

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()
SCREAMING_SNAKE_CASE_        =      logging.get_logger()

@dataclass
class 				a  :
      _lowercase    						=			4_2
      _lowercase    						=			field(default_factory=UpperCAmelCase				)
      _lowercase    						=			field(default_factory=UpperCAmelCase				)


      def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







          '''simple docstring'''


          _UpperCAmelCase       :		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_	):







          '''simple docstring'''


          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        _UpperCAmelCase				(       self	):







          '''simple docstring'''


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







@dataclass
class 				a  :
      _lowercase    						=			4_2
      _lowercase    						=			4_2
      _lowercase    						=			1
      _lowercase    						=			field(default_factory=UpperCAmelCase				)
      _lowercase    						=			field(default_factory=UpperCAmelCase				)
      _lowercase    						=			True


      def __call__(       self						,					A_	):







          '''simple docstring'''


          _UpperCAmelCase       :		Optional[int] 		=    Tracker(self.dest	)(A_	).parametrized
          _UpperCAmelCase       :		List[Any] 		=    Tracker(self.src	)(A_	).parametrized

          _UpperCAmelCase       :		Optional[int] 		=    list(filter(lambda A_	: type(A_	) not in self.src_skip						,					A_	)	)
          _UpperCAmelCase       :		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 				a  (	nn.Module				):


      def __init__(       self						,					A_	):







          '''simple docstring'''


          super().__init__()

          _UpperCAmelCase       :		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}'
              _UpperCAmelCase       :		Optional[Any] 		=    len(A_	) + 1
              feature_blocks.append((f'res{block_index}', v)	)

          _UpperCAmelCase       :		Dict 		=    nn.ModuleDict(A_	)





      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


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







class 				a  (	UpperCAmelCase				):


      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


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





      def __getitem__(       self						,					A_	):







          '''simple docstring'''


          if x not in self:
              _UpperCAmelCase       :		Dict 		=    self.convert_name_to_timm(A_	)
              _UpperCAmelCase       :		Dict 		=    partial(lambda: (timm.create_model(A_						,					pretrained=A_	).eval(), None)	)

          else:
              _UpperCAmelCase       :		Union[str, Any] 		=    super().__getitem__(A_	)

          return val







class 				a  (	UpperCAmelCase				):


      def __getitem__(       self						,					A_	):







          '''simple docstring'''


          if "seer" in x and "in1k" not in x:
              _UpperCAmelCase       :		Tuple 		=    RegNetModel
          else:
              _UpperCAmelCase       :		int 		=    RegNetForImageClassification
          return val







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[Tuple[str, str]] )   ->       str:
    for from_key, to_key in keys:
        _UpperCAmelCase       :		Union[str, Any] 		=    from_state_dict[from_key].clone()
        print(F'Copied key={from_key} to={to_key}' )
    return to_state_dict

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Callable[[], nn.Module]  ,      lowerCAmelCase:  Callable[[], nn.Module]  ,      lowerCAmelCase:  RegNetConfig  ,      lowerCAmelCase:  Path  ,      lowerCAmelCase:  bool = True  ,      )   ->       Union[str, Any]:
    print(F'Converting {name}...' )
    with torch.no_grad():
        _UpperCAmelCase       :		str 		=    from_model_func()
        _UpperCAmelCase       :		Union[str, Any] 		=    our_model_func(lowerCAmelCase ).eval()
        _UpperCAmelCase       :		Optional[int] 		=    ModuleTransfer(src=lowerCAmelCase  ,      dest=lowerCAmelCase  ,      raise_if_mismatch=lowerCAmelCase )
        _UpperCAmelCase       :		List[Any] 		=    torch.randn((1, 3, 224, 224) )
        module_transfer(lowerCAmelCase )

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

    _UpperCAmelCase       :		Optional[Any] 		=    our_model(lowerCAmelCase  ,      output_hidden_states=lowerCAmelCase )
    _UpperCAmelCase       :		str 		=    (
        our_outputs.logits if isinstance(lowerCAmelCase  ,      lowerCAmelCase ) else our_outputs.last_hidden_state
    )

    _UpperCAmelCase       :		Tuple 		=    from_model(lowerCAmelCase )
    _UpperCAmelCase       :		int 		=    from_output[-1] if type(lowerCAmelCase ) 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:
        _UpperCAmelCase       :		Any 		=    our_outputs.hidden_states[-1]

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

        _UpperCAmelCase       :		str 		=    224 if "seer" not in name else 384
        # we can use the convnext one
        _UpperCAmelCase       :		Any 		=    AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,      size=lowerCAmelCase )
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / name  ,      commit_message="Add image processor"  ,      use_temp_dir=lowerCAmelCase  ,      )

        print(F'Pushed {name}' )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Path  ,      lowerCAmelCase:  str = None  ,      lowerCAmelCase:  bool = True )   ->       Tuple:
    _UpperCAmelCase       :		Union[str, Any] 		=    "imagenet-1k-id2label.json"
    _UpperCAmelCase       :		Any 		=    1000
    _UpperCAmelCase       :		Dict 		=    (1, num_labels)

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

    _UpperCAmelCase       :		Optional[Any] 		=    idalabel
    _UpperCAmelCase       :		Any 		=    {v: k for k, v in idalabel.items()}

    _UpperCAmelCase       :		Union[str, Any] 		=    partial(lowerCAmelCase  ,      num_labels=lowerCAmelCase  ,      idalabel=lowerCAmelCase  ,      labelaid=lowerCAmelCase )

    _UpperCAmelCase       :		str 		=    {
        "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 ),
    }

    _UpperCAmelCase       :		Any 		=    NameToOurModelFuncMap()
    _UpperCAmelCase       :		Optional[int] 		=    NameToFromModelFuncMap()
    # add seer weights logic

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

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

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

    _UpperCAmelCase       :		List[str] 		=    partial(
        lowerCAmelCase  ,      "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() )  ,      )

    _UpperCAmelCase       :		Union[str, Any] 		=    partial(
        lowerCAmelCase  ,      "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
    _UpperCAmelCase       :		Tuple 		=    partial(
        lowerCAmelCase  ,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"  ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,      )

    _UpperCAmelCase       :		Union[str, Any] 		=    partial(
        lowerCAmelCase  ,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"  ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,      )

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

    _UpperCAmelCase       :		List[Any] 		=    partial(
        lowerCAmelCase  ,      "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(
            lowerCAmelCase  ,      names_to_from_model_map[model_name]  ,      names_to_ours_model_map[model_name]  ,      names_to_config[model_name]  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(
                lowerCAmelCase  ,      names_to_from_model_map[model_name]  ,      names_to_ours_model_map[model_name]  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      )
    return config, expected_shape


if __name__ == "__main__":
     SCREAMING_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.',
     )

     SCREAMING_SNAKE_CASE_        =      parser.parse_args()

     SCREAMING_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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


