






'''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()
A:      str  				=				logging.get_logger()






@dataclass
class 							__magic_name__     :







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      nn.Module
	SCREAMING_SNAKE_CASE_					:      List[nn.Module]        =  field(default_factory=UpperCAmelCase_				)
	SCREAMING_SNAKE_CASE_					:      list        =  field(default_factory=UpperCAmelCase_				)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Dict:
							lowercase_      :   Union[str, Any]        =			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        lowerCamelCase__		(							self       )						->  Any:
							# 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 							__magic_name__     :







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      nn.Module
	SCREAMING_SNAKE_CASE_					:      nn.Module
	SCREAMING_SNAKE_CASE_					:      int        =  0
	SCREAMING_SNAKE_CASE_					:      List        =  field(default_factory=UpperCAmelCase_				)
	SCREAMING_SNAKE_CASE_					:      List        =  field(default_factory=UpperCAmelCase_				)
	def __call__(							self  ,      _lowercase       )						->  Any:
							lowercase_      :   List[str]        =			Tracker(self.dest       )(_lowercase       ).parametrized
							lowercase_      :   Any        =			Tracker(self.src       )(_lowercase       ).parametrized

							lowercase_      :   Optional[Any]        =			list(filter(lambda _lowercase       : type(_lowercase       ) not in self.src_skip  ,      _lowercase       )       )
							lowercase_      :   Tuple        =			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 	_UpperCAmelCase    (  a		:	str						,			a		:	ResNetConfig						,			a		:	Path						,			a		:	bool = True							)			->			Any:





						"""simple docstring"""
						print(f"Converting {name}..."							)
						with torch.no_grad():
												lowercase_      :   List[str]        =			timm.create_model(a						,			pretrained=a							).eval()
												lowercase_      :   List[str]        =			ResNetForImageClassification(a							).eval()
												lowercase_      :   Tuple        =			ModuleTransfer(src=a						,			dest=a							)
												lowercase_      :   Optional[int]        =			torch.randn((1, 3, 2_2_4, 2_2_4)							)
												module_transfer(a							)

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

						lowercase_      :   Optional[Any]        =			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
												lowercase_      :   Dict        =			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 	_UpperCAmelCase    (  a		:	Path						,			a		:	str = None						,			a		:	bool = True							)			->			Any:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			'imagenet-1k-id2label.json'
						lowercase_      :   Union[str, Any]        =			1_0_0_0
						lowercase_      :   str        =			(1, num_labels)

						lowercase_      :   List[str]        =			'huggingface/label-files'
						lowercase_      :   Optional[Any]        =			num_labels
						lowercase_      :   Optional[Any]        =			json.load(open(hf_hub_download(a						,			a						,			repo_type='dataset'							)						,			'r'							)							)
						lowercase_      :   List[str]        =			{int(a							): v for k, v in idalabel.items()}

						lowercase_      :   Any        =			idalabel
						lowercase_      :   int        =			{v: k for k, v in idalabel.items()}

						lowercase_      :   List[str]        =			partial(a						,			num_labels=a						,			idalabel=a						,			labelaid=a							)

						lowercase_      :   List[str]        =			{
						    'resnet18': ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]						,			hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,			layer_type='basic'							),
						    'resnet26': ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]						,			hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,			layer_type='bottleneck'							),
						    'resnet34': ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]						,			hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,			layer_type='basic'							),
						    'resnet50': ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]						,			hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,			layer_type='bottleneck'							),
						    'resnet101': ImageNetPreTrainedConfig(
						        depths=[3, 4, 2_3, 3]						,			hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,			layer_type='bottleneck'							),
						    'resnet152': ImageNetPreTrainedConfig(
						        depths=[3, 8, 3_6, 3]						,			hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,			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__":
						A:      Tuple  				=				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:      Union[str, Any]  				=				parser.parse_args()
						A:      Path  				=				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'''

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
A:      Tuple  				=				logging.getLogger(__name__)






def 	_UpperCAmelCase    (  a		:	str							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			git.Repo(search_parent_directories=a							)
						lowercase_      :   Union[str, Any]        =			{
						    'repo_id': str(a							),
						    'repo_sha': str(repo.head.object.hexsha							),
						    'repo_branch': str(repo.active_branch							),
						}

						with open(os.path.join(a						,			'git_log.json'							)						,			'w'							) as f:
												json.dump(a						,			a						,			indent=4							)






def 	_UpperCAmelCase    (  a		:	str							)			->			Union[str, Any]:





						"""simple docstring"""
						if params.n_gpu <= 0:
												lowercase_      :   int        =			0
												lowercase_      :   Union[str, Any]        =			-1
												lowercase_      :   List[str]        =			True
												lowercase_      :   Optional[Any]        =			False
												return

						assert torch.cuda.is_available()

						logger.info('Initializing GPUs'							)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase_      :   Dict        =			int(os.environ['WORLD_SIZE']							)
												lowercase_      :   Union[str, Any]        =			int(os.environ['N_GPU_NODE']							)
												lowercase_      :   Optional[int]        =			int(os.environ['RANK']							)

												# number of nodes / node ID
												lowercase_      :   int        =			params.world_size // params.n_gpu_per_node
												lowercase_      :   str        =			params.global_rank // params.n_gpu_per_node
												lowercase_      :   Dict        =			True

												assert params.n_nodes == int(os.environ['N_NODES']							)
												assert params.node_id == int(os.environ['NODE_RANK']							)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase_      :   str        =			1
												lowercase_      :   Dict        =			0
												lowercase_      :   Tuple        =			0
												lowercase_      :   List[Any]        =			0
												lowercase_      :   int        =			1
												lowercase_      :   Tuple        =			1
												lowercase_      :   str        =			False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase_      :   List[str]        =			params.node_id == 0 and params.local_rank == 0
						lowercase_      :   Optional[Any]        =			params.n_nodes > 1

						# summary
						lowercase_      :   int        =			f"--- Global rank: {params.global_rank} - "
						logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes							)
						logger.info(PREFIX + 'Node ID        : %i' % params.node_id							)
						logger.info(PREFIX + 'Local rank     : %i' % params.local_rank							)
						logger.info(PREFIX + 'World size     : %i' % params.world_size							)
						logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node							)
						logger.info(PREFIX + 'Master         : %s' % str(params.is_master							)							)
						logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node							)							)
						logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu							)							)
						logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()							)

						# set GPU device
						torch.cuda.set_device(params.local_rank							)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info('Initializing PyTorch distributed'							)
												torch.distributed.init_process_group(
												    init_method='env://'						,			backend='nccl'						,			)






def 	_UpperCAmelCase    (  a		:	Dict							)			->			Optional[int]:





						"""simple docstring"""
						np.random.seed(args.seed							)
						torch.manual_seed(args.seed							)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed							)
