



'''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()
SCREAMING_SNAKE_CASE__      					=     logging.get_logger()


@dataclass
class 				snake_case :
       lowerCAmelCase__  :nn.Module
       lowerCAmelCase__  :List[nn.Module]						   =					field(default_factory=UpperCamelCase   )
       lowerCAmelCase__  :list						   =					field(default_factory=UpperCamelCase   )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						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_      )  ->							List[str]:
           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 							_a      (				self      )  ->							str:
           # 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 				snake_case :
       lowerCAmelCase__  :nn.Module
       lowerCAmelCase__  :nn.Module
       lowerCAmelCase__  :int						   =					0
       lowerCAmelCase__  :List						   =					field(default_factory=UpperCamelCase   )
       lowerCAmelCase__  :List						   =					field(default_factory=UpperCamelCase   )



       def __call__(				self     ,UpperCAmelCase_      )  ->							Union[str, Any]:
           lowercase__		       =						Tracker(self.dest      )(UpperCAmelCase_      ).parametrized
           lowercase__		       =						Tracker(self.src      )(UpperCAmelCase_      ).parametrized

           lowercase__		       =						list(filter(lambda UpperCAmelCase_      : type(UpperCAmelCase_      ) not in self.src_skip     ,UpperCAmelCase_      )      )
           lowercase__		       =						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        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						ResNetConfig     ,_snake_case  :						Path     ,_snake_case  :						bool = True       ):





    '''simple docstring'''


    print(f'''Converting {name}...'''       )
    with torch.no_grad():
        lowercase__		       =						timm.create_model(_snake_case     ,pretrained=_snake_case       ).eval()
        lowercase__		       =						ResNetForImageClassification(_snake_case       ).eval()
        lowercase__		       =						ModuleTransfer(src=_snake_case     ,dest=_snake_case       )
        lowercase__		       =						torch.randn((1, 3, 224, 224)       )
        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."

    lowercase__		       =						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
        lowercase__		       =						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        lowerCamelCase  (				_snake_case  :						Path     ,_snake_case  :						str = None     ,_snake_case  :						bool = True       ):





    '''simple docstring'''


    lowercase__		       =						"imagenet-1k-id2label.json"
    lowercase__		       =						1_000
    lowercase__		       =						(1, num_labels)

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

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

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

    lowercase__		       =						{
        "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, 1_024, 2_048]     ,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, 1_024, 2_048]     ,layer_type="bottleneck"       ),
        "resnet101": ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]     ,hidden_sizes=[256, 512, 1_024, 2_048]     ,layer_type="bottleneck"       ),
        "resnet152": ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]     ,hidden_sizes=[256, 512, 1_024, 2_048]     ,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__":
 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 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.",
 )

 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)









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int = 4_000_000       ):





    '''simple docstring'''


    lowercase__		       =						[0, 1]
    lowercase__		       =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]       )
        if fib[i + 2] > n:
            break
        i += 1
    lowercase__		       =						0
    for j in range(len(_snake_case       ) - 1       ):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(f'''{solution() = }''')




