

"""simple docstring"""


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__A		     =    logging.get_logger(__name__)





@dataclass
class 					snake_case			:
				SCREAMING_SNAKE_CASE_     :   Optional[Any]        =						42
				SCREAMING_SNAKE_CASE_     :   str        =						field(default_factory=UpperCamelCase__						)
				SCREAMING_SNAKE_CASE_     :   str        =						field(default_factory=UpperCamelCase__						)

				def 							lowercase_  (			self							:       Any  ,						UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       List[str])->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    len(list(m.modules())) == 1 or isinstance(snake_case_  ,						nn.Convad) or isinstance(snake_case_  ,						nn.BatchNormad)
						if has_not_submodules:
								if not isinstance(snake_case_  ,						snake_case_):
										self.traced.append(snake_case_)

				def __call__(			self							:       Optional[int]  ,						UpperCamelCase__							:       Dict)->     List[Any]:



						'''simple docstring'''


						for m in self.module.modules():
								self.handles.append(m.register_forward_hook(self._forward_hook))
						self.module(snake_case_)
						[x.remove() for x in self.handles]
						return self




				@property
				def 							lowercase_  (			self							:       str)->     int:



						'''simple docstring'''


						return list(filter(lambda UpperCamelCase__: len(list(x.state_dict().keys())) > 0  ,						self.traced))





@dataclass
class 					snake_case			:
				SCREAMING_SNAKE_CASE_     :   Optional[Any]        =						42
				SCREAMING_SNAKE_CASE_     :   List[str]        =						42
				SCREAMING_SNAKE_CASE_     :   Optional[Any]        =						0
				SCREAMING_SNAKE_CASE_     :   List[Any]        =						field(default_factory=UpperCamelCase__						)
				SCREAMING_SNAKE_CASE_     :   Tuple        =						field(default_factory=UpperCamelCase__						)

				def __call__(			self							:       int  ,						UpperCamelCase__							:       str)->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    Tracker(self.dest)(snake_case_).parametrized
						__lowerCAmelCase: Optional[Any]					   =    Tracker(self.src)(snake_case_).parametrized

						__lowerCAmelCase: Union[str, Any]					   =    list(filter(lambda UpperCamelCase__: type(snake_case_) not in self.src_skip  ,						snake_case_))
						__lowerCAmelCase: Dict					   =    list(filter(lambda UpperCamelCase__: type(snake_case_) not in self.dest_skip  ,						snake_case_))

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

						for dest_m, src_m in zip(snake_case_  ,						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 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			nn.Module:
		__lowerCAmelCase: str					   =    from_model.state_dict()
		__lowerCAmelCase: List[str]					   =    our_model.state_dict()
		__lowerCAmelCase: int					   =    our_model.config
		__lowerCAmelCase: Dict					   =    []
		for stage_idx in range(len(config.hidden_sizes				)				):
				for block_id in range(config.depths[stage_idx]				):
						__lowerCAmelCase: Optional[Any]					   =    F"block{stage_idx + 1}.{block_id}.layer_scale_1"
						__lowerCAmelCase: List[str]					   =    F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

						all_keys.append((from_key, to_key)				)
						__lowerCAmelCase: int					   =    F"block{stage_idx + 1}.{block_id}.layer_scale_2"
						__lowerCAmelCase: List[str]					   =    F"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

						all_keys.append((from_key, to_key)				)

		for from_key, to_key in all_keys:
				__lowerCAmelCase: List[Any]					   =    from_state_dict.pop(__a				)

		our_model.load_state_dict(__a				)
		return our_model






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = True      ,				)			->			Any:
		print(F"Downloading weights for {name}..."				)
		__lowerCAmelCase: Dict					   =    cached_download(__a				)
		print(F"Converting {name}..."				)
		__lowerCAmelCase: Dict					   =    torch.load(__a				)['''state_dict''']
		from_model.load_state_dict(__a				)
		from_model.eval()
		with torch.no_grad():
				__lowerCAmelCase: int					   =    VanForImageClassification(__a				).eval()
				__lowerCAmelCase: Tuple					   =    ModuleTransfer(src=__a      ,				dest=__a				)
				__lowerCAmelCase: Any					   =    torch.randn((1, 3, 2_2_4, 2_2_4)				)
				module_transfer(__a				)
				__lowerCAmelCase: str					   =    copy_parameters(__a      ,				__a				)

		if not torch.allclose(from_model(__a				)      ,				our_model(__a				).logits				):
				raise ValueError("The model logits don\'t match the original one."				)

		__lowerCAmelCase: Tuple					   =    name
		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
				__lowerCAmelCase: Tuple					   =    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 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = None      ,				__SCREAMING_SNAKE_CASE = True				)			->			int:
		__lowerCAmelCase: Tuple					   =    '''imagenet-1k-id2label.json'''
		__lowerCAmelCase: Tuple					   =    1_0_0_0

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

		__lowerCAmelCase: Optional[int]					   =    idalabel
		__lowerCAmelCase: Tuple					   =    {v: k for k, v in idalabel.items()}

		__lowerCAmelCase: Tuple					   =    partial(__a      ,				num_labels=__a      ,				idalabel=__a      ,				labelaid=__a				)

		__lowerCAmelCase: int					   =    {
		    '''van-tiny''': ImageNetPreTrainedConfig(
		        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]      ,				depths=[3, 3, 5, 2]      ,				mlp_ratios=[8, 8, 4, 4]      ,				),
		    '''van-small''': ImageNetPreTrainedConfig(
		        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,				depths=[2, 2, 4, 2]      ,				mlp_ratios=[8, 8, 4, 4]      ,				),
		    '''van-base''': ImageNetPreTrainedConfig(
		        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,				depths=[3, 3, 1_2, 3]      ,				mlp_ratios=[8, 8, 4, 4]      ,				),
		    '''van-large''': ImageNetPreTrainedConfig(
		        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,				depths=[3, 5, 2_7, 3]      ,				mlp_ratios=[8, 8, 4, 4]      ,				),
		}

		__lowerCAmelCase: Union[str, Any]					   =    {
		    '''van-tiny''': van_tiny,
		    '''van-small''': van_small,
		    '''van-base''': van_base,
		    '''van-large''': van_large,
		}

		__lowerCAmelCase: List[Any]					   =    {
		    '''van-tiny''': (
		        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
		    ),
		    '''van-small''': (
		        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
		    ),
		    '''van-base''': (
		        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
		    ),
		    '''van-large''': (
		        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
		    ),
		}

		if model_name:
				convert_weight_and_push(
				    __a      ,				names_to_config[model_name]      ,				checkpoint=names_to_original_checkpoints[model_name]      ,				from_model=names_to_original_models[model_name]()      ,				save_directory=__a      ,				push_to_hub=__a      ,				)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    __a      ,				__a      ,				checkpoint=names_to_original_checkpoints[model_name]      ,				from_model=names_to_original_models[model_name]()      ,				save_directory=__a      ,				push_to_hub=__a      ,				)


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: van-tiny/small/base/large. 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(
	    "--van_dir",
	    required=True,
	    type=Path,
	    help=(
	        "A path to VAN's original implementation directory. You can download from here:"
	        " https://github.com/Visual-Attention-Network/VAN-Classification"
	    ),
	)
	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)
	__A		     =    args.van_dir
	# append the path to the parents to maskformer dir
	sys.path.append(str(van_dir.parent))
	from van.models.van import van_base, van_large, van_small, van_tiny

	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    "", "", ""
		__lowerCAmelCase: Any					   =    []

		for character in message_part:
				tmp.append(character_to_number[character]				)

		for each in tmp:
				one += each[0]
				two += each[1]
				three += each[2]

		return one + two + three






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, str]:
		__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    "", ""
		__lowerCAmelCase: int					   =    []

		for character in message_part:
				this_part += character_to_number[character]

		for digit in this_part:
				tmp += digit
				if len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				):
						result.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: Union[str, Any]					   =    ""

		return result[0], result[1], result[2]






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, dict[str, str], dict[str, str]]:
		# Validate message and alphabet, set to upper and remove spaces
		__lowerCAmelCase: int					   =    alphabet.replace(" "      ,				""				).upper()
		__lowerCAmelCase: int					   =    message.replace(" "      ,				""				).upper()

		# Check length and characters
		if len(__SCREAMING_SNAKE_CASE				) != 2_7:
				raise KeyError("Length of alphabet has to be 27."				)
		for each in message:
				if each not in alphabet:
						raise ValueError("Each message character has to be included in alphabet!"				)

    # Generate dictionares
		__lowerCAmelCase: Optional[int]					   =    (
		    "111",
		    "112",
		    "113",
		    "121",
		    "122",
		    "123",
		    "131",
		    "132",
		    "133",
		    "211",
		    "212",
		    "213",
		    "221",
		    "222",
		    "223",
		    "231",
		    "232",
		    "233",
		    "311",
		    "312",
		    "313",
		    "321",
		    "322",
		    "323",
		    "331",
		    "332",
		    "333",
		)
		__lowerCAmelCase: Tuple					   =    {}
		__lowerCAmelCase: Optional[int]					   =    {}
		for letter, number in zip(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: List[Any]					   =    number
				__lowerCAmelCase: Any					   =    letter

		return message, alphabet, character_to_number, number_to_character






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase   ,				__lowerCAmelCase: str					   =    "", ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				encrypted_numeric += __encrypt_part(
				    message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				)      ,				3				):
				encrypted += number_to_character[encrypted_numeric[i : i + 3]]

		return encrypted






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Dict					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[str]					   =    []
		__lowerCAmelCase: Optional[int]					   =    ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    __decrypt_part(message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

				for j in range(0      ,				len(__SCREAMING_SNAKE_CASE				)				):
						decrypted_numeric.append(a[j] + b[j] + c[j]				)

		for each in decrypted_numeric:
				decrypted += number_to_character[each]

		return decrypted


if __name__ == "__main__":
			__A		     =    "DEFEND THE EAST WALL OF THE CASTLE."
			__A		     =    encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			__A		     =    decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')


