



"""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()
lowerCamelCase				:     Optional[Any]								=  logging.get_logger()




@dataclass
class    __snake_case:
    _A       = 42
    _A       = field(default_factory=__A       )
    _A       = field(default_factory=__A       )
    _A       = field(default_factory=__A       )




    def   A   (   self    ,							A_    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



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




    def __call__(   self    ,							A_			):





      '''simple docstring'''



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




    @property
    def   A   (   self			):





      '''simple docstring'''



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




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_			):





      '''simple docstring'''



      super().__init__()

      _SCREAMING_SNAKE_CASE        =			[]
      # - 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}'''
        _SCREAMING_SNAKE_CASE        =			len(A_			) + 1
        feature_blocks.append((F'''res{block_index}''', v)			)

      _SCREAMING_SNAKE_CASE        =			nn.ModuleDict(A_			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



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




class    __snake_case(					__A       ):




    def   A   (   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 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			RegNetConfig(depths=[2, 7, 17, 1]       ,							hidden_sizes=[8, 8, 8, 8]       ,							groups_width=8    )
  if "in1k" in model_name:
    _SCREAMING_SNAKE_CASE        =			RegNetForImageClassification(UpperCamelCase__    )
  else:
    _SCREAMING_SNAKE_CASE        =			RegNetModel(UpperCamelCase__    )
  # create from model (with small weights)
  _SCREAMING_SNAKE_CASE        =			FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27       ,							group_width=1_010       ,							w_a=1_744       ,							w_a=6_20.83       ,							w_m=2.52    )    )    )

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

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

    pprint(dest_tracker.nameamodule    )
    _SCREAMING_SNAKE_CASE        =			Tracker(UpperCamelCase__    )
    _SCREAMING_SNAKE_CASE        =			src_tracker(UpperCamelCase__    ).parametrized

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

  _SCREAMING_SNAKE_CASE        =			{}

  _SCREAMING_SNAKE_CASE        =			to_params_dict(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			to_params_dict(UpperCamelCase__    )

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

  return from_to_ours_keys


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = True    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			'''imagenet-1k-id2label.json'''
  _SCREAMING_SNAKE_CASE        =			1_000

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

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

  _SCREAMING_SNAKE_CASE        =			partial(UpperCamelCase__       ,							num_labels=UpperCamelCase__       ,							idalabel=UpperCamelCase__       ,							labelaid=UpperCamelCase__    )

  _SCREAMING_SNAKE_CASE        =			{
      '''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(UpperCamelCase__    ) -> Tuple[Dict, Dict]:
    _SCREAMING_SNAKE_CASE        =			torch.hub.load_state_dict_from_url(UpperCamelCase__       ,							model_dir=str(UpperCamelCase__    )       ,							map_location='''cpu'''    )
    # check if we have a head, if yes add it
    _SCREAMING_SNAKE_CASE        =			files['''classy_state_dict''']['''base_model''']['''model''']
    return model_state_dict["trunk"], model_state_dict["heads"]

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

  _SCREAMING_SNAKE_CASE        =			get_from_to_our_keys(UpperCamelCase__    )

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

    _SCREAMING_SNAKE_CASE        =			{}

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

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

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

    lowerCamelCase				:     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)





"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCamelCase__    )

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
