import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
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.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case			      =    logging.get_logger()







@dataclass
class 			__snake_case   :
 __lowerCamelCase    :   nn.Module
 __lowerCamelCase    :   List[nn.Module]     =      field(default_factory=lowerCamelCase__   )
 __lowerCamelCase    :   list     =      field(default_factory=lowerCamelCase__   )
 __lowerCamelCase    :   Dict[str, nn.Module]     =      field(default_factory=lowerCamelCase__   )
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =len(list(m.modules()						)						) == 1 or isinstance(snake_case__		,   nn.Convad						) or isinstance(snake_case__		,   nn.BatchNormad						)
       if has_not_submodules:
             self.traced.append(snake_case__						)
             UpperCAmelCase    : int      =m
 def __call__(  self		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





       for name, m in self.module.named_modules():
             self.handles.append(m.register_forward_hook(partial(self._forward_hook		,   name=snake_case__						)						)						)
       self.module(snake_case__						)
       [x.remove() for x in self.handles]
       return self






 @property
 def 	UpperCAmelCase__       (  self						)	->							Optional[int]:
       '''simple docstring'''





       return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}







class 			__snake_case   (     nn.Module   ):
 def __init__(  self		,   snake_case__						)	->							List[Any]:
       '''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    : Dict      =len(snake_case__						) + 1
             feature_blocks.append((f'''res{block_index}''', v)						)

       UpperCAmelCase    : Any      =nn.ModuleDict(snake_case__						)






 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Optional[Any]:
       '''simple docstring'''





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







class 			__snake_case   (     lowerCamelCase__   ):
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Dict[str, str]:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      =RegNetConfig(depths=[2, 7, 17, 1]		,					hidden_sizes=[8, 8, 8, 8]		,					groups_width=8						)
      if "in1k" in model_name:
            UpperCAmelCase    : Dict      =RegNetForImageClassification(__lowerCAmelCase						)
      else:
            UpperCAmelCase    : List[Any]      =RegNetModel(__lowerCAmelCase						)
      # create from model (with small weights)
      UpperCAmelCase    : List[str]      =FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27		,					group_width=10_10		,					w_a=17_44		,					w_a=620.83		,					w_m=2.52						)						)						)

      with torch.no_grad():
            UpperCAmelCase    : Union[str, Any]      =from_model.eval()
            UpperCAmelCase    : str      =our_model.eval()

            UpperCAmelCase    : Any      =torch.randn((1, 3, 32, 32)						)
            # trace both
            UpperCAmelCase    : Any      =Tracker(__lowerCAmelCase						)
            UpperCAmelCase    : Union[str, Any]      =dest_tracker(__lowerCAmelCase						).parametrized

            pprint(dest_tracker.nameamodule						)
            UpperCAmelCase    : str      =Tracker(__lowerCAmelCase						)
            UpperCAmelCase    : List[str]      =src_tracker(__lowerCAmelCase						).parametrized

      # convert the keys -> module dict to keys -> params
      def to_params_dict(__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =OrderedDict()
            for name, module in dict_with_modules.items():
                  for param_name, param in module.state_dict().items():
                        UpperCAmelCase    : int      =param
            return params_dict

      UpperCAmelCase    : Optional[Any]      ={}

      UpperCAmelCase    : str      =to_params_dict(__lowerCAmelCase						)
      UpperCAmelCase    : str      =to_params_dict(__lowerCAmelCase						)

      for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		,					dst_state_dict.items()						):
            UpperCAmelCase    : Optional[Any]      =dest_key
            logger.info(f'''{src_key} -> {dest_key}'''						)
      # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
      if "in1k" in model_name:
            UpperCAmelCase    : Optional[int]      ='''classifier.1.weight'''
            UpperCAmelCase    : int      ='''classifier.1.bias'''

      return from_to_ours_keys





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase = None		,					__lowerCAmelCase = True						)->  Tuple:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      ='''imagenet-1k-id2label.json'''
      UpperCAmelCase    : str      =10_00

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

      UpperCAmelCase    : Dict      =idalabel
      UpperCAmelCase    : int      ={v: k for k, v in idalabel.items()}

      UpperCAmelCase    : Any      =partial(__lowerCAmelCase		,					num_labels=__lowerCAmelCase		,					idalabel=__lowerCAmelCase		,					labelaid=__lowerCAmelCase						)

      UpperCAmelCase    : Tuple      ={
          '''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-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						),
      }

      # add seer weights logic
      def load_using_classy_vision(__lowerCAmelCase						) -> Tuple[Dict, Dict]:
            UpperCAmelCase    : Optional[int]      =torch.hub.load_state_dict_from_url(__lowerCAmelCase		,					model_dir=str(__lowerCAmelCase						)		,					map_location='''cpu'''						)
            # check if we have a head, if yes add it
            UpperCAmelCase    : Dict      =files['''classy_state_dict''']['''base_model''']['''model''']
            return model_state_dict["trunk"], model_state_dict["heads"]

      UpperCAmelCase    : Dict      ={
          '''regnet-y-10b-seer''': partial(
              __lowerCAmelCase		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''		,					),
          '''regnet-y-10b-seer-in1k''': partial(
              __lowerCAmelCase		,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''		,					),
      }

      UpperCAmelCase    : List[str]      =get_from_to_our_keys(__lowerCAmelCase						)

      if not (save_directory / f'''{model_name}.pth''').exists():
            logger.info('''Loading original state_dict.'''						)
            UpperCAmelCase  ,						UpperCAmelCase    : int      =names_to_from_model[model_name]()
            UpperCAmelCase    : List[Any]      =from_state_dict_trunk
            if "in1k" in model_name:
                  # add the head
                  UpperCAmelCase    : Tuple      ={**from_state_dict_trunk, **from_state_dict_head}
            logger.info('''Done!'''						)

            UpperCAmelCase    : Dict      ={}

            UpperCAmelCase    : Optional[Any]      =list(from_state_dict.keys()						)
            UpperCAmelCase    : str      =R'''\.block.-part.'''
            # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
            for key in from_state_dict.keys():
                  # remove the weird "block[0,1]-part" from the key
                  UpperCAmelCase    : List[str]      =re.sub(__lowerCAmelCase		,					''''''		,					__lowerCAmelCase						)
                  # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
                  UpperCAmelCase    : List[Any]      =from_to_ours_keys[src_key]
                  # store the parameter with our key
                  UpperCAmelCase    : Any      =from_state_dict[key]
                  not_used_keys.remove(__lowerCAmelCase						)
            # check that all keys have been updated
            assert len(__lowerCAmelCase						) == 0, f'''Some keys where not used {','.join(__lowerCAmelCase						)}'''

            logger.info(f'''The following keys were not used: {','.join(__lowerCAmelCase						)}'''						)

            # save our state dict to disk
            torch.save(__lowerCAmelCase		,					save_directory / f'''{model_name}.pth'''						)

            del converted_state_dict
      else:
            logger.info('''The state_dict was already stored on disk.'''						)
      if push_to_hub:
            logger.info(f'''Token is {os.environ['HF_TOKEN']}'''						)
            logger.info('''Loading our model.'''						)
            # create our model
            UpperCAmelCase    : List[Any]      =names_to_config[model_name]
            UpperCAmelCase    : int      =RegNetModel
            if "in1k" in model_name:
                  UpperCAmelCase    : int      =RegNetForImageClassification
            UpperCAmelCase    : Dict      =our_model_func(__lowerCAmelCase						)
            # place our model to the meta device (so remove all the weights)
            our_model.to(torch.device('''meta'''						)						)
            logger.info('''Loading state_dict in our model.'''						)
            # load state dict
            UpperCAmelCase    : Tuple      =our_model.state_dict().keys()
            PreTrainedModel._load_pretrained_model_low_mem(
                __lowerCAmelCase		,					__lowerCAmelCase		,					[save_directory / f'''{model_name}.pth''']						)
            logger.info('''Finally, pushing!'''						)
            # push it to hub
            our_model.push_to_hub(
                repo_path_or_name=save_directory / model_name		,					commit_message='''Add model'''		,					output_dir=save_directory / model_name		,					)
            UpperCAmelCase    : List[Any]      =3_84
            # we can use the convnext one
            UpperCAmelCase    : List[Any]      =AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		,					size=__lowerCAmelCase						)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / model_name		,					commit_message='''Add image processor'''		,					output_dir=save_directory / model_name		,					)


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

     __snake_case			      =    parser.parse_args()

     __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_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
