






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


@dataclass
class       __lowercase      :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42
     SCREAMING_SNAKE_CASE   						=				field(default_factory=lowercase_  )
     SCREAMING_SNAKE_CASE   						=				field(default_factory=lowercase_  )




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tensor ,	UpperCamelCase_       :			Tensor				):


         """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       :			Any ,	UpperCamelCase_       :			Tensor				):


         """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       :			List[Any]				):


         """simple docstring"""



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


@dataclass
class       __lowercase      :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42
     SCREAMING_SNAKE_CASE   						=				42
     SCREAMING_SNAKE_CASE   						=				1
     SCREAMING_SNAKE_CASE   						=				field(default_factory=lowercase_  )
     SCREAMING_SNAKE_CASE   						=				field(default_factory=lowercase_  )
     SCREAMING_SNAKE_CASE   						=				True




     def __call__(				self       :			Dict ,	UpperCamelCase_       :			Tensor				):


         """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       __lowercase      (	nn.Module  ):







     '''simple docstring'''




     def __init__(				self       :			str ,	UpperCamelCase_       :			nn.Module				):


         """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 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Tensor				):


         """simple docstring"""



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


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			str				):


         """simple docstring"""



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





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


         """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       __lowercase      (	lowercase_  ):







     '''simple docstring'''




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


         """simple docstring"""



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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			List[Tuple[str, str]]    )						->      List[str]:





    """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   :			str			,				__lowercase   :			Callable[[], nn.Module]			,				__lowercase   :			Callable[[], nn.Module]			,				__lowercase   :			RegNetConfig			,				__lowercase   :			Path			,				__lowercase   :			bool = True			,				)						->      Optional[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, 2_2_4, 2_2_4)    )
        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         =	2_2_4 if """seer""" not in name else 3_8_4
        # 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   :			Path			,				__lowercase   :			str = None			,				__lowercase   :			bool = True    )						->      Optional[int]:





    """simple docstring"""

    __A         =	"""imagenet-1k-id2label.json"""
    __A         =	1_0_0_0
    __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=[2_4, 5_6, 1_5_2, 3_6_8]			,				groups_width=8			,				layer_type="""x"""    ),
        """regnet-x-004""": ImageNetPreTrainedConfig(
            depths=[1, 2, 7, 1_2]			,				hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]			,				groups_width=1_6			,				layer_type="""x"""    ),
        """regnet-x-006""": ImageNetPreTrainedConfig(
            depths=[1, 3, 5, 7]			,				hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]			,				groups_width=2_4			,				layer_type="""x"""    ),
        """regnet-x-008""": ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 5]			,				hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]			,				groups_width=1_6			,				layer_type="""x"""    ),
        """regnet-x-016""": ImageNetPreTrainedConfig(
            depths=[2, 4, 1_0, 2]			,				hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]			,				groups_width=2_4			,				layer_type="""x"""    ),
        """regnet-x-032""": ImageNetPreTrainedConfig(
            depths=[2, 6, 1_5, 2]			,				hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]			,				groups_width=4_8			,				layer_type="""x"""    ),
        """regnet-x-040""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_4, 2]			,				hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]			,				groups_width=4_0			,				layer_type="""x"""    ),
        """regnet-x-064""": ImageNetPreTrainedConfig(
            depths=[2, 4, 1_0, 1]			,				hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]			,				groups_width=5_6			,				layer_type="""x"""    ),
        """regnet-x-080""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_5, 1]			,				hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]			,				groups_width=1_2_0			,				layer_type="""x"""    ),
        """regnet-x-120""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_1, 1]			,				hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]			,				groups_width=1_1_2			,				layer_type="""x"""    ),
        """regnet-x-160""": ImageNetPreTrainedConfig(
            depths=[2, 6, 1_3, 1]			,				hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]			,				groups_width=1_2_8			,				layer_type="""x"""    ),
        """regnet-x-320""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_3, 1]			,				hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]			,				groups_width=1_6_8			,				layer_type="""x"""    ),
        # y variant
        """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,				hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]			,				groups_width=8    ),
        """regnet-y-004""": ImageNetPreTrainedConfig(
            depths=[1, 3, 6, 6]			,				hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]			,				groups_width=8    ),
        """regnet-y-006""": ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 4]			,				hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]			,				groups_width=1_6    ),
        """regnet-y-008""": ImageNetPreTrainedConfig(
            depths=[1, 3, 8, 2]			,				hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]			,				groups_width=1_6    ),
        """regnet-y-016""": ImageNetPreTrainedConfig(
            depths=[2, 6, 1_7, 2]			,				hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]			,				groups_width=2_4    ),
        """regnet-y-032""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_3, 1]			,				hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]			,				groups_width=2_4    ),
        """regnet-y-040""": ImageNetPreTrainedConfig(
            depths=[2, 6, 1_2, 2]			,				hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]			,				groups_width=6_4    ),
        """regnet-y-064""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_4, 2]			,				hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]			,				groups_width=7_2    ),
        """regnet-y-080""": ImageNetPreTrainedConfig(
            depths=[2, 4, 1_0, 1]			,				hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]			,				groups_width=5_6    ),
        """regnet-y-120""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_1, 1]			,				hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]			,				groups_width=1_1_2    ),
        """regnet-y-160""": ImageNetPreTrainedConfig(
            depths=[2, 4, 1_1, 1]			,				hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]			,				groups_width=1_1_2    ),
        """regnet-y-320""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_2, 1]			,				hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]			,				groups_width=2_3_2    ),
        # models created by SEER -> https://arxiv.org/abs/2202.08360
        """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 1_2, 1]			,				hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]			,				groups_width=2_3_2    ),
        """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 1_2, 1]			,				hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]			,				groups_width=3_2_8    ),
        """regnet-y-1280-seer""": RegNetConfig(
            depths=[2, 7, 1_7, 1]			,				hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]			,				groups_width=2_6_4    ),
        """regnet-y-2560-seer""": RegNetConfig(
            depths=[3, 7, 1_6, 1]			,				hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]			,				groups_width=6_4_0    ),
        """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]			,				hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]			,				groups_width=1_0_1_0    ),
        # finetuned on imagenet
        """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_2, 1]			,				hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]			,				groups_width=2_3_2    ),
        """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 5, 1_2, 1]			,				hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]			,				groups_width=3_2_8    ),
        """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]			,				hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]			,				groups_width=2_6_4    ),
        """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[3, 7, 1_6, 1]			,				hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]			,				groups_width=6_4_0    ),
        """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]			,				hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]			,				groups_width=1_0_1_0    ),
    }

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

    def load_using_classy_vision(__lowercase   :			str			,				__lowercase   :			Callable[[], nn.Module]    ) -> 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=2_7			,				group_width=1_0_1_0			,				w_a=1_7_4_4			,				w_a=620.83			,				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=2_7			,				group_width=1_0_1_0			,				w_a=1_7_4_4			,				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__":
  __a      :       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.",
  )

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

  __a      :       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)












from typing import TYPE_CHECKING

from ...utils import _LazyModule


__a      :       Optional[int]				  =   {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
  from .tokenization_bertweet import BertweetTokenizer

else:
  import sys

  __a      :       int				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




