
from __future__ import annotations

SCREAMING_SNAKE_CASE				:List[Any]									=							tuple[int, int, int]
SCREAMING_SNAKE_CASE				:Tuple									=							tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE				:Optional[int]									=							"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
SCREAMING_SNAKE_CASE				:List[Any]									=							"EGZWVONAHDCLFQMSIPJBYUKXTR"
SCREAMING_SNAKE_CASE				:int									=							"FOBHMDKEXQNRAULPGSJVTYICZW"
SCREAMING_SNAKE_CASE				:Any									=							"ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
SCREAMING_SNAKE_CASE				:Tuple									=							{
    "A": "N",
    "N": "A",
    "B": "O",
    "O": "B",
    "C": "P",
    "P": "C",
    "D": "Q",
    "Q": "D",
    "E": "R",
    "R": "E",
    "F": "S",
    "S": "F",
    "G": "T",
    "T": "G",
    "H": "U",
    "U": "H",
    "I": "V",
    "V": "I",
    "J": "W",
    "W": "J",
    "K": "X",
    "X": "K",
    "L": "Y",
    "Y": "L",
    "M": "Z",
    "Z": "M",
}

# -------------------------- extra rotors --------------------------
SCREAMING_SNAKE_CASE				:List[Any]									=							"RMDJXFUWGISLHVTCQNKYPBEZOA"
SCREAMING_SNAKE_CASE				:Dict									=							"SGLCPQWZHKXAREONTFBVIYJUDM"
SCREAMING_SNAKE_CASE				:Dict									=							"HVSICLTYKQUBXDWAJZOMFGPREN"
SCREAMING_SNAKE_CASE				:Union[str, Any]									=							"RZWQHFMVDBKICJLNTUXAGYPSOE"
SCREAMING_SNAKE_CASE				:int									=							"LFKIJODBEGAMQPXVUHYSTCZRWN"
SCREAMING_SNAKE_CASE				:List[Any]									=							"KOAEGVDHXPQZMLFTYWJNBRCIUS"


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:







				"""simple docstring"""



				if (unique_rotsel := len(set(lowerCamelCase_		)		)) < 3:
								__A       =					F'''Please use 3 unique rotors (not {unique_rotsel})'''
								raise Exception(lowerCamelCase_		)

				# Checks if rotor positions are valid
				__A       =					rotpos
				if not 0 < rotorposa <= len(lowerCamelCase_		):
								__A       =					F'''First rotor position is not within range of 1..26 ({rotorposa}'''
								raise ValueError(lowerCamelCase_		)
				if not 0 < rotorposa <= len(lowerCamelCase_		):
								__A       =					F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
								raise ValueError(lowerCamelCase_		)
				if not 0 < rotorposa <= len(lowerCamelCase_		):
								__A       =					F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
								raise ValueError(lowerCamelCase_		)

				# Validates string and returns dict
				__A       =					_plugboard(lowerCamelCase_		)

				return rotpos, rotsel, pbdict


def 		UpperCAmelCase     (      a_		)						->      dict[str, str]:







				"""simple docstring"""



				if not isinstance(lowerCamelCase_						,  lowerCamelCase_		):
								__A       =					F'''Plugboard setting isn\'t type string ({type(lowerCamelCase_		)})'''
								raise TypeError(lowerCamelCase_		)
				elif len(lowerCamelCase_		) % 2 != 0:
								__A       =					F'''Odd number of symbols ({len(lowerCamelCase_		)})'''
								raise Exception(lowerCamelCase_		)
				elif pbstring == "":
								return {}

				pbstring.replace(" "						,  ""		)

				# Checks if all characters are unique
				__A       =					set()
				for i in pbstring:
								if i not in abc:
												__A       =					F'''\'{i}\' not in list of symbols'''
												raise Exception(lowerCamelCase_		)
								elif i in tmppbl:
												__A       =					F'''Duplicate symbol ({i})'''
												raise Exception(lowerCamelCase_		)
								else:
												tmppbl.add(lowerCamelCase_		)
				del tmppbl

				# Created the dictionary
				__A       =					{}
				for j in range(0						,  len(lowerCamelCase_		) - 1						,  2		):
								__A       =					pbstring[j + 1]
								__A       =					pbstring[j]

				return pb


def 		UpperCAmelCase     (      a_						,  a_						,  a_ = (rotora, rotora, rotora)						,  a_ = ""						,  )						->      str:







				"""simple docstring"""



				__A       =					text.upper()
				__A       =					_validator(
				    lowerCamelCase_						,  lowerCamelCase_						,  plugb.upper()		)

				__A       =					rotor_position
				__A       =					rotor_selection
				rotorposa -= 1
				rotorposa -= 1
				rotorposa -= 1

				__A       =					[]

				# encryption/decryption process --------------------------
				for symbol in text:
								if symbol in abc:
												# 1st plugboard --------------------------
												if symbol in plugboard:
																__A       =					plugboard[symbol]

												# rotor ra --------------------------
												__A       =					abc.index(lowerCamelCase_		) + rotorposa
												__A       =					rotora[index % len(lowerCamelCase_		)]

												# rotor rb --------------------------
												__A       =					abc.index(lowerCamelCase_		) + rotorposa
												__A       =					rotora[index % len(lowerCamelCase_		)]

												# rotor rc --------------------------
												__A       =					abc.index(lowerCamelCase_		) + rotorposa
												__A       =					rotora[index % len(lowerCamelCase_		)]

												# reflector --------------------------
												# this is the reason you don't need another machine to decipher

												__A       =					reflector[symbol]

												# 2nd rotors
												__A       =					abc[rotora.index(lowerCamelCase_		) - rotorposa]
												__A       =					abc[rotora.index(lowerCamelCase_		) - rotorposa]
												__A       =					abc[rotora.index(lowerCamelCase_		) - rotorposa]

												# 2nd plugboard
												if symbol in plugboard:
																__A       =					plugboard[symbol]

												# moves/resets rotor positions
												rotorposa += 1
												if rotorposa >= len(lowerCamelCase_		):
																__A       =					0
																rotorposa += 1
												if rotorposa >= len(lowerCamelCase_		):
																__A       =					0
																rotorposa += 1
												if rotorposa >= len(lowerCamelCase_		):
																__A       =					0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
								result.append(lowerCamelCase_		)

				return "".join(lowerCamelCase_		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:Tuple									=							"This is my Python script that emulates the Enigma machine from WWII."
					SCREAMING_SNAKE_CASE				:Union[str, Any]									=							(1, 1, 1)
					SCREAMING_SNAKE_CASE				:List[str]									=							"pictures"
					SCREAMING_SNAKE_CASE				:Any									=							(rotora, rotora, rotora)
					SCREAMING_SNAKE_CASE				:Tuple									=							enigma(message, rotor_pos, rotor_sel, pb)

					print('Encrypted message:', en)
					print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    _lowercase			:				Tuple 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Any 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Dict 						=						True if 'large' in model_name or 'huge' in model_name else False

    if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
        if "fl3" in model_name:
            _lowercase			:				Any 						=						[3, 3, 3, 3]
            _lowercase			:				Any 						=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowercase			:				Dict 						=						[4, 4, 4, 4]
            _lowercase			:				Tuple 						=						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowercase			:				str 						=						[3, 3, 3, 3]
        if "lrf" in model_name:
            _lowercase			:				Optional[int] 						=						[3, 3, 3, 3]
        else:
            _lowercase			:				Dict 						=						[2, 2, 2, 2]

    if "tiny" in model_name:
        _lowercase			:				List[str] 						=						96
    elif "small" in model_name:
        _lowercase			:				Dict 						=						96
    elif "base" in model_name:
        _lowercase			:				Optional[int] 						=						128
    elif "large" in model_name:
        _lowercase			:				List[Any] 						=						192
    elif "xlarge" in model_name:
        _lowercase			:				Optional[Any] 						=						256
    elif "huge" in model_name:
        _lowercase			:				Dict 						=						352

    # set label information
    _lowercase			:				int 						=						'huggingface/label-files'
    if "large" in model_name or "huge" in model_name:
        _lowercase			:				str 						=						'imagenet-22k-id2label.json'
    else:
        _lowercase			:				Tuple 						=						'imagenet-1k-id2label.json'

    _lowercase			:				Union[str, Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    _lowercase			:				Optional[Any] 						=						FocalNetConfig(
        embed_dim=lowerCamelCase_				,					depths=lowerCamelCase_				,					focal_levels=lowerCamelCase_				,					focal_windows=lowerCamelCase_				,					use_conv_embed=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid=lowerCamelCase_				,					use_post_layernorm=lowerCamelCase_				,					use_layerscale=lowerCamelCase_				,					)

    return config



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    if "patch_embed.proj" in name:
        _lowercase			:				Optional[Any] 						=						name.replace('patch_embed.proj'				,					'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        _lowercase			:				str 						=						name.replace('patch_embed.norm'				,					'embeddings.norm'					)
    if "layers" in name:
        _lowercase			:				Any 						=						'encoder.' + name
    if "encoder.layers" in name:
        _lowercase			:				int 						=						name.replace('encoder.layers'				,					'encoder.stages'					)
    if "downsample.proj" in name:
        _lowercase			:				Tuple 						=						name.replace('downsample.proj'				,					'downsample.projection'					)
    if "blocks" in name:
        _lowercase			:				str 						=						name.replace('blocks'				,					'layers'					)
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        _lowercase			:				List[str] 						=						name.replace('modulation.f'				,					'modulation.projection_in'					)
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        _lowercase			:				int 						=						name.replace('modulation.h'				,					'modulation.projection_context'					)
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        _lowercase			:				Any 						=						name.replace('modulation.proj'				,					'modulation.projection_out'					)

    if name == "norm.weight":
        _lowercase			:				Any 						=						'layernorm.weight'
    if name == "norm.bias":
        _lowercase			:				Tuple 						=						'layernorm.bias'

    if "head" in name:
        _lowercase			:				Optional[int] 						=						name.replace('head'				,					'classifier'					)
    else:
        _lowercase			:				Optional[int] 						=						'focalnet.' + name

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    # fmt: off
    _lowercase			:				Dict 						=						{
        'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
        'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
        'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
        'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
        'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
        'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
        'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
        'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
        'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
        'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
    }
    # fmt: on

    _lowercase			:				Dict 						=						model_name_to_url[model_name]
    print('Checkpoint URL: '				,					lowerCamelCase_					)
    _lowercase			:				List[str] 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    # rename keys
    for key in state_dict.copy().keys():
        _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						val

    _lowercase			:				Union[str, Any] 						=						get_focalnet_config(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						FocalNetForImageClassification(lowerCamelCase_					)
    model.eval()

    # load state dict
    model.load_state_dict(lowerCamelCase_					)

    # verify conversion
    _lowercase			:				Optional[Any] 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowercase			:				Any 						=						BitImageProcessor(
        do_resize=lowerCamelCase_				,					size={'shortest_edge': 256}				,					resample=PILImageResampling.BILINEAR				,					do_center_crop=lowerCamelCase_				,					crop_size=224				,					do_normalize=lowerCamelCase_				,					image_mean=lowerCamelCase_				,					image_std=lowerCamelCase_				,					)
    _lowercase			:				List[str] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[Any] 						=						processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    _lowercase			:				str 						=						transforms.Compose(
        [
            transforms.Resize(256					),
            transforms.CenterCrop(224					),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,					std=[0.2_29, 0.2_24, 0.2_25]					),
        ]					)

    _lowercase			:				List[str] 						=						image_transforms(lowerCamelCase_					).unsqueeze(0					)

    # verify pixel_values
    assert torch.allclose(inputs.pixel_values				,					lowerCamelCase_				,					atol=1e-4					)

    _lowercase			:				Dict 						=						model(**lowerCamelCase_					)

    _lowercase			:				int 						=						outputs.logits.argmax(-1					).item()
    print('Predicted class:'				,					model.config.idalabel[predicted_class_idx]					)

    print('First values of logits:'				,					outputs.logits[0, :3]					)

    if model_name == "focalnet-tiny":
        _lowercase			:				Optional[Any] 						=						torch.tensor([0.21_66, -0.43_68, 0.21_91]					)
    elif model_name == "focalnet-tiny-lrf":
        _lowercase			:				int 						=						torch.tensor([1.16_69, 0.01_25, -0.16_95]					)
    elif model_name == "focalnet-small":
        _lowercase			:				str 						=						torch.tensor([0.49_17, -0.04_30, 0.13_41]					)
    elif model_name == "focalnet-small-lrf":
        _lowercase			:				Any 						=						torch.tensor([-0.25_88, -0.53_42, -0.23_31]					)
    elif model_name == "focalnet-base":
        _lowercase			:				List[Any] 						=						torch.tensor([-0.16_55, -0.40_90, -0.17_30]					)
    elif model_name == "focalnet-base-lrf":
        _lowercase			:				int 						=						torch.tensor([0.53_06, -0.04_83, -0.39_28]					)
    assert torch.allclose(outputs.logits[0, :3]				,					lowerCamelCase_				,					atol=1e-4					)
    print('Looks ok!'					)

    if pytorch_dump_folder_path is not None:
        print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''					)
        model.save_pretrained(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

    if push_to_hub:
        print(F'''Pushing model and processor of {model_name} to the hub...'''					)
        model.push_to_hub(F'''{model_name}'''					)
        processor.push_to_hub(F'''{model_name}'''					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: int				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="focalnet-tiny",
     type=str,
     help="Name of the FocalNet model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
     help="Whether to push the model and processor to the hub.",
 )

 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					parser.parse_args()
 convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


