



'''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()
_UpperCamelCase							    =						logging.get_logger()






@dataclass
class  lowerCamelCase_   :
  """simple docstring"""




  a_							 =42
  a_							 =field(default_factory=SCREAMING_SNAKE_CASE_					)
  a_							 =field(default_factory=SCREAMING_SNAKE_CASE_					)
  a_							 =field(default_factory=SCREAMING_SNAKE_CASE_					)




  def 						_lowercase					(			self				:     Optional[Any]      ,     _a				:     Dict      ,     _a				:     Tensor      ,     _a				:     Tensor      ,     _a				:     str       ) -> Union[str, Any]:
    __lowerCamelCase	:	Dict   	=			len(list(m.modules()       )       ) == 1 or isinstance(_a      ,     nn.Convad       ) or isinstance(_a      ,     nn.BatchNormad       )
    if has_not_submodules:
      self.traced.append(_a       )
      __lowerCamelCase	:	Dict   	=			m




  def __call__(			self				:     Tuple      ,     _a				:     Tensor       ) -> Union[str, Any]:
    for name, m in self.module.named_modules():
      self.handles.append(m.register_forward_hook(partial(self._forward_hook      ,     name=_a       )       )       )
    self.module(_a       )
    [x.remove() for x in self.handles]
    return self




  @property
  def 						_lowercase					(			self				:     int       ) -> Tuple:
    # check the len of the state_dict keys to see if we have learnable params
    return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()       )       ) > 0}






class  lowerCamelCase_   (    nn.Module					):
  """simple docstring"""




  def __init__(			self				:     int      ,     _a				:     nn.Module       ) -> Any:
    super().__init__()

    __lowerCamelCase	:	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}'
      __lowerCamelCase	:	Tuple   	=			len(_a       ) + 1
      feature_blocks.append((f'res{block_index}', v)       )

    __lowerCamelCase	:	int   	=			nn.ModuleDict(_a       )




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     Tensor       ) -> Optional[Any]:
    return get_trunk_forward_outputs(
        _a      ,     out_feat_keys=_a      ,     feature_blocks=self._feature_blocks      ,     )






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    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 	a_					(							_lowerCAmelCase	)				->				Dict[str, str]:
  __lowerCamelCase	:	int   	=			RegNetConfig(depths=[2, 7, 17, 1]	,hidden_sizes=[8, 8, 8, 8]	,groups_width=8	)
  if "in1k" in model_name:
    __lowerCamelCase	:	List[Any]   	=			RegNetForImageClassification(_lowerCAmelCase	)
  else:
    __lowerCamelCase	:	Tuple   	=			RegNetModel(_lowerCAmelCase	)
  # create from model (with small weights)
  __lowerCamelCase	:	Any   	=			FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27	,group_width=1010	,w_a=1744	,w_a=620.83	,w_m=2.52	)	)	)

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

    __lowerCamelCase	:	List[str]   	=			torch.randn((1, 3, 32, 32)	)
    # trace both
    __lowerCamelCase	:	Any   	=			Tracker(_lowerCAmelCase	)
    __lowerCamelCase	:	Optional[Any]   	=			dest_tracker(_lowerCAmelCase	).parametrized

    pprint(dest_tracker.nameamodule	)
    __lowerCamelCase	:	Any   	=			Tracker(_lowerCAmelCase	)
    __lowerCamelCase	:	Union[str, Any]   	=			src_tracker(_lowerCAmelCase	).parametrized

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

  __lowerCamelCase	:	Tuple   	=			{}

  __lowerCamelCase	:	Any   	=			to_params_dict(_lowerCAmelCase	)
  __lowerCamelCase	:	Dict   	=			to_params_dict(_lowerCAmelCase	)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()	,dst_state_dict.items()	):
    __lowerCamelCase	:	Union[str, 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:
    __lowerCamelCase	:	Optional[int]   	=			'classifier.1.weight'
    __lowerCamelCase	:	Dict   	=			'classifier.1.bias'

  return from_to_ours_keys

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase = None	,_lowerCAmelCase = True	)				->				Dict:
  __lowerCamelCase	:	str   	=			'imagenet-1k-id2label.json'
  __lowerCamelCase	:	Tuple   	=			1000

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

  __lowerCamelCase	:	Union[str, Any]   	=			idalabel
  __lowerCamelCase	:	Any   	=			{v: k for k, v in idalabel.items()}

  __lowerCamelCase	:	Tuple   	=			partial(_lowerCAmelCase	,num_labels=_lowerCAmelCase	,idalabel=_lowerCAmelCase	,labelaid=_lowerCAmelCase	)

  __lowerCamelCase	:	str   	=			{
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,hidden_sizes=[2020, 4040, 11110, 28280]	,groups_width=1010	),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,hidden_sizes=[2020, 4040, 11110, 28280]	,groups_width=1010	),
  }

  # add seer weights logic
  def load_using_classy_vision(_lowerCAmelCase	) -> Tuple[Dict, Dict]:
    __lowerCamelCase	:	str   	=			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
    __lowerCamelCase	:	Dict   	=			files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  __lowerCamelCase	:	Tuple   	=			{
      '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'	,),
  }

  __lowerCamelCase	:	Optional[int]   	=			get_from_to_our_keys(_lowerCAmelCase	)

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

    __lowerCamelCase	:	Dict   	=			{}

    __lowerCamelCase	:	Union[str, Any]   	=			list(from_state_dict.keys()	)
    __lowerCamelCase	:	List[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
      __lowerCamelCase	:	Dict   	=			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
      __lowerCamelCase	:	List[Any]   	=			from_to_ours_keys[src_key]
      # store the parameter with our key
      __lowerCamelCase	:	List[str]   	=			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
    __lowerCamelCase	:	Dict   	=			names_to_config[model_name]
    __lowerCamelCase	:	List[str]   	=			RegNetModel
    if "in1k" in model_name:
      __lowerCamelCase	:	Union[str, Any]   	=			RegNetForImageClassification
    __lowerCamelCase	:	Optional[int]   	=			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
    __lowerCamelCase	:	Union[str, Any]   	=			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	,)
    __lowerCamelCase	:	int   	=			384
    # we can use the convnext one
    __lowerCamelCase	:	Optional[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__":
   _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'''





from typing import TYPE_CHECKING

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


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

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






