




'''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()
lowercase      :   List[str]      =       logging.get_logger()







@dataclass
class 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     nn.Module
     lowerCAmelCase_  :     List[nn.Module]          =					field(default_factory=a__	)
     lowerCAmelCase_  :     list          =					field(default_factory=a__	)







     def 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Dict:
          snake_case			:      Union[str, 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							)      ->				Optional[int]:
          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 			snake_case_      (				self							)      ->				str:
          # 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 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     nn.Module
     lowerCAmelCase_  :     nn.Module
     lowerCAmelCase_  :     int          =					1
     lowerCAmelCase_  :     List          =					field(default_factory=a__	)
     lowerCAmelCase_  :     List          =					field(default_factory=a__	)
     lowerCAmelCase_  :     bool          =					True







     def __call__(				self							,__a							)      ->				Any:
          snake_case			:      Tuple        =			Tracker(self.dest							)(__a							).parametrized
          snake_case			:      Union[str, Any]        =			Tracker(self.src							)(__a							).parametrized

          snake_case			:      Tuple        =			list(filter(lambda __a							: type(__a							) not in self.src_skip							,__a							)							)
          snake_case			:      Any        =			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 					_a (nn.Module	):





     '''simple docstring'''



     def __init__(				self							,__a							)      ->				List[str]:
          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[Any]        =			nn.ModuleDict(__a							)







     def 			snake_case_      (				self							,__a							)      ->				List[Any]:
          return get_trunk_forward_outputs(
              __a							,out_feat_keys=__a							,feature_blocks=self._feature_blocks							,)







class 					_a (a__	):





     '''simple docstring'''



     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			x.split("""-"""							)
          return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)







     def __getitem__(				self							,__a							)      ->				Callable[[], Tuple[nn.Module, Dict]]:
          # default to timm!
          if x not in self:
               snake_case			:      Optional[int]        =			self.convert_name_to_timm(__a							)
               snake_case			:      Tuple        =			partial(lambda: (timm.create_model(__a							,pretrained=__a							).eval(), None)							)

          else:
               snake_case			:      Any        =			super().__getitem__(__a							)

          return val







class 					_a (a__	):





     '''simple docstring'''



     def __getitem__(				self							,__a							)      ->				Callable[[], nn.Module]:
          if "seer" in x and "in1k" not in x:
               snake_case			:      int        =			RegNetModel
          else:
               snake_case			:      str        =			RegNetForImageClassification
          return val



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



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

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

     snake_case			:      int        =			our_model(__lowercase     ,   output_hidden_states=__lowercase				)
     snake_case			:      Union[str, Any]        =			(
         our_outputs.logits if isinstance(__lowercase     ,   __lowercase				) else our_outputs.last_hidden_state
     )

     snake_case			:      List[str]        =			from_model(__lowercase				)
     snake_case			:      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			:      int        =			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			:      Any        =			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     ,   __lowercase = None     ,   __lowercase = True				):
     snake_case			:      str        =			"""imagenet-1k-id2label.json"""
     snake_case			:      Dict        =			1_000
     snake_case			:      Union[str, Any]        =			(1, num_labels)

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

     snake_case			:      Tuple        =			idalabel
     snake_case			:      Any        =			{v: k for k, v in idalabel.items()}

     snake_case			:      Dict        =			partial(__lowercase     ,   num_labels=__lowercase     ,   idalabel=__lowercase     ,   labelaid=__lowercase				)

     snake_case			:      Optional[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, 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				),
     }

     snake_case			:      List[Any]        =			NameToOurModelFuncMap()
     snake_case			:      List[Any]        =			NameToFromModelFuncMap()
     # add seer weights logic

     def load_using_classy_vision(__lowercase     ,   __lowercase				) -> Tuple[nn.Module, Dict]:
          snake_case			:      Union[str, Any]        =			torch.hub.load_state_dict_from_url(__lowercase     ,   model_dir=str(__lowercase				)     ,   map_location="""cpu"""				)
          snake_case			:      Dict        =			model_func()
          # check if we have a head, if yes add it
          snake_case			:      str        =			files["""classy_state_dict"""]["""base_model"""]["""model"""]
          snake_case			:      List[str]        =			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			:      Optional[int]        =			partial(
         __lowercase     ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,   )

     snake_case			:      Tuple        =			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			:      Dict        =			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=620.83     ,   w_m=2.52				)				)				)     ,   )

     # IN1K finetuned
     snake_case			:      Any        =			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			:      Dict        =			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			:      List[str]        =			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			:      Dict        =			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=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__":
      lowercase      :   Dict      =       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.""",
      )

      lowercase      :   List[Any]      =       parser.parse_args()

      lowercase      :   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'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :   Optional[int]      =       {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   int      =       [
          """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PegasusXForConditionalGeneration""",
          """PegasusXModel""",
          """PegasusXPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_pegasus_x import (
                PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                PegasusXForConditionalGeneration,
                PegasusXModel,
                PegasusXPreTrainedModel,
            )


else:
      import sys

      lowercase      :   Optional[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



