"""simple docstring"""



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()
__magic_name__    				=					logging.get_logger()

@dataclass
class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 a_       :	nn.Module
 a_       :	List[nn.Module]  			=field(default_factory=a	)
 a_       :	list  			=field(default_factory=a	)
 a_       :	Dict[str, nn.Module]  			=field(default_factory=a	)


 def        _lowerCAmelCase				(   self		:      Union[str, Any]					,   _snake_case		:      Tuple					,   _snake_case		:      Tensor					,   _snake_case		:      Tensor					,   _snake_case		:      str				)				->			Dict:




    '''simple docstring'''






    a__     				=		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				)
       a__     				=		m


 def __call__(   self		:      List[Any]					,   _snake_case		:      Tensor				)				->			Union[str, Any]:




    '''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        _lowerCAmelCase				(   self		:      List[str]				)				->			int:




    '''simple docstring'''






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

class SCREAMING_SNAKE_CASE     (    nn.Module	):







 """simple docstring"""


 def __init__(   self		:      List[str]					,   _snake_case		:      nn.Module				)				->			Dict:




    '''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(_snake_case				) + 1
       feature_blocks.append((F'''res{block_index}''', v)				)

    a__     				=		nn.ModuleDict(_snake_case				)



 def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      Tensor				)				->			Union[str, Any]:




    '''simple docstring'''






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

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Any				)				->			str:




    '''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 ( UpperCAmelCase__					)							->     Dict[str, str]:


   '''simple docstring'''


   a__     				=		RegNetConfig(depths=[2, 7, 17, 1],hidden_sizes=[8, 8, 8, 8],groups_width=8					)
   if "in1k" in model_name:
      a__     				=		RegNetForImageClassification(UpperCAmelCase__					)
   else:
      a__     				=		RegNetModel(UpperCAmelCase__					)
   # create from model (with small weights)
   a__     				=		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():
      a__     				=		from_model.eval()
      a__     				=		our_model.eval()

      a__     				=		torch.randn((1, 3, 32, 32)					)
      # trace both
      a__     				=		Tracker(UpperCAmelCase__					)
      a__     				=		dest_tracker(UpperCAmelCase__					).parametrized

      pprint(dest_tracker.nameamodule					)
      a__     				=		Tracker(UpperCAmelCase__					)
      a__     				=		src_tracker(UpperCAmelCase__					).parametrized

   # convert the keys -> module dict to keys -> params
   def to_params_dict(UpperCAmelCase__					):
      a__     				=		OrderedDict()
      for name, module in dict_with_modules.items():
         for param_name, param in module.state_dict().items():
            a__     				=		param
      return params_dict

   a__     				=		{}

   a__     				=		to_params_dict(UpperCAmelCase__					)
   a__     				=		to_params_dict(UpperCAmelCase__					)

   for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),dst_state_dict.items()					):
      a__     				=		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:
      a__     				=		'classifier.1.weight'
      a__     				=		'classifier.1.bias'

   return from_to_ours_keys





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__ = None,UpperCAmelCase__ = True					)							->     List[str]:


   '''simple docstring'''


   a__     				=		'imagenet-1k-id2label.json'
   a__     				=		10_00

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

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

   a__     				=		partial(UpperCAmelCase__,num_labels=UpperCAmelCase__,idalabel=UpperCAmelCase__,labelaid=UpperCAmelCase__					)

   a__     				=		{
       '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(UpperCAmelCase__					) -> Tuple[Dict, Dict]:
      a__     				=		torch.hub.load_state_dict_from_url(UpperCAmelCase__,model_dir=str(UpperCAmelCase__					),map_location='cpu'					)
      # check if we have a head, if yes add it
      a__     				=		files['classy_state_dict']['base_model']['model']
      return model_state_dict["trunk"], model_state_dict["heads"]

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

   a__     				=		get_from_to_our_keys(UpperCAmelCase__					)

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

      a__     				=		{}

      a__     				=		list(from_state_dict.keys()					)
      a__     				=		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
         a__     				=		re.sub(UpperCAmelCase__,'',UpperCAmelCase__					)
         # 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
         a__     				=		from_to_ours_keys[src_key]
         # store the parameter with our key
         a__     				=		from_state_dict[key]
         not_used_keys.remove(UpperCAmelCase__					)
      # check that all keys have been updated
      assert len(UpperCAmelCase__					) == 0, f'''Some keys where not used {",".join(UpperCAmelCase__					)}'''

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

      # save our state dict to disk
      torch.save(UpperCAmelCase__,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
      a__     				=		names_to_config[model_name]
      a__     				=		RegNetModel
      if "in1k" in model_name:
         a__     				=		RegNetForImageClassification
      a__     				=		our_model_func(UpperCAmelCase__					)
      # 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
      a__     				=		our_model.state_dict().keys()
      PreTrainedModel._load_pretrained_model_low_mem(
          UpperCAmelCase__,UpperCAmelCase__,[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,)
      a__     				=		3_84
      # we can use the convnext one
      a__     				=		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k',size=UpperCAmelCase__					)
      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__":
   __magic_name__    				=					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.",
   )

   __magic_name__    				=					parser.parse_args()

   __magic_name__    				=					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"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__    				=					{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DonutSwinModel",
         "DonutSwinPreTrainedModel",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					["DonutFeatureExtractor"]
     __magic_name__    				=					["DonutImageProcessor"]


if TYPE_CHECKING:
     from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
     from .processing_donut import DonutProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_donut_swin import (
              DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              DonutSwinModel,
              DonutSwinPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_donut import DonutFeatureExtractor
          from .image_processing_donut import DonutImageProcessor

else:
     import sys

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


