKE_CASE					,				labelaid=SCREAMING_SNAKE_CASE       )

     __A  :					str						     =       {
         "regnet-y-10b-seer": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,				hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,				groups_width=1010       ),
         # finetuned on imagenet
         "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,				hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,				groups_width=1010       ),
     }

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

     __A  :					List[str]						     =       {
         "regnet-y-10b-seer": partial(
             SCREAMING_SNAKE_CASE					,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"					,				),
         "regnet-y-10b-seer-in1k": partial(
             SCREAMING_SNAKE_CASE					,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"					,				),
     }

     __A  :					Optional[Any]						     =       get_from_to_our_keys(SCREAMING_SNAKE_CASE       )

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

          __A  :					Optional[int]						     =       {}

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

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

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

      _UpperCamelCase          =     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)





'''simple docstring'''


from sklearn.metrics import accuracy_score

import datasets


_UpperCamelCase          =     """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


_UpperCamelCase          =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


_UpperCamelCase          =     """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,		_KWARGS_DESCRIPTION   )
class        __magic_name__  (					datasets.Metric   ):

     """simple docstring"""






     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return datasets.MetricInfo(
              description=_DESCRIPTION ,    citation=_CITATION ,    inputs_description=_KWARGS_DESCRIPTION ,    features=datasets.Features(
                  {
                      "predictions": datasets.Sequence(datasets.Value("int32"		)		),
                      "references": datasets.Sequence(datasets.Value("int32"		)		),
                  }
                  if self.config_name == "multilabel"
                  else {
                      "predictions": datasets.Value("int32"		),
                      "references": datasets.Value("int32"		),
                  }		) ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"] ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=True ,    lowerCamelCase=None		):


          '''simple docstring'''





          return {
              "accuracy": float(
                  accuracy_score(lowerCamelCase ,    lowerCamelCase ,    normalize=lowerCamelCase ,    sample_weight=lowerCamelCase		)		)
          }

