




"""simple docstring"""

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
a							      =     logging.get_logger()





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	nn.Module
 UpperCAmelCase       :	List[nn.Module]     =      field(default_factory=__lowerCAmelCase				)
 UpperCAmelCase       :	list     =      field(default_factory=__lowerCAmelCase				)

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Tensor       ,				_UpperCAmelCase			:			Tensor						):
       _A     =  len(list(m.modules()						)						) == 1 or isinstance(_UpperCAmelCase       ,				nn.Convad						) or isinstance(_UpperCAmelCase       ,				nn.BatchNormad						)
       if has_not_submodules:
             self.traced.append(_UpperCAmelCase						)

 def __call__(					self			:			List[str]       ,				_UpperCAmelCase			:			Tensor						):
       for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook						)						)
       self.module(_UpperCAmelCase						)
       [x.remove() for x in self.handles]
       return self



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda _UpperCAmelCase						: len(list(x.state_dict().keys()						)						) > 0       ,				self.traced						)						)





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	nn.Module
 UpperCAmelCase       :	nn.Module
 UpperCAmelCase       :	int     =      0
 UpperCAmelCase       :	List     =      field(default_factory=__lowerCAmelCase				)
 UpperCAmelCase       :	List     =      field(default_factory=__lowerCAmelCase				)

 def __call__(					self			:			Dict       ,				_UpperCAmelCase			:			Tensor						):
       _A     =  Tracker(self.dest						)(_UpperCAmelCase						).parametrized
       _A     =  Tracker(self.src						)(_UpperCAmelCase						).parametrized

       _A     =  list(filter(lambda _UpperCAmelCase						: type(_UpperCAmelCase						) not in self.src_skip       ,				_UpperCAmelCase						)						)
       _A     =  list(filter(lambda _UpperCAmelCase						: type(_UpperCAmelCase						) not in self.dest_skip       ,				_UpperCAmelCase						)						)

       if len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
             raise Exception(
                 F'''Numbers of operations are different. Source module has {len(_UpperCAmelCase						)} operations while'''
                 F''' destination module has {len(_UpperCAmelCase						)}.'''						)

       for dest_m, src_m in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             dest_m.load_state_dict(src_m.state_dict()						)
             if self.verbose == 1:
                   print(F'''Transfered from={src_m} to={dest_m}'''						)






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   ResNetConfig							,    _snake_case				:   Path							,    _snake_case				:   bool = True				)       ->   Union[str, Any]:






      '''simple docstring'''


      print(F'''Converting {name}...'''				)
      with torch.no_grad():
            _A     =  timm.create_model(_snake_case							,    pretrained=_snake_case				).eval()
            _A     =  ResNetForImageClassification(_snake_case				).eval()
            _A     =  ModuleTransfer(src=_snake_case							,    dest=_snake_case				)
            _A     =  torch.randn((1, 3, 2_24, 2_24)				)
            module_transfer(_snake_case				)

      assert torch.allclose(from_model(_snake_case				)							,    our_model(_snake_case				).logits				), "The model logits don't match the original one."

      _A     =  F'''resnet{"-".join(name.split("resnet"				)				)}'''
      print(_snake_case				)

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name							,    commit_message='Add model'							,    use_temp_dir=_snake_case							,    )

            # we can use the convnext one
            _A     =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name							,    commit_message='Add image processor'							,    use_temp_dir=_snake_case							,    )

            print(F'''Pushed {checkpoint_name}'''				)






def 		_snake_case	(   _snake_case				:   Path							,    _snake_case				:   str = None							,    _snake_case				:   bool = True				)       ->   Tuple:






      '''simple docstring'''


      _A     =  'imagenet-1k-id2label.json'
      _A     =  10_00
      _A     =  (1, num_labels)

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

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

      _A     =  partial(_snake_case							,    num_labels=_snake_case							,    idalabel=_snake_case							,    labelaid=_snake_case				)

      _A     =  {
          'resnet18': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]							,    hidden_sizes=[64, 1_28, 2_56, 5_12]							,    layer_type='basic'				),
          'resnet26': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
          'resnet34': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]							,    hidden_sizes=[64, 1_28, 2_56, 5_12]							,    layer_type='basic'				),
          'resnet50': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
          'resnet101': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
          'resnet152': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
      }

      if model_name:
            convert_weight_and_push(_snake_case							,    names_to_config[model_name]							,    _snake_case							,    _snake_case				)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(_snake_case							,    _snake_case							,    _snake_case							,    _snake_case				)
      return config, expected_shape


if __name__ == "__main__":
       a							      =     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 resnet* architecture,'''
               ''' currently: resnet18,26,34,50,101,152. 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							      =     parser.parse_args()
       a							      =     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 .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

