
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 	_lowerCAmelCase					:
				_lowercase					=42
				_lowercase					=field(default_factory=a__       )
				_lowercase					=field(default_factory=a__       )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	len(list(m.modules()     )     ) == 1 or isinstance(lowerCAmelCase__				,				nn.Convad     ) or isinstance(lowerCAmelCase__				,				nn.BatchNormad     )
							if has_not_submodules:
										self.traced.append(lowerCAmelCase__     )




				def __call__(					self				,				_UpperCamelCase     )				->      Optional[Any]:
							for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook     )     )
							self.module(lowerCAmelCase__     )
							[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 	_lowerCAmelCase					:
				_lowercase					=42
				_lowercase					=42
				_lowercase					=0
				_lowercase					=field(default_factory=a__       )
				_lowercase					=field(default_factory=a__       )




				def __call__(					self				,				_UpperCamelCase     )				->      List[Any]:
							lowerCAmelCase_						=	Tracker(self.dest     )(lowerCAmelCase__     ).parametrized
							lowerCAmelCase_						=	Tracker(self.src     )(lowerCAmelCase__     ).parametrized

							lowerCAmelCase_						=	list(filter(lambda _UpperCamelCase     : type(lowerCAmelCase__     ) not in self.src_skip				,				lowerCAmelCase__     )     )
							lowerCAmelCase_						=	list(filter(lambda _UpperCamelCase     : type(lowerCAmelCase__     ) not in self.dest_skip				,				lowerCAmelCase__     )     )

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

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  ResNetConfig    ,							__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  bool = True				):




			"""simple docstring"""


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

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

			lowerCAmelCase_						=	F"""resnet{'-'.join(name.split('resnet'				)				)}"""
			print(_lowercase				)

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

						# 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=_lowercase    ,							)

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  str = None    ,							__lowerCAmelCase  :  bool = True				):




			"""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(_lowercase    ,							_lowercase    ,							repo_type="dataset"				)    ,							"r"				)				)
			lowerCAmelCase_						=	{int(_lowercase				): v for k, v in idalabel.items()}

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

			lowerCAmelCase_						=	partial(_lowercase    ,							num_labels=_lowercase    ,							idalabel=_lowercase    ,							labelaid=_lowercase				)

			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(_lowercase    ,							names_to_config[model_name]    ,							_lowercase    ,							_lowercase				)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(_lowercase    ,							_lowercase    ,							_lowercase    ,							_lowercase				)
			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 __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(_lowercase    ,       _lowercase					)					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->list[list[list[float] | float]]:






	'''simple docstring'''






	if dataset.ndim != value_array.ndim:
		a      :		str	=				(
		    "Wrong input data's dimensions... "
		    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
		)
		raise ValueError(_lowercase					)

	try:
		if dataset.shape[1] != value_array.shape[1]:
			a      :		int	=				(
			    "Wrong input data's shape... "
			    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
			)
			raise ValueError(_lowercase					)
	except IndexError:
		if dataset.ndim != value_array.ndim:
			raise TypeError("Wrong shape"					)

	if dataset.dtype != value_array.dtype:
		a      :		Union[str, Any]	=				(
		    "Input data have different datatype... "
		    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
		)
		raise TypeError(_lowercase					)

	a      :		str	=				[]

	for value in value_array:
		a      :		List[Any]	=				euclidean(_lowercase    ,       dataset[0]					)
		a      :		str	=				dataset[0].tolist()

		for dataset_value in dataset[1:]:
			a      :		Tuple	=				euclidean(_lowercase    ,       _lowercase					)

			if dist > temp_dist:
				a      :		Dict	=				temp_dist
				a      :		Optional[int]	=				dataset_value.tolist()

		answer.append([vector, dist]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return np.dot(_lowercase    ,       _lowercase					) / (norm(_lowercase					) * norm(_lowercase					))


if __name__ == "__main__":
		import doctest

		doctest.testmod()


