






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__							=     logging.get_logger()




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				nn.Module
       lowerCamelCase							:				List[nn.Module] 				= field(default_factory=lowercase_)
       lowerCamelCase							:				list 				= field(default_factory=lowercase_)



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[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__    ) ->      int:






            '''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 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





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




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				nn.Module
       lowerCamelCase							:				nn.Module
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				List 				= field(default_factory=lowercase_)
       lowerCamelCase							:				List 				= field(default_factory=lowercase_)
       lowerCamelCase							:				bool 				= True



       def __call__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						Tracker(self.dest    )(a__    ).parametrized
            __snake_case			:int          =						Tracker(self.src    )(a__    ).parametrized

            __snake_case			:List[Any]          =						list(filter(lambda a__    : type(a__    ) not in self.src_skip			,						a__    )    )
            __snake_case			:Optional[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 			snake_case__   (							nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Optional[Any]:






            '''simple docstring'''





            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[Any]          =						len(a__    ) + 1
                 feature_blocks.append((F'''res{block_index}''', v)    )

            __snake_case			:List[Any]          =						nn.ModuleDict(a__    )



       def 					__lowercase						(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





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




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def 					__lowercase						(							self			,						a__    ) ->      str:






            '''simple docstring'''





            __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]]:






            '''simple docstring'''





            if x not in self:
                 __snake_case			:Optional[Any]          =						self.convert_name_to_timm(a__    )
                 __snake_case			:List[str]          =						partial(lambda: (timm.create_model(a__			,						pretrained=a__    ).eval(), None)    )

            else:
                 __snake_case			:List[str]          =						super().__getitem__(a__    )

            return val




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __getitem__(							self			,						a__    ) ->      Callable[[], nn.Module]:






            '''simple docstring'''





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






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   List[Any]	,snake_case__     :   List[Tuple[str, str]]  ):




     '''simple docstring'''






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






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




     '''simple docstring'''






     print(f'''Converting {name}...'''  )
     with torch.no_grad():
          __snake_case       , __snake_case			:List[str]          =						from_model_func()
          __snake_case			:Tuple          =						our_model_func(snake_case__  ).eval()
          __snake_case			:Optional[Any]          =						ModuleTransfer(src=snake_case__	,dest=snake_case__	,raise_if_mismatch=snake_case__  )
          __snake_case			:Any          =						torch.randn((1, 3, 224, 224)  )
          module_transfer(snake_case__  )

     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			:Union[str, Any]          =						[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
          __snake_case			:int          =						manually_copy_vissl_head(snake_case__	,our_model.state_dict()	,snake_case__  )
          our_model.load_state_dict(snake_case__  )

     __snake_case			:List[Any]          =						our_model(snake_case__	,output_hidden_states=snake_case__  )
     __snake_case			:Tuple          =						(
         our_outputs.logits if isinstance(snake_case__	,snake_case__  ) else our_outputs.last_hidden_state
     )

     __snake_case			:Union[str, Any]          =						from_model(snake_case__  )
     __snake_case			:Tuple          =						from_output[-1] if type(snake_case__  ) 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			:Dict          =						our_outputs.hidden_states[-1]

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

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

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






def   UpperCamelCase  (       snake_case__     :   Path	,snake_case__     :   str = None	,snake_case__     :   bool = True  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						"""imagenet-1k-id2label.json"""
     __snake_case			:str          =						1000
     __snake_case			:Tuple          =						(1, num_labels)

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

     __snake_case			:Dict          =						idalabel
     __snake_case			:str          =						{v: k for k, v in idalabel.items()}

     __snake_case			:Union[str, Any]          =						partial(snake_case__	,num_labels=snake_case__	,idalabel=snake_case__	,labelaid=snake_case__  )

     __snake_case			:List[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  ),
     }

     __snake_case			:Optional[Any]          =						NameToOurModelFuncMap()
     __snake_case			:Tuple          =						NameToFromModelFuncMap()
     # add seer weights logic

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

     # pretrained
     __snake_case			:List[Any]          =						partial(
         snake_case__	,"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""	,lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )	,)

     __snake_case			:Optional[Any]          =						partial(
         snake_case__	,"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""	,lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )	,)

     __snake_case			:List[str]          =						partial(
         snake_case__	,"""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			:Optional[Any]          =						partial(
         snake_case__	,"""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=6_2_0.8_3	,w_m=2.5_2  )  )  )	,)

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

     __snake_case			:Optional[int]          =						partial(
         snake_case__	,"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""	,lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )	,)

     __snake_case			:Optional[int]          =						partial(
         snake_case__	,"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""	,lambda: FakeRegNetVisslWrapper(RegNetYaaagf()  )	,)

     __snake_case			:Optional[Any]          =						partial(
         snake_case__	,"""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=6_2_0.8_3	,w_m=2.5_2  )  )  )	,)

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


if __name__ == "__main__":
     lowerCamelCase__							=     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__							=     parser.parse_args()

     lowerCamelCase__							=     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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase__							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


