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			: Dict							   =		logging.get_logger()







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Tensor       ,	_lowercase			:				Tensor      ):



     """simple docstring"""






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




   def __call__(    self			:				Union[str, Any]       ,	_lowercase			:				Tensor      ):



     """simple docstring"""






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




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






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







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)
   lowerCAmelCase_														=							True




   def __call__(    self			:				Any       ,	_lowercase			:				Tensor      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      Tracker(self.dest      )(_lowercase      ).parametrized
     SCREAMING_SNAKE_CASE__										=      Tracker(self.src      )(_lowercase      ).parametrized

     SCREAMING_SNAKE_CASE__										=      list(filter(lambda _lowercase      : type(_lowercase      ) not in self.src_skip       ,	_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      list(filter(lambda _lowercase      : type(_lowercase      ) not in self.dest_skip       ,	_lowercase      )      )

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

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







class __snake_case				(						nn.Module		):




   def __init__(    self			:				Union[str, Any]       ,	_lowercase			:				nn.Module      ):



     """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(_lowercase      ) + 1
       feature_blocks.append((f"""res{block_index}""", v)      )

     SCREAMING_SNAKE_CASE__										=      nn.ModuleDict(_lowercase      )




   def 	__a						(    self			:				Any       ,	_lowercase			:				Tensor      ):



     """simple docstring"""






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







class __snake_case				(						lowerCamelCase_		):




   def 	__a						(    self			:				Dict       ,	_lowercase			:				str      ):



     """simple docstring"""






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




   def __getitem__(    self			:				str       ,	_lowercase			:				str      ):



     """simple docstring"""






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

     else:
       SCREAMING_SNAKE_CASE__										=      super().__getitem__(_lowercase      )

     return val







class __snake_case				(						lowerCamelCase_		):




   def __getitem__(    self			:				List[Any]       ,	_lowercase			:				str      ):



     """simple docstring"""






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





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       List[Tuple[str, str]]	)							->					Any:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       Callable[[], nn.Module] ,     __UpperCamelCase    :       Callable[[], nn.Module] ,     __UpperCamelCase    :       RegNetConfig ,     __UpperCamelCase    :       Path ,     __UpperCamelCase    :       bool = True ,     )							->					List[str]:







  """simple docstring"""





  print(f"""Converting {name}..."""	)
  with torch.no_grad():
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      from_model_func()
    SCREAMING_SNAKE_CASE__										=      our_model_func(__UpperCamelCase	).eval()
    SCREAMING_SNAKE_CASE__										=      ModuleTransfer(src=__UpperCamelCase ,     dest=__UpperCamelCase ,     raise_if_mismatch=__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      torch.randn((1, 3, 2_24, 2_24)	)
    module_transfer(__UpperCamelCase	)

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

  SCREAMING_SNAKE_CASE__										=      our_model(__UpperCamelCase ,     output_hidden_states=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      (
      our_outputs.logits if isinstance(__UpperCamelCase ,     __UpperCamelCase	) else our_outputs.last_hidden_state
  )

  SCREAMING_SNAKE_CASE__										=      from_model(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      from_output[-1] if type(__UpperCamelCase	) 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:
    SCREAMING_SNAKE_CASE__										=      our_outputs.hidden_states[-1]

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

    SCREAMING_SNAKE_CASE__										=      2_24 if """seer""" not in name else 3_84
    # 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 / name ,     commit_message="""Add image processor""" ,     use_temp_dir=__UpperCamelCase ,     )

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Path ,     __UpperCamelCase    :       str = None ,     __UpperCamelCase    :       bool = True	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      """imagenet-1k-id2label.json"""
  SCREAMING_SNAKE_CASE__										=      10_00
  SCREAMING_SNAKE_CASE__										=      (1, num_labels)

  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-x-002""": ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7] ,     hidden_sizes=[24, 56, 1_52, 3_68] ,     groups_width=8 ,     layer_type="""x"""	),
      """regnet-x-004""": ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12] ,     hidden_sizes=[32, 64, 1_60, 3_84] ,     groups_width=16 ,     layer_type="""x"""	),
      """regnet-x-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7] ,     hidden_sizes=[48, 96, 2_40, 5_28] ,     groups_width=24 ,     layer_type="""x"""	),
      """regnet-x-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5] ,     hidden_sizes=[64, 1_28, 2_88, 6_72] ,     groups_width=16 ,     layer_type="""x"""	),
      """regnet-x-016""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2] ,     hidden_sizes=[72, 1_68, 4_08, 9_12] ,     groups_width=24 ,     layer_type="""x"""	),
      """regnet-x-032""": ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2] ,     hidden_sizes=[96, 1_92, 4_32, 10_08] ,     groups_width=48 ,     layer_type="""x"""	),
      """regnet-x-040""": ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2] ,     hidden_sizes=[80, 2_40, 5_60, 13_60] ,     groups_width=40 ,     layer_type="""x"""	),
      """regnet-x-064""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1] ,     hidden_sizes=[1_68, 3_92, 7_84, 16_24] ,     groups_width=56 ,     layer_type="""x"""	),
      """regnet-x-080""": ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1] ,     hidden_sizes=[80, 2_40, 7_20, 19_20] ,     groups_width=1_20 ,     layer_type="""x"""	),
      """regnet-x-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1] ,     hidden_sizes=[2_24, 4_48, 8_96, 22_40] ,     groups_width=1_12 ,     layer_type="""x"""	),
      """regnet-x-160""": ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1] ,     hidden_sizes=[2_56, 5_12, 8_96, 20_48] ,     groups_width=1_28 ,     layer_type="""x"""	),
      """regnet-x-320""": ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1] ,     hidden_sizes=[3_36, 6_72, 13_44, 25_20] ,     groups_width=1_68 ,     layer_type="""x"""	),
      # y variant
      """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] ,     hidden_sizes=[24, 56, 1_52, 3_68] ,     groups_width=8	),
      """regnet-y-004""": ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6] ,     hidden_sizes=[48, 1_04, 2_08, 4_40] ,     groups_width=8	),
      """regnet-y-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4] ,     hidden_sizes=[48, 1_12, 2_56, 6_08] ,     groups_width=16	),
      """regnet-y-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2] ,     hidden_sizes=[64, 1_28, 3_20, 7_68] ,     groups_width=16	),
      """regnet-y-016""": ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2] ,     hidden_sizes=[48, 1_20, 3_36, 8_88] ,     groups_width=24	),
      """regnet-y-032""": ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1] ,     hidden_sizes=[72, 2_16, 5_76, 15_12] ,     groups_width=24	),
      """regnet-y-040""": ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2] ,     hidden_sizes=[1_28, 1_92, 5_12, 10_88] ,     groups_width=64	),
      """regnet-y-064""": ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2] ,     hidden_sizes=[1_44, 2_88, 5_76, 12_96] ,     groups_width=72	),
      """regnet-y-080""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1] ,     hidden_sizes=[1_68, 4_48, 8_96, 20_16] ,     groups_width=56	),
      """regnet-y-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1] ,     hidden_sizes=[2_24, 4_48, 8_96, 22_40] ,     groups_width=1_12	),
      """regnet-y-160""": ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1] ,     hidden_sizes=[2_24, 4_48, 12_32, 30_24] ,     groups_width=1_12	),
      """regnet-y-320""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1] ,     hidden_sizes=[2_32, 6_96, 13_92, 37_12] ,     groups_width=2_32	),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1] ,     hidden_sizes=[2_32, 6_96, 13_92, 37_12] ,     groups_width=2_32	),
      """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1] ,     hidden_sizes=[3_28, 9_84, 19_68, 49_20] ,     groups_width=3_28	),
      """regnet-y-1280-seer""": RegNetConfig(
          depths=[2, 7, 17, 1] ,     hidden_sizes=[5_28, 10_56, 29_04, 73_92] ,     groups_width=2_64	),
      """regnet-y-2560-seer""": RegNetConfig(
          depths=[3, 7, 16, 1] ,     hidden_sizes=[6_40, 16_96, 25_44, 50_88] ,     groups_width=6_40	),
      """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] ,     hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] ,     groups_width=10_10	),
      # finetuned on imagenet
      """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1] ,     hidden_sizes=[2_32, 6_96, 13_92, 37_12] ,     groups_width=2_32	),
      """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1] ,     hidden_sizes=[3_28, 9_84, 19_68, 49_20] ,     groups_width=3_28	),
      """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] ,     hidden_sizes=[5_28, 10_56, 29_04, 73_92] ,     groups_width=2_64	),
      """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1] ,     hidden_sizes=[6_40, 16_96, 25_44, 50_88] ,     groups_width=6_40	),
      """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] ,     hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] ,     groups_width=10_10	),
  }

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

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

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

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

  SCREAMING_SNAKE_CASE__										=      partial(
      __UpperCamelCase ,     """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()	) ,     )

  SCREAMING_SNAKE_CASE__										=      partial(
      __UpperCamelCase ,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" ,     lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27 ,     group_width=10_10 ,     w_a=17_44 ,     w_a=620.83 ,     w_m=2.52	)	)	) ,     )

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

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

  SCREAMING_SNAKE_CASE__										=      partial(
      __UpperCamelCase ,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,     lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	) ,     )

  SCREAMING_SNAKE_CASE__										=      partial(
      __UpperCamelCase ,     """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=10_10 ,     w_a=17_44 ,     w_a=620.83 ,     w_m=2.52	)	)	) ,     )

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


if __name__ == "__main__":
   __lowerCamelCase			: Union[str, Any]							   =		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[Any]							   =		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			: List[Any]							   =		6_5521



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      0
  for plain_chr in plain_text:
    SCREAMING_SNAKE_CASE__										=      (a + ord(__UpperCamelCase	)) % MOD_ADLER
    SCREAMING_SNAKE_CASE__										=      (b + a) % MOD_ADLER
  return (b << 16) | a
