

'''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()
UpperCAmelCase					 =	logging.get_logger()


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			field(default_factory=A )
       lowerCAmelCase_												=			field(default_factory=A )







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Any    ,   __lowercase      :			Tensor    ,   __lowercase      :			Tensor   ):







            """simple docstring"""







            __lowercase      		=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      :			str    ,   __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       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







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






@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			1
       lowerCAmelCase_												=			field(default_factory=A )
       lowerCAmelCase_												=			field(default_factory=A )
       lowerCAmelCase_												=			True







       def __call__(				self      :			Tuple    ,   __lowercase      :			Tensor   ):







            """simple docstring"""







            __lowercase      		=Tracker(self.dest   )(__lowercase   ).parametrized
            __lowercase      		=Tracker(self.src   )(__lowercase   ).parametrized

            __lowercase      		=list(filter(lambda __lowercase   : type(__lowercase   ) not in self.src_skip    ,   __lowercase   )   )
            __lowercase      		=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 	lowerCAmelCase  (   nn.Module ):







       def __init__(				self      :			int    ,   __lowercase      :			nn.Module   ):







            """simple docstring"""







            super().__init__()

            __lowercase      		=[]
            # - 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}'''
                 __lowercase      		=len(__lowercase   ) + 1
                 feature_blocks.append((f'''res{block_index}''', v)   )

            __lowercase      		=nn.ModuleDict(__lowercase   )







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Tensor   ):







            """simple docstring"""







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






class 	lowerCAmelCase  (   A ):







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







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







       def __getitem__(				self      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







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

            else:
                 __lowercase      		=super().__getitem__(__lowercase   )

            return val






class 	lowerCAmelCase  (   A ):







       def __getitem__(				self      :			Tuple    ,   __lowercase      :			str   ):







            """simple docstring"""







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






def 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	Dict,       lowercase__    :	List[Tuple[str, str]]			):






     '''simple docstring'''





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

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






     '''simple docstring'''





     print(F'''Converting {name}...'''			)
     with torch.no_grad():
          __lowercase		,  __lowercase      		=from_model_func()
          __lowercase      		=our_model_func(lowercase__			).eval()
          __lowercase      		=ModuleTransfer(src=lowercase__,       dest=lowercase__,       raise_if_mismatch=lowercase__			)
          __lowercase      		=torch.randn((1, 3, 2_24, 2_24)			)
          module_transfer(lowercase__			)

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

     __lowercase      		=our_model(lowercase__,       output_hidden_states=lowercase__			)
     __lowercase      		=(
         our_outputs.logits if isinstance(lowercase__,       lowercase__			) else our_outputs.last_hidden_state
     )

     __lowercase      		=from_model(lowercase__			)
     __lowercase      		=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:
          __lowercase      		=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__,       )

          __lowercase      		=2_24 if 'seer' not in name else 3_84
          # we can use the convnext one
          __lowercase      		=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 					__UpperCamelCase				(						lowercase__    :	Path,       lowercase__    :	str = None,       lowercase__    :	bool = True			):






     '''simple docstring'''





     __lowercase      		='imagenet-1k-id2label.json'
     __lowercase      		=10_00
     __lowercase      		=(1, num_labels)

     __lowercase      		='huggingface/label-files'
     __lowercase      		=num_labels
     __lowercase      		=json.load(open(cached_download(hf_hub_url(lowercase__,       lowercase__,       repo_type='dataset'			)			),       'r'			)			)
     __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}

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

     __lowercase      		=partial(lowercase__,       num_labels=lowercase__,       idalabel=lowercase__,       labelaid=lowercase__			)

     __lowercase      		={
         '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			),
     }

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

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

     # pretrained
     __lowercase      		=partial(
         lowercase__,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch',       lambda: FakeRegNetVisslWrapper(RegNetYaagf()			),       )

     __lowercase      		=partial(
         lowercase__,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch',       lambda: FakeRegNetVisslWrapper(RegNetYaagf()			),       )

     __lowercase      		=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()			),       )

     __lowercase      		=partial(
         lowercase__,       '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
     __lowercase      		=partial(
         lowercase__,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch',       lambda: FakeRegNetVisslWrapper(RegNetYaagf()			),       )

     __lowercase      		=partial(
         lowercase__,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch',       lambda: FakeRegNetVisslWrapper(RegNetYaagf()			),       )

     __lowercase      		=partial(
         lowercase__,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch',       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			),       )

     __lowercase      		=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=10_10,       w_a=17_44,       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__":
       UpperCAmelCase					 =	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.''',
       )

       UpperCAmelCase					 =	parser.parse_args()

       UpperCAmelCase					 =	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'''

class 	lowerCAmelCase  :







       def __init__(				self      :			List[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=name
            __lowercase      		=value
            __lowercase      		=weight







       def __repr__(				self      :			List[str]   ):







            """simple docstring"""







            return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.value







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return self.name







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return self.weight







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self.value / self.weight






def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	List[str],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=[]
     for i in range(len(lowercase__			)			):
          menu.append(Things(name[i],       value[i],       weight[i]			)			)
     return menu

def 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	List[Any],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=sorted(lowercase__,       key=lowercase__,       reverse=lowercase__			)
     __lowercase      		=[]
     __lowercase		,  __lowercase      		=0.0, 0.0
     for i in range(len(lowercase__			)			):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]			)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)

def 					__UpperCamelCase				(						):






     '''simple docstring'''







if __name__ == "__main__":
       import doctest

       doctest.testmod()


