




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_     :    Union[str, Any]      =			logging.get_logger()






@dataclass
class    __lowerCAmelCase			:
			snake_case_				:			nn.Module
			snake_case_				:			List[nn.Module]								=					field(default_factory=UpperCAmelCase__			)
			snake_case_				:			list								=					field(default_factory=UpperCAmelCase__			)
			snake_case_				:			Dict[str, nn.Module]								=					field(default_factory=UpperCAmelCase__			)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Tensor   ,   snake_case__   :      Tensor   ,   snake_case__   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					len(list(m.modules()				)				) == 1 or isinstance(snake_case__   ,   nn.Convad				) or isinstance(snake_case__   ,   nn.BatchNormad				)
									if has_not_submodules:
															self.traced.append(snake_case__				)
															_UpperCAmelCase													=					m







			def __call__(	self   :      str   ,   snake_case__   :      Tensor				):




									"""simple docstring"""


									for name, m in self.module.named_modules():
															self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,   name=snake_case__				)				)				)
									self.module(snake_case__				)
									[x.remove() for x in self.handles]
									return self







			@property
			def UpperCamelCase      (	self   :      List[Any]				):




									"""simple docstring"""


									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()				)				) > 0}






class    __lowerCAmelCase			(	nn.Module			):







			def __init__(	self   :      int   ,   snake_case__   :      nn.Module				):




									"""simple docstring"""


									super().__init__()

									_UpperCAmelCase													=					[]
									# - get the stem
									feature_blocks.append(("conv1", model.stem)				)
									# - get all the feature blocks
									for k, v in model.trunk_output.named_children():
															assert k.startswith("block"				), F"""Unexpected layer name {k}"""
															_UpperCAmelCase													=					len(snake_case__				) + 1
															feature_blocks.append((F"""res{block_index}""", v)				)

									_UpperCAmelCase													=					nn.ModuleDict(snake_case__				)







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      Tensor				):




									"""simple docstring"""


									return get_trunk_forward_outputs(
									    snake_case__   ,   out_feat_keys=snake_case__   ,   feature_blocks=self._feature_blocks   ,   )






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					RegNetConfig(depths=[2, 7, 17, 1]     ,   hidden_sizes=[8, 8, 8, 8]     ,   groups_width=8						)
						if "in1k" in model_name:
												_UpperCAmelCase													=					RegNetForImageClassification(snake_case_						)
						else:
												_UpperCAmelCase													=					RegNetModel(snake_case_						)
						# create from model (with small weights)
						_UpperCAmelCase													=					FakeRegNetVisslWrapper(
						    RegNet(FakeRegNetParams(depth=27     ,   group_width=1010     ,   w_a=1744     ,   w_a=620.83     ,   w_m=2.52						)						)						)

						with torch.no_grad():
												_UpperCAmelCase													=					from_model.eval()
												_UpperCAmelCase													=					our_model.eval()

												_UpperCAmelCase													=					torch.randn((1, 3, 32, 32)						)
												# trace both
												_UpperCAmelCase													=					Tracker(snake_case_						)
												_UpperCAmelCase													=					dest_tracker(snake_case_						).parametrized

												pprint(dest_tracker.nameamodule						)
												_UpperCAmelCase													=					Tracker(snake_case_						)
												_UpperCAmelCase													=					src_tracker(snake_case_						).parametrized

						# convert the keys -> module dict to keys -> params
						def to_params_dict(snake_case_						):
												_UpperCAmelCase													=					OrderedDict()
												for name, module in dict_with_modules.items():
																		for param_name, param in module.state_dict().items():
																								_UpperCAmelCase													=					param
												return params_dict

						_UpperCAmelCase													=					{}

						_UpperCAmelCase													=					to_params_dict(snake_case_						)
						_UpperCAmelCase													=					to_params_dict(snake_case_						)

						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()     ,   dst_state_dict.items()						):
												_UpperCAmelCase													=					dest_key
												logger.info(f"""{src_key} -> {dest_key}"""						)
						# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
						if "in1k" in model_name:
												_UpperCAmelCase													=					"classifier.1.weight"
												_UpperCAmelCase													=					"classifier.1.bias"

						return from_to_ours_keys


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_ = None     ,   snake_case_ = True						):

						'''simple docstring'''


						_UpperCAmelCase													=					"imagenet-1k-id2label.json"
						_UpperCAmelCase													=					1000

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

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

						_UpperCAmelCase													=					partial(snake_case_     ,   num_labels=snake_case_     ,   idalabel=snake_case_     ,   labelaid=snake_case_						)

						_UpperCAmelCase													=					{
						    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]     ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]     ,   groups_width=1010						),
						    # finetuned on imagenet
						    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]     ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]     ,   groups_width=1010						),
						}

						# add seer weights logic
						def load_using_classy_vision(snake_case_						) -> Tuple[Dict, Dict]:
												_UpperCAmelCase													=					torch.hub.load_state_dict_from_url(snake_case_     ,   model_dir=str(snake_case_						)     ,   map_location="cpu"						)
												# check if we have a head, if yes add it
												_UpperCAmelCase													=					files["classy_state_dict"]["base_model"]["model"]
												return model_state_dict["trunk"], model_state_dict["heads"]

						_UpperCAmelCase													=					{
						    "regnet-y-10b-seer": partial(
						        snake_case_     ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"     ,   ),
						    "regnet-y-10b-seer-in1k": partial(
						        snake_case_     ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"     ,   ),
						}

						_UpperCAmelCase													=					get_from_to_our_keys(snake_case_						)

						if not (save_directory / f"""{model_name}.pth""").exists():
												logger.info("Loading original state_dict."						)
												_UpperCAmelCase							,			_UpperCAmelCase													=					names_to_from_model[model_name]()
												_UpperCAmelCase													=					from_state_dict_trunk
												if "in1k" in model_name:
																		# add the head
																		_UpperCAmelCase													=					{**from_state_dict_trunk, **from_state_dict_head}
												logger.info("Done!"						)

												_UpperCAmelCase													=					{}

												_UpperCAmelCase													=					list(from_state_dict.keys()						)
												_UpperCAmelCase													=					R"\.block.-part."
												# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
												for key in from_state_dict.keys():
																		# remove the weird "block[0,1]-part" from the key
																		_UpperCAmelCase													=					re.sub(snake_case_     ,   ""     ,   snake_case_						)
																		# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																		_UpperCAmelCase													=					from_to_ours_keys[src_key]
																		# store the parameter with our key
																		_UpperCAmelCase													=					from_state_dict[key]
																		not_used_keys.remove(snake_case_						)
												# check that all keys have been updated
												assert len(snake_case_						) == 0, f"""Some keys where not used {",".join(snake_case_						)}"""

												logger.info(f"""The following keys were not used: {",".join(snake_case_						)}"""						)

												# save our state dict to disk
												torch.save(snake_case_     ,   save_directory / f"""{model_name}.pth"""						)

												del converted_state_dict
						else:
												logger.info("The state_dict was already stored on disk."						)
						if push_to_hub:
												logger.info(f"""Token is {os.environ["HF_TOKEN"]}"""						)
												logger.info("Loading our model."						)
												# create our model
												_UpperCAmelCase													=					names_to_config[model_name]
												_UpperCAmelCase													=					RegNetModel
												if "in1k" in model_name:
																		_UpperCAmelCase													=					RegNetForImageClassification
												_UpperCAmelCase													=					our_model_func(snake_case_						)
												# place our model to the meta device (so remove all the weights)
												our_model.to(torch.device("meta"						)						)
												logger.info("Loading state_dict in our model."						)
												# load state dict
												_UpperCAmelCase													=					our_model.state_dict().keys()
												PreTrainedModel._load_pretrained_model_low_mem(
												    snake_case_     ,   snake_case_     ,   [save_directory / f"""{model_name}.pth"""]						)
												logger.info("Finally, pushing!"						)
												# push it to hub
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / model_name     ,   commit_message="Add model"     ,   output_dir=save_directory / model_name     ,   )
												_UpperCAmelCase													=					384
												# we can use the convnext one
												_UpperCAmelCase													=					AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"     ,   size=snake_case_						)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / model_name     ,   commit_message="Add image processor"     ,   output_dir=save_directory / model_name     ,   )


if __name__ == "__main__":
				lowercase_     :    List[str]      =			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 regnet* architecture,'
				        ' currently: regnetx-*, regnety-*. 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_     :    Any      =			parser.parse_args()

				lowercase_     :    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)










from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			@abstractmethod
			def __lt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __gt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __eq__(	self   :      T   ,   snake_case__   :      object				):




									"""simple docstring"""


									pass




lowercase_     :    Tuple      =			TypeVar('T', bound=Comparable)






class    __lowerCAmelCase			(	Generic[T]			):







			def __init__(	self   :      Optional[int]				):




									"""simple docstring"""


									_UpperCAmelCase													=					[]
									_UpperCAmelCase													=					0







			def __repr__(	self   :      Dict				):




									"""simple docstring"""


									return str(self.h				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									if child_idx > 0:
															return (child_idx - 1) // 2
									return None







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 1
									if left_child_index < self.heap_size:
															return left_child_index
									return None







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 2
									if right_child_index < self.heap_size:
															return right_child_index
									return None







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      int				):




									"""simple docstring"""


									if index < self.heap_size:
															_UpperCAmelCase													=					index
															_UpperCAmelCase													=					self.left_child_idx(snake_case__				)
															_UpperCAmelCase													=					self.right_child_idx(snake_case__				)
															# check which child is larger than its parent
															if left_child is not None and self.h[left_child] > self.h[violation]:
																					_UpperCAmelCase													=					left_child
															if right_child is not None and self.h[right_child] > self.h[violation]:
																					_UpperCAmelCase													=					right_child
															# if violation indeed exists
															if violation != index:
																					# swap to fix the violation
																					_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[index], self.h[violation]
																					# fix the subsequent violation recursively if any
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Iterable[T]				):




									"""simple docstring"""


									_UpperCAmelCase													=					list(snake_case__				)
									_UpperCAmelCase													=					len(self.h				)
									if self.heap_size > 1:
															# max_heapify from right to left but exclude leaves (last level)
															for i in range(self.heap_size // 2 - 1   ,   -1   ,   -1				):
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									if self.heap_size >= 2:
															_UpperCAmelCase													=					self.h[0]
															_UpperCAmelCase													=					self.h.pop(-1				)
															self.heap_size -= 1
															self.max_heapify(0				)
															return me
									elif self.heap_size == 1:
															self.heap_size -= 1
															return self.h.pop(-1				)
									else:
															raise Exception("Empty heap"				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      T				):




									"""simple docstring"""


									self.h.append(snake_case__				)
									_UpperCAmelCase													=					(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
															self.max_heapify(snake_case__				)
															_UpperCAmelCase													=					(idx - 1) // 2







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.heap_size
									for j in range(size - 1   ,   0   ,   -1				):
															_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[j], self.h[0]
															self.heap_size -= 1
															self.max_heapify(0				)
									_UpperCAmelCase													=					size


if __name__ == "__main__":
				import doctest

				# run doc test
				doctest.testmod()

				# demo
				for unsorted in [
				    [0],
				    [2],
				    [3, 5],
				    [5, 3],
				    [5, 5],
				    [0, 0, 0, 0],
				    [1, 1, 1, 1],
				    [2, 2, 3, 5],
				    [0, 2, 2, 3, 5],
				    [2, 5, 3, 0, 2, 3, 0, 3],
				    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
				    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
				    [-45, -2, -5],
				]:
								print(f"""unsorted array: {unsorted}""")

								lowercase_     :    Heap[int]      =			Heap()
								heap.build_max_heap(unsorted)
								print(f"""after build heap: {heap}""")

								print(f"""max value: {heap.extract_max()}""")
								print(f"""after max value removed: {heap}""")

								heap.insert(1_00)
								print(f"""after new value 100 inserted: {heap}""")

								heap.heap_sort()
								print(f"""heap-sorted array: {heap}\n""")




