
'''simple docstring'''

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()
UpperCAmelCase__   : str					=		logging.get_logger()






@dataclass
class    A     :
	snake_case__      :nn.Module
	snake_case__      :List[nn.Module]        =     field(default_factory=SCREAMING_SNAKE_CASE__    )
	snake_case__      :list        =     field(default_factory=SCREAMING_SNAKE_CASE__    )
	snake_case__      :Dict[str, nn.Module]        =     field(default_factory=SCREAMING_SNAKE_CASE__    )





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       int      ,							__magic_name__			:       Tensor      ,							__magic_name__			:       Tensor      ,							__magic_name__			:       str							):


				"""simple docstring"""

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





	def __call__(					self			:       Any      ,							__magic_name__			:       Tensor							):


				"""simple docstring"""

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







	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

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






class    A     (	nn.Module    ):





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


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__								=							[]
				# - 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}"""
							lowerCAmelCase__								=							len(__magic_name__							) + 1
							feature_blocks.append((f"""res{block_index}""", v)							)

				lowerCAmelCase__								=							nn.ModuleDict(__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Tensor							):


				"""simple docstring"""

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






class    A     (	SCREAMING_SNAKE_CASE__    ):





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""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   A     (      UpperCamelCase_					:     str      )					->       Dict[str, str]:







			'''simple docstring'''



			lowerCAmelCase__								=							RegNetConfig(depths=[2, 7, 17, 1]						,	hidden_sizes=[8, 8, 8, 8]						,	groups_width=8      )
			if "in1k" in model_name:
						lowerCAmelCase__								=							RegNetForImageClassification(UpperCamelCase_      )
			else:
						lowerCAmelCase__								=							RegNetModel(UpperCamelCase_      )
			# create from model (with small weights)
			lowerCAmelCase__								=							FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=27						,	group_width=10_10						,	w_a=17_44						,	w_a=620.83						,	w_m=2.52      )      )      )

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

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

						pprint(dest_tracker.nameamodule      )
						lowerCAmelCase__								=							Tracker(UpperCamelCase_      )
						lowerCAmelCase__								=							src_tracker(UpperCamelCase_      ).parametrized

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

			lowerCAmelCase__								=							{}

			lowerCAmelCase__								=							to_params_dict(UpperCamelCase_      )
			lowerCAmelCase__								=							to_params_dict(UpperCamelCase_      )

			for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,	dst_state_dict.items()      ):
						lowerCAmelCase__								=							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:
						lowerCAmelCase__								=							"classifier.1.weight"
						lowerCAmelCase__								=							"classifier.1.bias"

			return from_to_ours_keys







def   A     (      UpperCamelCase_					:     Path						,	UpperCamelCase_					:     str = None						,	UpperCamelCase_					:     bool = True      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							"imagenet-1k-id2label.json"
			lowerCAmelCase__								=							10_00

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

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

			lowerCAmelCase__								=							partial(UpperCamelCase_						,	num_labels=UpperCamelCase_						,	idalabel=UpperCamelCase_						,	labelaid=UpperCamelCase_      )

			lowerCAmelCase__								=							{
			    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]						,	hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]						,	groups_width=10_10      ),
			    # finetuned on imagenet
			    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]						,	hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]						,	groups_width=10_10      ),
			}

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

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

			lowerCAmelCase__								=							get_from_to_our_keys(UpperCamelCase_      )

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

						lowerCAmelCase__								=							{}

						lowerCAmelCase__								=							list(from_state_dict.keys()      )
						lowerCAmelCase__								=							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
									lowerCAmelCase__								=							re.sub(UpperCamelCase_						,	""						,	UpperCamelCase_      )
									# 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
									lowerCAmelCase__								=							from_to_ours_keys[src_key]
									# store the parameter with our key
									lowerCAmelCase__								=							from_state_dict[key]
									not_used_keys.remove(UpperCamelCase_      )
						# check that all keys have been updated
						assert len(UpperCamelCase_      ) == 0, F"""Some keys where not used {",".join(UpperCamelCase_      )}"""

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

						# save our state dict to disk
						torch.save(UpperCamelCase_						,	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
						lowerCAmelCase__								=							names_to_config[model_name]
						lowerCAmelCase__								=							RegNetModel
						if "in1k" in model_name:
									lowerCAmelCase__								=							RegNetForImageClassification
						lowerCAmelCase__								=							our_model_func(UpperCamelCase_      )
						# 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
						lowerCAmelCase__								=							our_model.state_dict().keys()
						PreTrainedModel._load_pretrained_model_low_mem(
						    UpperCamelCase_						,	UpperCamelCase_						,	[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						,	)
						lowerCAmelCase__								=							3_84
						# we can use the convnext one
						lowerCAmelCase__								=							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						,	size=UpperCamelCase_      )
						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__":
		UpperCAmelCase__   : Any					=		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.",
		)

		UpperCAmelCase__   : str					=		parser.parse_args()

		UpperCAmelCase__   : 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'''

def   A     (      UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			return len_board * len_board_column * row + column







def   A     (      UpperCamelCase_					:     list[list[str]]						,	UpperCamelCase_					:     str						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     set[int]						,	)					->       bool:







			'''simple docstring'''



			if board[row][column] != word[word_index]:
						return False

			if word_index == len(UpperCamelCase_      ) - 1:
						return True

			lowerCAmelCase__								=							[(0, 1), (0, -1), (-1, 0), (1, 0)]
			lowerCAmelCase__								=							len(UpperCamelCase_      )
			lowerCAmelCase__								=							len(board[0]      )
			for direction in traverts_directions:
						lowerCAmelCase__								=							row + direction[0]
						lowerCAmelCase__								=							column + direction[1]
						if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
									continue

						lowerCAmelCase__								=							get_point_key(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )
						if key in visited_points_set:
									continue

						visited_points_set.add(UpperCamelCase_      )
						if exits_word(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	word_index + 1						,	UpperCamelCase_      ):
									return True

						visited_points_set.remove(UpperCamelCase_      )

			return False







def   A     (      UpperCamelCase_					:     list[list[str]]						,	UpperCamelCase_					:     str      )					->       bool:







			'''simple docstring'''



			lowerCAmelCase__								=							(
			    "The board should be a non empty matrix of single chars strings."
			)

			lowerCAmelCase__								=							len(UpperCamelCase_      )
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
						raise ValueError(UpperCamelCase_      )

			for row in board:
						if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
									raise ValueError(UpperCamelCase_      )

						for item in row:
									if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) != 1:
												raise ValueError(UpperCamelCase_      )

    # Validate word
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
						raise ValueError(
						    "The word parameter should be a string of length greater than 0."      )

			lowerCAmelCase__								=							len(board[0]      )
			for i in range(UpperCamelCase_      ):
						for j in range(UpperCamelCase_      ):
									if exits_word(
									    UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	0						,	{get_point_key(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )}      ):
												return True

			return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()





