





"""simple docstring"""




from __future__ import annotations





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	int    						=  0
  A_  :	str    						=  len(_UpperCAmelCase ) - 1
  while left <= right:
    A_  :	Any    						=  (left + right) // 2
    if left == right:
      if sorted_list[middle] < item:
        A_  :	Optional[Any]    						=  middle + 1
      break
    elif sorted_list[middle] < item:
      A_  :	Tuple    						=  middle + 1
    else:
      A_  :	List[Any]    						=  middle - 1
  sorted_list.insert(_UpperCAmelCase			, _UpperCAmelCase )
  return sorted_list





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	str    						=  []
  while left and right:
    if left[0][0] < right[0][0]:
      result.append(left.pop(0 ) )
    else:
      result.append(right.pop(0 ) )
  return result + left + right





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  len(_UpperCAmelCase )
  if length <= 1:
    return list_ad
  A_  :	int    						=  length // 2
  return merge(sortlist_ad(list_ad[:middle] )			, sortlist_ad(list_ad[middle:] ) )





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  if len(_UpperCAmelCase ) <= 1:
    return collection
  A_  :	Optional[int]    						=  []
  A_  :	int    						=  False
  for i in range(0			, len(_UpperCAmelCase )			, 2 ):
    if i == len(_UpperCAmelCase ) - 1:
      A_  :	str    						=  True
    else:
      if collection[i] < collection[i + 1]:
        two_paired_list.append([collection[i], collection[i + 1]] )
      else:
        two_paired_list.append([collection[i + 1], collection[i]] )
  A_  :	Union[str, Any]    						=  sortlist_ad(_UpperCAmelCase )
  A_  :	List[Any]    						=  [i[0] for i in sorted_list_ad]
  result.append(sorted_list_ad[-1][1] )
  if has_last_odd_item:
    A_  :	Tuple    						=  collection[-1]
    A_  :	Optional[Any]    						=  binary_search_insertion(_UpperCAmelCase			, _UpperCAmelCase )
  A_  :	Optional[int]    						=  False
  for i in range(len(_UpperCAmelCase ) - 1 ):
    if result[i] == collection[-1] and has_last_odd_item:
      A_  :	Any    						=  True
    A_  :	List[str]    						=  sorted_list_ad[i][1]
    # If last_odd_item is inserted before the item's index,
    # you should forward index one more.
    if is_last_odd_item_inserted_before_this_index:
      A_  :	Dict    						=  result[: i + 2] + binary_search_insertion(result[i + 2 :]			, _UpperCAmelCase )
    else:
      A_  :	str    						=  result[: i + 1] + binary_search_insertion(result[i + 1 :]			, _UpperCAmelCase )

  return result


if __name__ == "__main__":
   lowerCamelCase_					:						str      = input('Enter numbers separated by a comma:\n').strip()
   lowerCamelCase_					:						int      = [int(item) for item in user_input.split(',')]
   print(merge_insertion_sort(unsorted))






"""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()
lowerCamelCase_					:						Dict      = logging.get_logger()

@dataclass
class 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			nn.Module
      lowercase_				:			List[nn.Module]        =       field(default_factory=UpperCAmelCase__		)
      lowercase_				:			list        =       field(default_factory=UpperCAmelCase__		)
      lowercase_				:			Dict[str, nn.Module]        =       field(default_factory=UpperCAmelCase__		)

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	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_  )
          A_  :	List[str]    						=  m

      def __call__(					self ,				snake_case_  ):

        """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  ):

        """simple docstring"""

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


class 			_UpperCAmelCase  (       nn.Module		):



      '''simple docstring'''

      def __init__(					self ,				snake_case_  ):

        """simple docstring"""

        super().__init__()

        A_  :	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}"""
          A_  :	str    						=  len(snake_case_  ) + 1
          feature_blocks.append((F"""res{block_index}""", v)  )

        A_  :	Dict    						=  nn.ModuleDict(snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

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


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      def  lowerCamelCase_      (					self  ):

        """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 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





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

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

    A_  :	Tuple    						=  torch.randn((1, 3, 32, 32) )
    # trace both
    A_  :	int    						=  Tracker(_UpperCAmelCase )
    A_  :	Any    						=  dest_tracker(_UpperCAmelCase ).parametrized

    pprint(dest_tracker.nameamodule )
    A_  :	Union[str, Any]    						=  Tracker(_UpperCAmelCase )
    A_  :	Tuple    						=  src_tracker(_UpperCAmelCase ).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(_UpperCAmelCase ):
    A_  :	Optional[Any]    						=  OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        A_  :	List[Any]    						=  param
    return params_dict

  A_  :	Dict    						=  {}

  A_  :	Optional[Any]    						=  to_params_dict(_UpperCAmelCase )
  A_  :	List[str]    						=  to_params_dict(_UpperCAmelCase )

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

  return from_to_ours_keys





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = None			, _UpperCAmelCase = True ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  'imagenet-1k-id2label.json'
  A_  :	Dict    						=  1000

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

  A_  :	List[Any]    						=  idalabel
  A_  :	List[Any]    						=  {v: k for k, v in idalabel.items()}

  A_  :	int    						=  partial(_UpperCAmelCase			, num_labels=_UpperCAmelCase			, idalabel=_UpperCAmelCase			, labelaid=_UpperCAmelCase )

  A_  :	Dict    						=  {
      '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(_UpperCAmelCase ) -> Tuple[Dict, Dict]:
    A_  :	Any    						=  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_  :	Optional[Any]    						=  files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  A_  :	Optional[Any]    						=  {
      '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_  :	List[str]    						=  get_from_to_our_keys(_UpperCAmelCase )

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

    A_  :	List[str]    						=  {}

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

   lowerCamelCase_					:						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)