
"""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 				A__ :
		A_		: nn.Module
		A_		: List[nn.Module]            =			field(default_factory=_lowerCamelCase)
		A_		: list            =			field(default_factory=_lowerCamelCase)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   len(list(m.modules()				)				) == 1 or isinstance(_SCREAMING_SNAKE_CASE ,	nn.Convad				) or isinstance(_SCREAMING_SNAKE_CASE ,	nn.BatchNormad				)
			if has_not_submodules:
				self.traced.append(_SCREAMING_SNAKE_CASE				)

		def __call__(   self ,	_SCREAMING_SNAKE_CASE				):
			for m in self.module.modules():
				self.handles.append(m.register_forward_hook(self._forward_hook				)				)
			self.module(_SCREAMING_SNAKE_CASE				)
			[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 _SCREAMING_SNAKE_CASE				: len(list(x.state_dict().keys()				)				) > 0 ,	self.traced				)				)




@dataclass
class 				A__ :
		A_		: nn.Module
		A_		: nn.Module
		A_		: int            =			0
		A_		: List            =			field(default_factory=_lowerCamelCase)
		A_		: List            =			field(default_factory=_lowerCamelCase)

		def __call__(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
			__lowerCAmelCase    :		Tuple		    =   Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

			__lowerCAmelCase    :		Dict		    =   list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.src_skip ,	_SCREAMING_SNAKE_CASE				)				)
			__lowerCAmelCase    :		str		    =   list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.dest_skip ,	_SCREAMING_SNAKE_CASE				)				)

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

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



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

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

	__lowerCAmelCase    :		List[str]		    =   F"resnet{'-'.join(name.split('resnet'	)	)}"
	print(_UpperCamelCase	)

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

		# we can use the convnext one
		__lowerCAmelCase    :		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=_UpperCamelCase ,						)

		print(F"Pushed {checkpoint_name}"	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = None ,						_UpperCamelCase = True	):
	__lowerCAmelCase    :		Tuple		    =   'imagenet-1k-id2label.json'
	__lowerCAmelCase    :		Union[str, Any]		    =   1000
	__lowerCAmelCase    :		Union[str, Any]		    =   (1, num_labels)

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

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

	__lowerCAmelCase    :		Dict		    =   partial(_UpperCamelCase ,						num_labels=_UpperCamelCase ,						idalabel=_UpperCamelCase ,						labelaid=_UpperCamelCase	)

	__lowerCAmelCase    :		Optional[int]		    =   {
	    '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(_UpperCamelCase ,						names_to_config[model_name] ,						_UpperCamelCase ,						_UpperCamelCase	)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	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"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()