




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
_lowerCamelCase     :		Optional[Any]								=       logging.get_logger()


@dataclass
class     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				42
    UpperCAmelCase__							  =				field(default_factory=UpperCAmelCase__					)
    UpperCAmelCase__							  =				field(default_factory=UpperCAmelCase__					)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tensor ,			UpperCAmelCase__				:     Tensor)						->str:





           '''simple docstring'''

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


    def __call__(      self				:     int ,			UpperCAmelCase__				:     Tensor)						->List[Any]:





           '''simple docstring'''

           for m in self.module.modules():
                  self.handles.append(m.register_forward_hook(self._forward_hook))
           self.module(UpperCAmelCase__)
           [x.remove() for x in self.handles]
           return self





    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->List[Any]:





           '''simple docstring'''

           return list(filter(lambda UpperCAmelCase__: len(list(x.state_dict().keys())) > 0 ,			self.traced))


@dataclass
class     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				42
    UpperCAmelCase__							  =				42
    UpperCAmelCase__							  =				1
    UpperCAmelCase__							  =				field(default_factory=UpperCAmelCase__					)
    UpperCAmelCase__							  =				field(default_factory=UpperCAmelCase__					)
    UpperCAmelCase__							  =				True


    def __call__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Tensor)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   Tracker(self.dest)(UpperCAmelCase__).parametrized
           A__							=   Tracker(self.src)(UpperCAmelCase__).parametrized

           A__							=   list(filter(lambda UpperCAmelCase__: type(UpperCAmelCase__) not in self.src_skip ,			UpperCAmelCase__))
           A__							=   list(filter(lambda UpperCAmelCase__: type(UpperCAmelCase__) not in self.dest_skip ,			UpperCAmelCase__))

           if len(UpperCAmelCase__) != len(UpperCAmelCase__) and self.raise_if_mismatch:
                  raise Exception(
                      f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__)} operations while"""
                      f""" destination module has {len(UpperCAmelCase__)}.""")

           for dest_m, src_m in zip(UpperCAmelCase__ ,			UpperCAmelCase__):
                  dest_m.load_state_dict(src_m.state_dict())
                  if self.verbose == 1:
                         print(f"""Transfered from={src_m} to={dest_m}""")


class     UpperCamelCase_   (			nn.Module					):

    '''simple docstring'''






    def __init__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     nn.Module)						->Optional[int]:





           '''simple docstring'''

           super().__init__()

           A__							=   []
           # - 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__							=   len(UpperCAmelCase__) + 1
                  feature_blocks.append((f"""res{block_index}""", v))

           A__							=   nn.ModuleDict(UpperCAmelCase__)





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     Tensor)						->List[Any]:





           '''simple docstring'''

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


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   x.split('''-''')
           return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])





    def __getitem__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     str)						->Callable[[], Tuple[nn.Module, Dict]]:





           '''simple docstring'''

           if x not in self:
                  A__							=   self.convert_name_to_timm(UpperCAmelCase__)
                  A__							=   partial(lambda: (timm.create_model(UpperCAmelCase__ ,			pretrained=UpperCAmelCase__).eval(), None))

           else:
                  A__							=   super().__getitem__(UpperCAmelCase__)

           return val


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __getitem__(      self				:     Tuple ,			UpperCAmelCase__				:     str)						->Callable[[], nn.Module]:





           '''simple docstring'''

           if "seer" in x and "in1k" not in x:
                  A__							=   RegNetModel
           else:
                  A__							=   RegNetForImageClassification
           return val







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Union[str, Any]:



       """simple docstring"""

       for from_key, to_key in keys:
              A__							=   from_state_dict[from_key].clone()
              print(f"""Copied key={from_key} to={to_key}"""						)
       return to_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_ = True			,    )       ->	Union[str, Any]:



       """simple docstring"""

       print(f"""Converting {name}..."""						)
       with torch.no_grad():
              A__			,							A__							=   from_model_func()
              A__							=   our_model_func(lowercase_						).eval()
              A__							=   ModuleTransfer(src=lowercase_			,    dest=lowercase_			,    raise_if_mismatch=lowercase_						)
              A__							=   torch.randn((1, 3, 224, 224)						)
              module_transfer(lowercase_						)

       if from_state_dict is not None:
              A__							=   []
              # for seer - in1k finetuned we have to manually copy the head
              if "seer" in name and "in1k" in name:
                     A__							=   [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
              A__							=   manually_copy_vissl_head(lowercase_			,    our_model.state_dict()			,    lowercase_						)
              our_model.load_state_dict(lowercase_						)

       A__							=   our_model(lowercase_			,    output_hidden_states=lowercase_						)
       A__							=   (
           our_outputs.logits if isinstance(lowercase_			,    lowercase_						) else our_outputs.last_hidden_state
       )

       A__							=   from_model(lowercase_						)
       A__							=   from_output[-1] if type(lowercase_						) is list else from_output

       # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
       if "seer" in name and "in1k" in name:
              A__							=   our_outputs.hidden_states[-1]

       assert torch.allclose(lowercase_			,    lowercase_						), "The model logits don't match the original one."

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / name			,    commit_message='''Add model'''			,    use_temp_dir=lowercase_			,    )

              A__							=   224 if '''seer''' not in name else 384
              # we can use the convnext one
              A__							=   AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''			,    size=lowercase_						)
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / name			,    commit_message='''Add image processor'''			,    use_temp_dir=lowercase_			,    )

              print(f"""Pushed {name}"""						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = None			,    lowercase_ = True						)       ->	Any:



       """simple docstring"""

       A__							=   '''imagenet-1k-id2label.json'''
       A__							=   1_000
       A__							=   (1, num_labels)

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

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

       A__							=   partial(lowercase_			,    num_labels=lowercase_			,    idalabel=lowercase_			,    labelaid=lowercase_						)

       A__							=   {
           '''regnet-x-002''': ImageNetPreTrainedConfig(
               depths=[1, 1, 4, 7]			,    hidden_sizes=[24, 56, 152, 368]			,    groups_width=8			,    layer_type='''x'''						),
           '''regnet-x-004''': ImageNetPreTrainedConfig(
               depths=[1, 2, 7, 12]			,    hidden_sizes=[32, 64, 160, 384]			,    groups_width=16			,    layer_type='''x'''						),
           '''regnet-x-006''': ImageNetPreTrainedConfig(
               depths=[1, 3, 5, 7]			,    hidden_sizes=[48, 96, 240, 528]			,    groups_width=24			,    layer_type='''x'''						),
           '''regnet-x-008''': ImageNetPreTrainedConfig(
               depths=[1, 3, 7, 5]			,    hidden_sizes=[64, 128, 288, 672]			,    groups_width=16			,    layer_type='''x'''						),
           '''regnet-x-016''': ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 2]			,    hidden_sizes=[72, 168, 408, 912]			,    groups_width=24			,    layer_type='''x'''						),
           '''regnet-x-032''': ImageNetPreTrainedConfig(
               depths=[2, 6, 15, 2]			,    hidden_sizes=[96, 192, 432, 1_008]			,    groups_width=48			,    layer_type='''x'''						),
           '''regnet-x-040''': ImageNetPreTrainedConfig(
               depths=[2, 5, 14, 2]			,    hidden_sizes=[80, 240, 560, 1_360]			,    groups_width=40			,    layer_type='''x'''						),
           '''regnet-x-064''': ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 1]			,    hidden_sizes=[168, 392, 784, 1_624]			,    groups_width=56			,    layer_type='''x'''						),
           '''regnet-x-080''': ImageNetPreTrainedConfig(
               depths=[2, 5, 15, 1]			,    hidden_sizes=[80, 240, 720, 1_920]			,    groups_width=120			,    layer_type='''x'''						),
           '''regnet-x-120''': ImageNetPreTrainedConfig(
               depths=[2, 5, 11, 1]			,    hidden_sizes=[224, 448, 896, 2_240]			,    groups_width=112			,    layer_type='''x'''						),
           '''regnet-x-160''': ImageNetPreTrainedConfig(
               depths=[2, 6, 13, 1]			,    hidden_sizes=[256, 512, 896, 2_048]			,    groups_width=128			,    layer_type='''x'''						),
           '''regnet-x-320''': ImageNetPreTrainedConfig(
               depths=[2, 7, 13, 1]			,    hidden_sizes=[336, 672, 1_344, 2_520]			,    groups_width=168			,    layer_type='''x'''						),
           # y variant
           '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,    hidden_sizes=[24, 56, 152, 368]			,    groups_width=8						),
           '''regnet-y-004''': ImageNetPreTrainedConfig(
               depths=[1, 3, 6, 6]			,    hidden_sizes=[48, 104, 208, 440]			,    groups_width=8						),
           '''regnet-y-006''': ImageNetPreTrainedConfig(
               depths=[1, 3, 7, 4]			,    hidden_sizes=[48, 112, 256, 608]			,    groups_width=16						),
           '''regnet-y-008''': ImageNetPreTrainedConfig(
               depths=[1, 3, 8, 2]			,    hidden_sizes=[64, 128, 320, 768]			,    groups_width=16						),
           '''regnet-y-016''': ImageNetPreTrainedConfig(
               depths=[2, 6, 17, 2]			,    hidden_sizes=[48, 120, 336, 888]			,    groups_width=24						),
           '''regnet-y-032''': ImageNetPreTrainedConfig(
               depths=[2, 5, 13, 1]			,    hidden_sizes=[72, 216, 576, 1_512]			,    groups_width=24						),
           '''regnet-y-040''': ImageNetPreTrainedConfig(
               depths=[2, 6, 12, 2]			,    hidden_sizes=[128, 192, 512, 1_088]			,    groups_width=64						),
           '''regnet-y-064''': ImageNetPreTrainedConfig(
               depths=[2, 7, 14, 2]			,    hidden_sizes=[144, 288, 576, 1_296]			,    groups_width=72						),
           '''regnet-y-080''': ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 1]			,    hidden_sizes=[168, 448, 896, 2_016]			,    groups_width=56						),
           '''regnet-y-120''': ImageNetPreTrainedConfig(
               depths=[2, 5, 11, 1]			,    hidden_sizes=[224, 448, 896, 2_240]			,    groups_width=112						),
           '''regnet-y-160''': ImageNetPreTrainedConfig(
               depths=[2, 4, 11, 1]			,    hidden_sizes=[224, 448, 1_232, 3_024]			,    groups_width=112						),
           '''regnet-y-320''': ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]			,    hidden_sizes=[232, 696, 1_392, 3_712]			,    groups_width=232						),
           # models created by SEER -> https://arxiv.org/abs/2202.08360
           '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]			,    hidden_sizes=[232, 696, 1_392, 3_712]			,    groups_width=232						),
           '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]			,    hidden_sizes=[328, 984, 1_968, 4_920]			,    groups_width=328						),
           '''regnet-y-1280-seer''': RegNetConfig(
               depths=[2, 7, 17, 1]			,    hidden_sizes=[528, 1_056, 2_904, 7_392]			,    groups_width=264						),
           '''regnet-y-2560-seer''': RegNetConfig(
               depths=[3, 7, 16, 1]			,    hidden_sizes=[640, 1_696, 2_544, 5_088]			,    groups_width=640						),
           '''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-320-seer-in1k''': ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]			,    hidden_sizes=[232, 696, 1_392, 3_712]			,    groups_width=232						),
           '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]			,    hidden_sizes=[328, 984, 1_968, 4_920]			,    groups_width=328						),
           '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]			,    hidden_sizes=[528, 1_056, 2_904, 7_392]			,    groups_width=264						),
           '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
               depths=[3, 7, 16, 1]			,    hidden_sizes=[640, 1_696, 2_544, 5_088]			,    groups_width=640						),
           '''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						),
       }

       A__							=   NameToOurModelFuncMap()
       A__							=   NameToFromModelFuncMap()
       # add seer weights logic

       def load_using_classy_vision(lowercase_			,    lowercase_						) -> Tuple[nn.Module, Dict]:
              A__							=   torch.hub.load_state_dict_from_url(lowercase_			,    model_dir=str(lowercase_						)			,    map_location='''cpu'''						)
              A__							=   model_func()
              # check if we have a head, if yes add it
              A__							=   files['''classy_state_dict''']['''base_model''']['''model''']
              A__							=   model_state_dict['''trunk''']
              model.load_state_dict(lowercase_						)
              return model.eval(), model_state_dict["heads"]

       # pretrained
       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''			,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)			,    )

       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''			,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)			,    )

       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''			,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()						)			,    )

       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''			,    lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=27			,    group_width=1_010			,    w_a=1_744			,    w_a=6_20.83			,    w_m=2.52						)						)						)			,    )

       # IN1K finetuned
       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''			,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)			,    )

       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''			,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)			,    )

       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''			,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()						)			,    )

       A__							=   partial(
           lowercase_			,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''			,    lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=27			,    group_width=1_010			,    w_a=1_744			,    w_a=6_20.83			,    w_m=2.52						)						)						)			,    )

       if model_name:
              convert_weight_and_push(
                  lowercase_			,    names_to_from_model_map[model_name]			,    names_to_ours_model_map[model_name]			,    names_to_config[model_name]			,    lowercase_			,    lowercase_			,    )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         lowercase_			,    names_to_from_model_map[model_name]			,    names_to_ours_model_map[model_name]			,    lowercase_			,    lowercase_			,    lowercase_			,    )
       return config, expected_shape


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     :		List[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)








_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609_344,
    "knot": 1.852,
}

_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 0.277_777_778,
    "mph": 0.621_371_192,
    "knot": 0.539_956_803,
}







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	float:



       """simple docstring"""

       if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
              A__							=   (
                  f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
                  f"""Valid values are: {", ".join(lowercase_						)}"""
              )
              raise ValueError(lowercase_						)
       return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]			,    3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


