"""simple docstring"""



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()
__snake_case             =   logging.get_logger()







@dataclass
class _lowerCAmelCase    :
   __UpperCAmelCase			:	nn.Module
   __UpperCAmelCase			:	List[nn.Module]							  =  field(default_factory=snake_case_     )
   __UpperCAmelCase			:	list							  =  field(default_factory=snake_case_     )







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				str				 =   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						,     UpperCamelCase__						)     ->		Tuple:

       '''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 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




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







@dataclass
class _lowerCAmelCase    :
   __UpperCAmelCase			:	nn.Module
   __UpperCAmelCase			:	nn.Module
   __UpperCAmelCase			:	int							  =  1
   __UpperCAmelCase			:	List							  =  field(default_factory=snake_case_     )
   __UpperCAmelCase			:	List							  =  field(default_factory=snake_case_     )
   __UpperCAmelCase			:	bool							  =  True







   def __call__(     self						,     UpperCamelCase__						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   Tracker(self.dest						)(UpperCamelCase__						).parametrized
       snake_case   :				Any				 =   Tracker(self.src						)(UpperCamelCase__						).parametrized

       snake_case   :				List[str]				 =   list(filter(lambda UpperCamelCase__						: type(UpperCamelCase__						) not in self.src_skip						,     UpperCamelCase__						)						)
       snake_case   :				int				 =   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 _lowerCAmelCase    (				nn.Module     ):







   def __init__(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       super().__init__()

       snake_case   :				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}'
           snake_case   :				Tuple				 =   len(UpperCamelCase__						) + 1
           feature_blocks.append((F'res{block_index}', v)						)

       snake_case   :				List[Any]				 =   nn.ModuleDict(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




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







class _lowerCAmelCase    (				snake_case_     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




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







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

       '''simple docstring'''




       if x not in self:
           snake_case   :				List[str]				 =   self.convert_name_to_timm(UpperCamelCase__						)
           snake_case   :				List[Any]				 =   partial(lambda: (timm.create_model(UpperCamelCase__						,     pretrained=UpperCamelCase__						).eval(), None)						)

       else:
           snake_case   :				Union[str, Any]				 =   super().__getitem__(UpperCamelCase__						)

       return val







class _lowerCAmelCase    (				snake_case_     ):







   def __getitem__(     self						,     UpperCamelCase__						)     ->		Callable[[], nn.Module]:

       '''simple docstring'''




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







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[Tuple[str, str]]			)     ->						List[Any]:


    """simple docstring"""





    for from_key, to_key in keys:
        snake_case   :				str				 =   from_state_dict[from_key].clone()
        print(F'Copied key={from_key} to={to_key}'			)
    return to_state_dict







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Callable[[], nn.Module]						,							lowercase  :  Callable[[], nn.Module]						,							lowercase  :  RegNetConfig						,							lowercase  :  Path						,							lowercase  :  bool = True						,							)     ->						Any:


    """simple docstring"""





    print(F'Converting {name}...'			)
    with torch.no_grad():
        snake_case   :				Optional[int]				 =   from_model_func()
        snake_case   :				Tuple				 =   our_model_func(lowercase			).eval()
        snake_case   :				Dict				 =   ModuleTransfer(src=lowercase						,							dest=lowercase						,							raise_if_mismatch=lowercase			)
        snake_case   :				List[str]				 =   torch.randn((1, 3, 224, 224)			)
        module_transfer(lowercase			)

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

    snake_case   :				List[Any]				 =   our_model(lowercase						,							output_hidden_states=lowercase			)
    snake_case   :				Any				 =   (
        our_outputs.logits if isinstance(lowercase						,							lowercase			) else our_outputs.last_hidden_state
    )

    snake_case   :				int				 =   from_model(lowercase			)
    snake_case   :				Union[str, Any]				 =   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:
        snake_case   :				Optional[Any]				 =   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						,							)

        snake_case   :				List[str]				 =   224 if "seer" not in name else 384
        # we can use the convnext one
        snake_case   :				List[str]				 =   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   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  str = None						,							lowercase  :  bool = True			)     ->						str:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   "imagenet-1k-id2label.json"
    snake_case   :				Optional[Any]				 =   1000
    snake_case   :				str				 =   (1, num_labels)

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

    snake_case   :				str				 =   idalabel
    snake_case   :				Dict				 =   {v: k for k, v in idalabel.items()}

    snake_case   :				int				 =   partial(lowercase						,							num_labels=lowercase						,							idalabel=lowercase						,							labelaid=lowercase			)

    snake_case   :				Any				 =   {
        "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, 1008]						,							groups_width=48						,							layer_type="x"			),
        "regnet-x-040": ImageNetPreTrainedConfig(
            depths=[2, 5, 14, 2]						,							hidden_sizes=[80, 240, 560, 1360]						,							groups_width=40						,							layer_type="x"			),
        "regnet-x-064": ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]						,							hidden_sizes=[168, 392, 784, 1624]						,							groups_width=56						,							layer_type="x"			),
        "regnet-x-080": ImageNetPreTrainedConfig(
            depths=[2, 5, 15, 1]						,							hidden_sizes=[80, 240, 720, 1920]						,							groups_width=120						,							layer_type="x"			),
        "regnet-x-120": ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]						,							hidden_sizes=[224, 448, 896, 2240]						,							groups_width=112						,							layer_type="x"			),
        "regnet-x-160": ImageNetPreTrainedConfig(
            depths=[2, 6, 13, 1]						,							hidden_sizes=[256, 512, 896, 2048]						,							groups_width=128						,							layer_type="x"			),
        "regnet-x-320": ImageNetPreTrainedConfig(
            depths=[2, 7, 13, 1]						,							hidden_sizes=[336, 672, 1344, 2520]						,							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, 1512]						,							groups_width=24			),
        "regnet-y-040": ImageNetPreTrainedConfig(
            depths=[2, 6, 12, 2]						,							hidden_sizes=[128, 192, 512, 1088]						,							groups_width=64			),
        "regnet-y-064": ImageNetPreTrainedConfig(
            depths=[2, 7, 14, 2]						,							hidden_sizes=[144, 288, 576, 1296]						,							groups_width=72			),
        "regnet-y-080": ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]						,							hidden_sizes=[168, 448, 896, 2016]						,							groups_width=56			),
        "regnet-y-120": ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]						,							hidden_sizes=[224, 448, 896, 2240]						,							groups_width=112			),
        "regnet-y-160": ImageNetPreTrainedConfig(
            depths=[2, 4, 11, 1]						,							hidden_sizes=[224, 448, 1232, 3024]						,							groups_width=112			),
        "regnet-y-320": ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]						,							hidden_sizes=[232, 696, 1392, 3712]						,							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, 1392, 3712]						,							groups_width=232			),
        "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]						,							hidden_sizes=[328, 984, 1968, 4920]						,							groups_width=328			),
        "regnet-y-1280-seer": RegNetConfig(
            depths=[2, 7, 17, 1]						,							hidden_sizes=[528, 1056, 2904, 7392]						,							groups_width=264			),
        "regnet-y-2560-seer": RegNetConfig(
            depths=[3, 7, 16, 1]						,							hidden_sizes=[640, 1696, 2544, 5088]						,							groups_width=640			),
        "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-320-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]						,							hidden_sizes=[232, 696, 1392, 3712]						,							groups_width=232			),
        "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]						,							hidden_sizes=[328, 984, 1968, 4920]						,							groups_width=328			),
        "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]						,							hidden_sizes=[528, 1056, 2904, 7392]						,							groups_width=264			),
        "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
            depths=[3, 7, 16, 1]						,							hidden_sizes=[640, 1696, 2544, 5088]						,							groups_width=640			),
        "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]						,							hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,							groups_width=1010			),
    }

    snake_case   :				Optional[int]				 =   NameToOurModelFuncMap()
    snake_case   :				Tuple				 =   NameToFromModelFuncMap()
    # add seer weights logic

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

    # pretrained
    snake_case   :				Optional[Any]				 =   partial(
        lowercase						,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"						,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)						,							)

    snake_case   :				Tuple				 =   partial(
        lowercase						,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"						,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)						,							)

    snake_case   :				Optional[Any]				 =   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()			)						,							)

    snake_case   :				str				 =   partial(
        lowercase						,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"						,							lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27						,							group_width=1010						,							w_a=1744						,							w_a=620.83						,							w_m=2.52			)			)			)						,							)

    # IN1K finetuned
    snake_case   :				Optional[int]				 =   partial(
        lowercase						,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"						,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)						,							)

    snake_case   :				Tuple				 =   partial(
        lowercase						,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"						,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)						,							)

    snake_case   :				Optional[Any]				 =   partial(
        lowercase						,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"						,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			)						,							)

    snake_case   :				Union[str, Any]				 =   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=1010						,							w_a=1744						,							w_a=620.83						,							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__":
  __snake_case             =   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.""",
  )

  __snake_case             =   parser.parse_args()

  __snake_case             =   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 __future__ import annotations

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  list[float]			)     ->						Any:


    """simple docstring"""





    return np.maximum(0						,							lowercase			)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]





