


"""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()
_lowerCamelCase     =  logging.get_logger()


@dataclass
class 			lowerCamelCase_		:





    """simple docstring"""







    _lowerCAmelCase      :      nn.Module
    _lowerCAmelCase      :      List[nn.Module]      							=					field(default_factory=lowercase   )
    _lowerCAmelCase      :      list      							=					field(default_factory=lowercase   )



    def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
        SCREAMING_SNAKE_CASE__     							=				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       ,		UpperCAmelCase__     ):
        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     ):
        # 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 			lowerCamelCase_		:





    """simple docstring"""







    _lowerCAmelCase      :      nn.Module
    _lowerCAmelCase      :      nn.Module
    _lowerCAmelCase      :      int      							=					0
    _lowerCAmelCase      :      List      							=					field(default_factory=lowercase   )
    _lowerCAmelCase      :      List      							=					field(default_factory=lowercase   )



    def __call__(						self       ,		UpperCAmelCase__     ):
        SCREAMING_SNAKE_CASE__     							=				Tracker(self.dest     )(UpperCAmelCase__     ).parametrized
        SCREAMING_SNAKE_CASE__     							=				Tracker(self.src     )(UpperCAmelCase__     ).parametrized

        SCREAMING_SNAKE_CASE__     							=				list(filter(lambda UpperCAmelCase__     : type(UpperCAmelCase__     ) not in self.src_skip       ,		UpperCAmelCase__     )     )
        SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      ResNetConfig						,					lowerCamelCase_  :      Path						,					lowerCamelCase_  :      bool = True  ):
    print(F'''Converting {name}...'''  )
    with torch.no_grad():
        SCREAMING_SNAKE_CASE__     							=				timm.create_model(lowerCamelCase_						,					pretrained=lowerCamelCase_  ).eval()
        SCREAMING_SNAKE_CASE__     							=				ResNetForImageClassification(lowerCamelCase_  ).eval()
        SCREAMING_SNAKE_CASE__     							=				ModuleTransfer(src=lowerCamelCase_						,					dest=lowerCamelCase_  )
        SCREAMING_SNAKE_CASE__     							=				torch.randn((1, 3, 224, 224)  )
        module_transfer(lowerCamelCase_  )

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

    SCREAMING_SNAKE_CASE__     							=				F'''resnet{"-".join(name.split("resnet"  )  )}'''
    print(lowerCamelCase_  )

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

        # we can use the convnext one
        SCREAMING_SNAKE_CASE__     							=				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=lowerCamelCase_						,					)

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


def __lowercase     (      lowerCamelCase_  :      Path						,					lowerCamelCase_  :      str = None						,					lowerCamelCase_  :      bool = True  ):
    SCREAMING_SNAKE_CASE__     							=				"imagenet-1k-id2label.json"
    SCREAMING_SNAKE_CASE__     							=				1000
    SCREAMING_SNAKE_CASE__     							=				(1, num_labels)

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

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

    SCREAMING_SNAKE_CASE__     							=				partial(lowerCamelCase_						,					num_labels=lowerCamelCase_						,					idalabel=lowerCamelCase_						,					labelaid=lowerCamelCase_  )

    SCREAMING_SNAKE_CASE__     							=				{
        "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(lowerCamelCase_						,					names_to_config[model_name]						,					lowerCamelCase_						,					lowerCamelCase_  )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )
    return config, expected_shape


if __name__ == "__main__":
 _lowerCamelCase     =  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.',
 )

 _lowerCamelCase     =  parser.parse_args()
 _lowerCamelCase     =  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"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
