

'''simple docstring'''




import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class        lowercase_	(		enum.Enum ):

  '''simple docstring'''





  __lowerCAmelCase					: Dict      =   "timm"
  __lowerCAmelCase					: Any      =   "transformers"




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[Iterable[str]]       ,   SCREAMING_SNAKE_CASE      :   Optional[Iterable[int]]       ,   SCREAMING_SNAKE_CASE      :   Optional[Iterable[str]]						):
  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'						)

  if out_features is not None:
    if not isinstance(SCREAMING_SNAKE_CASE       ,   (list,)						):
      raise ValueError(f'''out_features must be a list {type(SCREAMING_SNAKE_CASE						)}'''						)
    if any(feat not in stage_names for feat in out_features						):
      raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''						)

  if out_indices is not None:
    if not isinstance(SCREAMING_SNAKE_CASE       ,   (list, tuple)						):
      raise ValueError(f'''out_indices must be a list or tuple, got {type(SCREAMING_SNAKE_CASE						)}'''						)
    if any(idx >= len(SCREAMING_SNAKE_CASE						) for idx in out_indices						):
      raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'						)

  if out_features is not None and out_indices is not None:
    if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
      raise ValueError('out_features and out_indices should have the same length if both are set'						)
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('out_features and out_indices should correspond to the same stages if both are set'						)


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[List[str]]       ,   SCREAMING_SNAKE_CASE      :   Optional[Union[List[int], Tuple[int]]]       ,   SCREAMING_SNAKE_CASE      :   List[str]       ,   ):
  if out_indices is None and out_features is None:
    UpperCAmelCase													=	[len(SCREAMING_SNAKE_CASE						) - 1]
    UpperCAmelCase													=	[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    UpperCAmelCase													=	[stage_names.index(SCREAMING_SNAKE_CASE						) for layer in out_features]
  elif out_features is None and out_indices is not None:
    UpperCAmelCase													=	[stage_names[idx] for idx in out_indices]
  return out_features, out_indices


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[List[str]]       ,   SCREAMING_SNAKE_CASE      :   Optional[Union[List[int], Tuple[int]]]       ,   SCREAMING_SNAKE_CASE      :   List[str]       ,   ):
  verify_out_features_out_indices(out_features=SCREAMING_SNAKE_CASE       ,   out_indices=SCREAMING_SNAKE_CASE       ,   stage_names=SCREAMING_SNAKE_CASE						)
  UpperCAmelCase							,      UpperCAmelCase													=	_align_output_features_output_indices(
      out_features=SCREAMING_SNAKE_CASE       ,   out_indices=SCREAMING_SNAKE_CASE       ,   stage_names=SCREAMING_SNAKE_CASE						)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=SCREAMING_SNAKE_CASE       ,   out_indices=SCREAMING_SNAKE_CASE       ,   stage_names=SCREAMING_SNAKE_CASE						)
  return output_features, output_indices



class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: Optional[BackboneType]      =   None
  def 						snake_case_			(     self   , a_     )							->				None:


    """simple docstring"""
    if getattr(self   , '_backbone'   , a_     ) is None:
      raise ValueError('self._backbone must be set before calling _init_timm_backbone'     )

    # These will diagree with the defaults for the transformers models e.g. for resnet50
    # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
    # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
    UpperCAmelCase													=	[stage['module'] for stage in self._backbone.feature_info.info]
    UpperCAmelCase													=	[stage['num_chs'] for stage in self._backbone.feature_info.info]
    UpperCAmelCase													=	self._backbone.feature_info.out_indices
    UpperCAmelCase													=	self._backbone.feature_info.module_name()

    # We verify the out indices and out features are valid
    verify_out_features_out_indices(
        out_features=a_   , out_indices=a_   , stage_names=self.stage_names     )
    UpperCAmelCase							,      UpperCAmelCase													=	out_features, out_indices
  def 						snake_case_			(     self   , a_     )							->				None:


    """simple docstring"""
    UpperCAmelCase													=	getattr(a_   , 'stage_names'     )
    UpperCAmelCase													=	getattr(a_   , 'out_features'   , a_     )
    UpperCAmelCase													=	getattr(a_   , 'out_indices'   , a_     )

    UpperCAmelCase													=	stage_names
    UpperCAmelCase							,      UpperCAmelCase													=	get_aligned_output_features_output_indices(
        out_features=a_   , out_indices=a_   , stage_names=a_     )
    # Number of channels for each stage. This is set in the transformer backbone model init
    UpperCAmelCase													=	None
  def 						snake_case_			(     self   , a_     )							->				None:


    """simple docstring"""
    UpperCAmelCase													=	config

    UpperCAmelCase													=	getattr(a_   , 'use_timm_backbone'   , a_     )
    UpperCAmelCase													=	BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

    if self.backbone_type == BackboneType.TIMM:
      self._init_timm_backbone(a_     )
    elif self.backbone_type == BackboneType.TRANSFORMERS:
      self._init_transformers_backbone(a_     )
    else:
      raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''     )
  @property
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    return self._out_features
  @out_features.setter
  def 						snake_case_			(     self   , a_     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase													=	get_aligned_output_features_output_indices(
        out_features=a_   , out_indices=a_   , stage_names=self.stage_names     )
  @property
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    return self._out_indices
  @out_indices.setter
  def 						snake_case_			(     self   , a_     )							->				Any:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase													=	get_aligned_output_features_output_indices(
        out_features=a_   , out_indices=a_   , stage_names=self.stage_names     )
  @property
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names     )}
  @property
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    return [self.out_feature_channels[name] for name in self.out_features]
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	dict(inspect.signature(self.forward     ).parameters     )
    UpperCAmelCase													=	{k: v for k, v in kwargs.items() if k in signature}
    return self(*a_   , **a_     )
  def 						snake_case_			(     self   , a_   , a_ = None   , a_ = None   , a_ = None   , )							->				Optional[Any]:


    """simple docstring"""
    raise NotImplementedError('This method should be implemented by the derived class.'     )





  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	super().to_dict()
    UpperCAmelCase													=	output.pop('_out_features'     )
    UpperCAmelCase													=	output.pop('_out_indices'     )
    return output




class        lowercase_	:

  '''simple docstring'''





  @property
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return self._out_features
  @out_features.setter
  def 						snake_case_			(     self   , a_     )							->				str:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase													=	get_aligned_output_features_output_indices(
        out_features=a_   , out_indices=a_   , stage_names=self.stage_names     )
  @property
  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""
    return self._out_indices
  @out_indices.setter
  def 						snake_case_			(     self   , a_     )							->				Optional[Any]:


    """simple docstring"""
    UpperCAmelCase							,      UpperCAmelCase													=	get_aligned_output_features_output_indices(
        out_features=a_   , out_indices=a_   , stage_names=self.stage_names     )





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	super().to_dict()
    UpperCAmelCase													=	output.pop('_out_features'     )
    UpperCAmelCase													=	output.pop('_out_indices'     )
    return output





'''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()
_a				: Optional[int] 							=						logging.get_logger()



@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: nn.Module
  __lowerCAmelCase					: List[nn.Module]      =   field(default_factory=a )
  __lowerCAmelCase					: list      =   field(default_factory=a )
  __lowerCAmelCase					: Dict[str, nn.Module]      =   field(default_factory=a )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_     )							->				Optional[int]:


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


    """simple docstring"""
    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 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    # 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        lowercase_	(		nn.Module ):

  '''simple docstring'''





  def __init__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    super().__init__()

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

    UpperCAmelCase													=	nn.ModuleDict(a_     )





  def 						snake_case_			(     self   , a_     )							->				Optional[Any]:


    """simple docstring"""
    return get_trunk_forward_outputs(
        a_   , out_feat_keys=a_   , feature_blocks=self._feature_blocks   , )




class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	RegNetConfig(depths=[2, 7, 17, 1]       ,   hidden_sizes=[8, 8, 8, 8]       ,   groups_width=8						)
  if "in1k" in model_name:
    UpperCAmelCase													=	RegNetForImageClassification(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	RegNetModel(SCREAMING_SNAKE_CASE						)
  # create from model (with small weights)
  UpperCAmelCase													=	FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27       ,   group_width=1010       ,   w_a=1744       ,   w_a=620.83       ,   w_m=2.52						)						)						)

  with torch.no_grad():
    UpperCAmelCase													=	from_model.eval()
    UpperCAmelCase													=	our_model.eval()

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

    pprint(dest_tracker.nameamodule						)
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	src_tracker(SCREAMING_SNAKE_CASE						).parametrized

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

  UpperCAmelCase													=	{}

  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)

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

  return from_to_ours_keys


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Path       ,   SCREAMING_SNAKE_CASE      :   str = None       ,   SCREAMING_SNAKE_CASE      :   bool = True						):
  UpperCAmelCase													=	'imagenet-1k-id2label.json'
  UpperCAmelCase													=	1000

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

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

  UpperCAmelCase													=	partial(SCREAMING_SNAKE_CASE       ,   num_labels=SCREAMING_SNAKE_CASE       ,   idalabel=SCREAMING_SNAKE_CASE       ,   labelaid=SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	{
      '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-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
  }

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

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

  UpperCAmelCase													=	get_from_to_our_keys(SCREAMING_SNAKE_CASE						)

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

    UpperCAmelCase													=	{}

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

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

    # save our state dict to disk
    torch.save(SCREAMING_SNAKE_CASE       ,   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													=	names_to_config[model_name]
    UpperCAmelCase													=	RegNetModel
    if "in1k" in model_name:
      UpperCAmelCase													=	RegNetForImageClassification
    UpperCAmelCase													=	our_model_func(SCREAMING_SNAKE_CASE						)
    # 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													=	our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   [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													=	384
    # we can use the convnext one
    UpperCAmelCase													=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,   size=SCREAMING_SNAKE_CASE						)
    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__":
  _a				: Dict 							=						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.',
  )

  _a				: Union[str, Any] 							=						parser.parse_args()

  _a				: Path 							=						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)


