


"""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()
_snake_case          =							logging.get_logger()



@dataclass
class   _a      :
    a_    :				nn.Module
    a_    :				List[nn.Module]             =    field(default_factory=SCREAMING_SNAKE_CASE_ )
    a_    :				list             =    field(default_factory=SCREAMING_SNAKE_CASE_ )
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Tensor							,					SCREAMING_SNAKE_CASE__							:					Tensor				):
          lowerCamelCase__           =		len(list(m.modules()				)				) == 1 or isinstance(SCREAMING_SNAKE_CASE__							,					nn.Convad				) or isinstance(SCREAMING_SNAKE_CASE__							,					nn.BatchNormad				)
          if has_not_submodules:
                self.traced.append(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Tensor				):
          for m in self.module.modules():
                self.handles.append(m.register_forward_hook(self._forward_hook				)				)
          self.module(SCREAMING_SNAKE_CASE__				)
          [x.remove() for x in self.handles]
          return self


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





@dataclass
class   _a      :
    a_    :				nn.Module
    a_    :				nn.Module
    a_    :				int             =    0
    a_    :				List             =    field(default_factory=SCREAMING_SNAKE_CASE_ )
    a_    :				List             =    field(default_factory=SCREAMING_SNAKE_CASE_ )
    def __call__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Tensor				):
          lowerCamelCase__           =		Tracker(self.dest				)(SCREAMING_SNAKE_CASE__				).parametrized
          lowerCamelCase__           =		Tracker(self.src				)(SCREAMING_SNAKE_CASE__				).parametrized

          lowerCamelCase__           =		list(filter(lambda SCREAMING_SNAKE_CASE__				: type(SCREAMING_SNAKE_CASE__				) not in self.src_skip							,					SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		list(filter(lambda SCREAMING_SNAKE_CASE__				: type(SCREAMING_SNAKE_CASE__				) not in self.dest_skip							,					SCREAMING_SNAKE_CASE__				)				)

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

          for dest_m, src_m in zip(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				):
                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						(				_a:     str		,   _a:     ResNetConfig		,   _a:     Path		,   _a:     bool = True							)->		str:
      '''simple docstring'''


      print(F'Converting {name}...'							)
      with torch.no_grad():
            lowerCamelCase__           =		timm.create_model(_a		,   pretrained=_a							).eval()
            lowerCamelCase__           =		ResNetForImageClassification(_a							).eval()
            lowerCamelCase__           =		ModuleTransfer(src=_a		,   dest=_a							)
            lowerCamelCase__           =		torch.randn((1, 3, 224, 224)							)
            module_transfer(_a							)

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

      lowerCamelCase__           =		F'resnet{"-".join(name.split("resnet"							)							)}'
      print(_a							)

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

            # we can use the convnext one
            lowerCamelCase__           =		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=_a		,   )

            print(F'Pushed {checkpoint_name}'							)



def   snake_case						(				_a:     Path		,   _a:     str = None		,   _a:     bool = True							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		'imagenet-1k-id2label.json'
      lowerCamelCase__           =		1000
      lowerCamelCase__           =		(1, num_labels)

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

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

      lowerCamelCase__           =		partial(_a		,   num_labels=_a		,   idalabel=_a		,   labelaid=_a							)

      lowerCamelCase__           =		{
          'resnet18': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,   hidden_sizes=[64, 128, 256, 512]		,   layer_type='basic'							),
          'resnet26': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,   hidden_sizes=[256, 512, 1024, 2048]		,   layer_type='bottleneck'							),
          'resnet34': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,   hidden_sizes=[64, 128, 256, 512]		,   layer_type='basic'							),
          'resnet50': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,   hidden_sizes=[256, 512, 1024, 2048]		,   layer_type='bottleneck'							),
          'resnet101': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]		,   hidden_sizes=[256, 512, 1024, 2048]		,   layer_type='bottleneck'							),
          'resnet152': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]		,   hidden_sizes=[256, 512, 1024, 2048]		,   layer_type='bottleneck'							),
      }

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


if __name__ == "__main__":
   _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 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.",
   )

   _snake_case          =							parser.parse_args()
   _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)







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



