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()
__lowercase										= logging.get_logger()


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    a__     :						nn.Module
    a__     :						List[nn.Module]      =				field(default_factory=UpperCAmelCase_			)
    a__     :						list      =				field(default_factory=UpperCAmelCase_			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     int:
        __UpperCamelCase					:Tuple        =		len(list(m.modules())) == 1 or isinstance(__lowercase			,			nn.Convad) or isinstance(__lowercase			,			nn.BatchNormad)
        if has_not_submodules:
            self.traced.append(__lowercase)







    def __call__(     self			,			__lowercase)	->     str:
        for m in self.module.modules():
            self.handles.append(m.register_forward_hook(self._forward_hook))
        self.module(__lowercase)
        [x.remove() for x in self.handles]
        return self







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


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    a__     :						nn.Module
    a__     :						nn.Module
    a__     :						int      =				0
    a__     :						List      =				field(default_factory=UpperCAmelCase_			)
    a__     :						List      =				field(default_factory=UpperCAmelCase_			)







    def __call__(     self			,			__lowercase)	->     Any:
        __UpperCamelCase					:List[Any]        =		Tracker(self.dest)(__lowercase).parametrized
        __UpperCamelCase					:Optional[int]        =		Tracker(self.src)(__lowercase).parametrized

        __UpperCamelCase					:str        =		list(filter(lambda __lowercase: type(__lowercase) not in self.src_skip			,			__lowercase))
        __UpperCamelCase					:Optional[int]        =		list(filter(lambda __lowercase: type(__lowercase) not in self.dest_skip			,			__lowercase))

        if len(__lowercase) != len(__lowercase):
            raise Exception(
                f"""Numbers of operations are different. Source module has {len(__lowercase)} operations while"""
                f""" destination module has {len(__lowercase)}.""")

        for dest_m, src_m in zip(__lowercase			,			__lowercase):
            dest_m.load_state_dict(src_m.state_dict())
            if self.verbose == 1:
                print(f"""Transfered from={src_m} to={dest_m}""")



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = True     ):
    '''simple docstring'''







    print(f"""Converting {name}..."""     )
    with torch.no_grad():
        __UpperCamelCase					:Any        =		timm.create_model(SCREAMING_SNAKE_CASE  ,      pretrained=SCREAMING_SNAKE_CASE     ).eval()
        __UpperCamelCase					:Optional[Any]        =		ResNetForImageClassification(SCREAMING_SNAKE_CASE     ).eval()
        __UpperCamelCase					:int        =		ModuleTransfer(src=SCREAMING_SNAKE_CASE  ,      dest=SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Dict        =		torch.randn((1, 3, 224, 224)     )
        module_transfer(SCREAMING_SNAKE_CASE     )

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

    __UpperCamelCase					:Tuple        =		f"""resnet{'-'.join(name.split('resnet'     )     )}"""
    print(SCREAMING_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=SCREAMING_SNAKE_CASE  ,      )

        # we can use the convnext one
        __UpperCamelCase					:Union[str, Any]        =		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=SCREAMING_SNAKE_CASE  ,      )

        print(f"""Pushed {checkpoint_name}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = None  ,      SCREAMING_SNAKE_CASE = True     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		'''imagenet-1k-id2label.json'''
    __UpperCamelCase					:int        =		1_000
    __UpperCamelCase					:Optional[Any]        =		(1, num_labels)

    __UpperCamelCase					:Tuple        =		'''huggingface/label-files'''
    __UpperCamelCase					:Optional[int]        =		num_labels
    __UpperCamelCase					:Tuple        =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''     )  ,      '''r'''     )     )
    __UpperCamelCase					:Dict        =		{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}

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

    __UpperCamelCase					:List[str]        =		partial(SCREAMING_SNAKE_CASE  ,      num_labels=SCREAMING_SNAKE_CASE  ,      idalabel=SCREAMING_SNAKE_CASE  ,      labelaid=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:List[str]        =		{
        '''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(SCREAMING_SNAKE_CASE  ,      names_to_config[model_name]  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    return config, expected_shape


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

    __lowercase										= parser.parse_args()
    __lowercase										= 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)






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (inductance, frequency, reactance).count(0     ) != 1:
        raise ValueError('''One and only one argument must be 0'''     )
    if inductance < 0:
        raise ValueError('''Inductance cannot be negative'''     )
    if frequency < 0:
        raise ValueError('''Frequency cannot be negative'''     )
    if reactance < 0:
        raise ValueError('''Inductive reactance cannot be negative'''     )
    if inductance == 0:
        return {"inductance": reactance / (2 * pi * frequency)}
    elif frequency == 0:
        return {"frequency": reactance / (2 * pi * inductance)}
    elif reactance == 0:
        return {"reactance": 2 * pi * frequency * inductance}
    else:
        raise ValueError('''Exactly one argument must be 0'''     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





