






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	:		Union[str, Any]										=   logging.get_logger()







@dataclass
class  _lowercase   :
      _a							:						nn.Module
      _a							:						List[nn.Module]										=							field(default_factory=_A						)
      _a							:						list										=							field(default_factory=_A						)
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Any							       =len(list(m.modules()   )   ) == 1 or isinstance(a				,			nn.Convad   ) or isinstance(a				,			nn.BatchNormad   )
       if has_not_submodules:
        self.traced.append(a   )
      def __call__(     self				,			a   ):
       for m in self.module.modules():
        self.handles.append(m.register_forward_hook(self._forward_hook   )   )
       self.module(a   )
       [x.remove() for x in self.handles]
       return self



      @property
      def 						lowercase__					(     self   ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda a   : len(list(x.state_dict().keys()   )   ) > 0				,			self.traced   )   )







@dataclass
class  _lowercase   :
      _a							:						nn.Module
      _a							:						nn.Module
      _a							:						int										=							1
      _a							:						List										=							field(default_factory=_A						)
      _a							:						List										=							field(default_factory=_A						)
      _a							:						bool										=							True
      def __call__(     self				,			a   ):
       snake_case__  :    int							       =Tracker(self.dest   )(a   ).parametrized
       snake_case__  :    str							       =Tracker(self.src   )(a   ).parametrized

       snake_case__  :    Any							       =list(filter(lambda a   : type(a   ) not in self.src_skip				,			a   )   )
       snake_case__  :    Optional[int]							       =list(filter(lambda a   : type(a   ) not in self.dest_skip				,			a   )   )

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

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







class  _lowercase   (   nn.Module						):
      def __init__(     self				,			a   ):
       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__  :    List[str]							       =len(a   ) + 1
        feature_blocks.append((F"res{block_index}", v)   )

       snake_case__  :    Optional[int]							       =nn.ModuleDict(a   )



      def 						lowercase__					(     self				,			a   ):
       return get_trunk_forward_outputs(
           a				,			out_feat_keys=a				,			feature_blocks=self._feature_blocks				,			)







class  _lowercase   (   _A						):
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =x.split("""-"""   )
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]   )



      def __getitem__(     self				,			a   ):
       # default to timm!
       if x not in self:
        snake_case__  :    int							       =self.convert_name_to_timm(a   )
        snake_case__  :    Optional[int]							       =partial(lambda: (timm.create_model(a				,			pretrained=a   ).eval(), None)   )

       else:
        snake_case__  :    str							       =super().__getitem__(a   )

       return val







class  _lowercase   (   _A						):
      def __getitem__(     self				,			a   ):
       if "seer" in x and "in1k" not in x:
        snake_case__  :    List[Any]							       =RegNetModel
       else:
        snake_case__  :    Optional[Any]							       =RegNetForImageClassification
       return val







def 			A__							(      _a  :					str		,  _a  :					Tuple		,  _a  :					List[Tuple[str, str]]	):





 '''simple docstring'''







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







def 			A__							(      _a  :					str		,  _a  :					Callable[[], nn.Module]		,  _a  :					Callable[[], nn.Module]		,  _a  :					RegNetConfig		,  _a  :					Path		,  _a  :					bool = True		,  ):





 '''simple docstring'''







 print(f"Converting {name}..."	)
 with torch.no_grad():
  snake_case__       ,   snake_case__  :    Dict							       =from_model_func()
  snake_case__  :    List[Any]							       =our_model_func(_a	).eval()
  snake_case__  :    Optional[Any]							       =ModuleTransfer(src=_a		,  dest=_a		,  raise_if_mismatch=_a	)
  snake_case__  :    Union[str, Any]							       =torch.randn((1, 3, 224, 224)	)
  module_transfer(_a	)

 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__  :    List[str]							       =[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
  snake_case__  :    Tuple							       =manually_copy_vissl_head(_a		,  our_model.state_dict()		,  _a	)
  our_model.load_state_dict(_a	)

 snake_case__  :    Union[str, Any]							       =our_model(_a		,  output_hidden_states=_a	)
 snake_case__  :    Union[str, Any]							       =(
     our_outputs.logits if isinstance(_a		,  _a	) else our_outputs.last_hidden_state
 )

 snake_case__  :    Any							       =from_model(_a	)
 snake_case__  :    List[Any]							       =from_output[-1] if type(_a	) 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__  :    List[Any]							       =our_outputs.hidden_states[-1]

 assert torch.allclose(_a		,  _a	), "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=_a		,  )

  snake_case__  :    Optional[Any]							       =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=_a	)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / name		,  commit_message="""Add image processor"""		,  use_temp_dir=_a		,  )

  print(f"Pushed {name}"	)







def 			A__							(      _a  :					Path		,  _a  :					str = None		,  _a  :					bool = True	):





 '''simple docstring'''







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

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

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

 snake_case__  :    Optional[int]							       =partial(_a		,  num_labels=_a		,  idalabel=_a		,  labelaid=_a	)

 snake_case__  :    int							       ={
     """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, 11110, 28280]		,  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, 11110, 28280]		,  groups_width=1010	),
 }

 snake_case__  :    Union[str, Any]							       =NameToOurModelFuncMap()
 snake_case__  :    Optional[int]							       =NameToFromModelFuncMap()
 # add seer weights logic

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

 # pretrained
 snake_case__  :    Tuple							       =partial(
     _a		,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""		,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,  )

 snake_case__  :    Any							       =partial(
     _a		,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""		,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,  )

 snake_case__  :    List[Any]							       =partial(
     _a		,  """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__  :    Optional[Any]							       =partial(
     _a		,  """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=6_2_0.8_3		,  w_m=2.5_2	)	)	)		,  )

 # IN1K finetuned
 snake_case__  :    Any							       =partial(
     _a		,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""		,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,  )

 snake_case__  :    Dict							       =partial(
     _a		,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""		,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,  )

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

 snake_case__  :    Optional[int]							       =partial(
     _a		,  """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=6_2_0.8_3		,  w_m=2.5_2	)	)	)		,  )

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


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   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	:		Tuple										=   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)













class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Optional[int]							       =size
       snake_case__  :    List[Any]							       =[0] * size
       snake_case__  :    List[Any]							       =[0] * size
      @staticmethod
      def 						lowercase__					(     a   ):
       return index | (index + 1)
      @staticmethod
      def 						lowercase__					(     a   ):
       return (index & (index + 1)) - 1
      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Optional[Any]							       =value
       while index < self.size:
        snake_case__  :    str							       =self.get_prev(a   ) + 1
        if current_left_border == index:
         snake_case__  :    Dict							       =value
        else:
         snake_case__  :    str							       =max(a				,			a				,			a   )
        snake_case__  :    Dict							       =self.get_next(a   )



      def 						lowercase__					(     self				,			a				,			a   ):
       right -= 1  # Because of right is exclusive
       snake_case__  :    Union[str, Any]							       =0
       while left <= right:
        snake_case__  :    List[str]							       =self.get_prev(a   )
        if left <= current_left:
         snake_case__  :    Optional[Any]							       =max(a				,			self.tree[right]   )
         snake_case__  :    Optional[Any]							       =current_left
        else:
         snake_case__  :    Tuple							       =max(a				,			self.arr[right]   )
         right -= 1
       return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()





