

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()
_SCREAMING_SNAKE_CASE          = logging.get_logger()


@dataclass
class     lowerCamelCase_   :
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  field(default_factory=_lowerCAmelCase)
 _lowerCAmelCase       		=  field(default_factory=_lowerCAmelCase)
 _lowerCAmelCase       		=  field(default_factory=_lowerCAmelCase)




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Dict		=    len(list(m.modules()       )       ) == 1 or isinstance(_UpperCAmelCase     ,     nn.Convad       ) or isinstance(_UpperCAmelCase     ,     nn.BatchNormad       )
   if has_not_submodules:
     self.traced.append(_UpperCAmelCase       )
     __UpperCAmelCase  :     Union[str, Any]		=    m




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




 @property
 def 						a					( self       ):
   # 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):




 def __init__( self     ,     _UpperCAmelCase       ):
   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  :     Union[str, Any]		=    len(_UpperCAmelCase       ) + 1
     feature_blocks.append((F"res{block_index}", v)       )

   __UpperCAmelCase  :     Union[str, Any]		=    nn.ModuleDict(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   return get_trunk_forward_outputs(
       _UpperCAmelCase     ,     out_feat_keys=_UpperCAmelCase     ,     feature_blocks=self._feature_blocks     ,     )



class     lowerCamelCase_   ( _lowerCAmelCase):




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

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

    __UpperCAmelCase  :     List[Any]		=    torch.randn((1, 3, 32, 32)     )
    # trace both
    __UpperCAmelCase  :     Dict		=    Tracker(snake_case_     )
    __UpperCAmelCase  :     Dict		=    dest_tracker(snake_case_     ).parametrized

    pprint(dest_tracker.nameamodule     )
    __UpperCAmelCase  :     Optional[Any]		=    Tracker(snake_case_     )
    __UpperCAmelCase  :     List[Any]		=    src_tracker(snake_case_     ).parametrized

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

  __UpperCAmelCase  :     Any		=    {}

  __UpperCAmelCase  :     List[str]		=    to_params_dict(snake_case_     )
  __UpperCAmelCase  :     Dict		=    to_params_dict(snake_case_     )

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,					dst_state_dict.items()     ):
    __UpperCAmelCase  :     Tuple		=    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[Any]		=    'classifier.1.weight'
    __UpperCAmelCase  :     Any		=    'classifier.1.bias'

  return from_to_ours_keys





def    _snake_case		(snake_case_				:    Path						,					snake_case_				:    str = None						,					snake_case_				:    bool = True     )       ->		Union[str, Any]:
  __UpperCAmelCase  :     Any		=    'imagenet-1k-id2label.json'
  __UpperCAmelCase  :     Any		=    1000

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

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

  __UpperCAmelCase  :     Tuple		=    partial(snake_case_						,					num_labels=snake_case_						,					idalabel=snake_case_						,					labelaid=snake_case_     )

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

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

  __UpperCAmelCase  :     Union[str, Any]		=    get_from_to_our_keys(snake_case_     )

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

    __UpperCAmelCase  :     str		=    {}

    __UpperCAmelCase  :     Dict		=    list(from_state_dict.keys()     )
    __UpperCAmelCase  :     Union[str, Any]		=    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  :     Optional[int]		=    re.sub(snake_case_						,					''						,					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  :     Optional[Any]		=    from_to_ours_keys[src_key]
      # store the parameter with our key
      __UpperCAmelCase  :     Optional[Any]		=    from_state_dict[key]
      not_used_keys.remove(snake_case_     )
    # check that all keys have been updated
    assert len(snake_case_     ) == 0, F"Some keys where not used {','.join(snake_case_     )}"

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

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

      _SCREAMING_SNAKE_CASE          = parser.parse_args()

      _SCREAMING_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)








def    _snake_case		(snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int     )       ->		int:
  return len_board * len_board_column * row + column





def    _snake_case		(snake_case_				:    list[list[str]]						,					snake_case_				:    str						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    set[int]						,					)       ->		bool:

  if board[row][column] != word[word_index]:
    return False

  if word_index == len(snake_case_     ) - 1:
    return True

  __UpperCAmelCase  :     List[Any]		=    [(0, 1), (0, -1), (-1, 0), (1, 0)]
  __UpperCAmelCase  :     Dict		=    len(snake_case_     )
  __UpperCAmelCase  :     str		=    len(board[0]     )
  for direction in traverts_directions:
    __UpperCAmelCase  :     Dict		=    row + direction[0]
    __UpperCAmelCase  :     Optional[int]		=    column + direction[1]
    if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
      continue

    __UpperCAmelCase  :     str		=    get_point_key(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )
    if key in visited_points_set:
      continue

    visited_points_set.add(snake_case_     )
    if exits_word(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_						,					word_index + 1						,					snake_case_     ):
      return True

    visited_points_set.remove(snake_case_     )

  return False





def    _snake_case		(snake_case_				:    list[list[str]]						,					snake_case_				:    str     )       ->		bool:
  __UpperCAmelCase  :     str		=    (
      'The board should be a non empty matrix of single chars strings.'
  )

  __UpperCAmelCase  :     Any		=    len(snake_case_     )
  if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
    raise ValueError(snake_case_     )

  for row in board:
    if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
      raise ValueError(snake_case_     )

    for item in row:
      if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) != 1:
        raise ValueError(snake_case_     )

    # Validate word
  if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
    raise ValueError(
        'The word parameter should be a string of length greater than 0.'     )

  __UpperCAmelCase  :     str		=    len(board[0]     )
  for i in range(snake_case_     ):
    for j in range(snake_case_     ):
      if exits_word(
          snake_case_						,					snake_case_						,					snake_case_						,					snake_case_						,					0						,					{get_point_key(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )}     ):
        return True

  return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()





