"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=False      ,				__SCREAMING_SNAKE_CASE=False				)			->			List[Any]:
		__lowerCAmelCase: Union[str, Any]					   =    "backbone." if is_semantic else ""

		__lowerCAmelCase: List[Any]					   =    []
		for i in range(config.num_hidden_layers				):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")				)
				rename_keys.append(
				    (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")				)
				rename_keys.append(
				    (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")				)
				rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")				)
				rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")				)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (F"{prefix}cls_token", "beit.embeddings.cls_token"),
		        (F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
		        (F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
		    ]				)

		if has_lm_head:
				# mask token + shared relative position bias + layernorm
				rename_keys.extend(
				    [
				        ("mask_token", "beit.embeddings.mask_token"),
				        (
				            "rel_pos_bias.relative_position_bias_table",
				            "beit.encoder.relative_position_bias.relative_position_bias_table",
				        ),
				        (
				            "rel_pos_bias.relative_position_index",
				            "beit.encoder.relative_position_bias.relative_position_index",
				        ),
				        ("norm.weight", "layernorm.weight"),
				        ("norm.bias", "layernorm.bias"),
				    ]				)
		elif is_semantic:
				# semantic segmentation classification heads
				rename_keys.extend(
				    [
				        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
				        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
				        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
				        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
				    ]				)
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
				        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
				        ("head.weight", "classifier.weight"),
				        ("head.bias", "classifier.bias"),
				    ]				)

		return rename_keys






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=False      ,				__SCREAMING_SNAKE_CASE=False				)			->			Any:
		for i in range(config.num_hidden_layers				):
				__lowerCAmelCase: List[str]					   =    "backbone." if is_semantic else ""
				# queries, keys and values
				__lowerCAmelCase: int					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"				)
				__lowerCAmelCase: List[str]					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"				)
				__lowerCAmelCase: Optional[Any]					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"				)

				__lowerCAmelCase: List[str]					   =    in_proj_weight[
				    : config.hidden_size, :
				]
				__lowerCAmelCase: Optional[Any]					   =    q_bias
				__lowerCAmelCase: Union[str, Any]					   =    in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				__lowerCAmelCase: Tuple					   =    in_proj_weight[
				    -config.hidden_size :, :
				]
				__lowerCAmelCase: Optional[int]					   =    v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				__lowerCAmelCase: Optional[int]					   =    state_dict.pop(F"{prefix}blocks.{i}.gamma_1"				)
				__lowerCAmelCase: Any					   =    state_dict.pop(F"{prefix}blocks.{i}.gamma_2"				)

				__lowerCAmelCase: Optional[Any]					   =    gamma_a
				__lowerCAmelCase: Optional[int]					   =    gamma_a

				# relative_position bias table + index
				if not has_lm_head:
						# each layer has its own relative position bias
						__lowerCAmelCase: Tuple					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"				)
						__lowerCAmelCase: Dict					   =    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"				)

						__lowerCAmelCase: Dict					   =    table
						__lowerCAmelCase: Any					   =    index






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Optional[int]:
		__lowerCAmelCase: Optional[int]					   =    dct.pop(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[Any]					   =    val






def 					a__					(					)			->			Optional[int]:
		__lowerCAmelCase: Union[str, Any]					   =    "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase: str					   =    Image.open(requests.get(__SCREAMING_SNAKE_CASE      ,				stream=__SCREAMING_SNAKE_CASE				).raw				)
		return im






@torch.no_grad()
def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
		__lowerCAmelCase: List[Any]					   =    BeitConfig()
		__lowerCAmelCase: Optional[int]					   =    False
		__lowerCAmelCase: str					   =    False
		__lowerCAmelCase: List[str]					   =    "huggingface/label-files"
		# set config parameters based on URL
		if checkpoint_url[-9:-4] == "pt22k":
				# masked image modeling
				__lowerCAmelCase: Optional[int]					   =    True
				__lowerCAmelCase: Tuple					   =    True
				__lowerCAmelCase: List[str]					   =    True
		elif checkpoint_url[-9:-4] == "ft22k":
				# intermediate fine-tuning on ImageNet-22k
				__lowerCAmelCase: Optional[Any]					   =    True
				__lowerCAmelCase: Any					   =    2_1_8_4_1
				__lowerCAmelCase: Any					   =    "imagenet-22k-id2label.json"
				__lowerCAmelCase: Any					   =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				repo_type="dataset"				)      ,				"r"				)				)
				__lowerCAmelCase: List[str]					   =    {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[9_2_0_5]
				del idalabel[1_5_0_2_7]
				__lowerCAmelCase: Optional[int]					   =    idalabel
				__lowerCAmelCase: str					   =    {v: k for k, v in idalabel.items()}
		elif checkpoint_url[-8:-4] == "to1k":
				# fine-tuning on ImageNet-1k
				__lowerCAmelCase: int					   =    True
				__lowerCAmelCase: List[Any]					   =    1_0_0_0
				__lowerCAmelCase: str					   =    "imagenet-1k-id2label.json"
				__lowerCAmelCase: Any					   =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				repo_type="dataset"				)      ,				"r"				)				)
				__lowerCAmelCase: Tuple					   =    {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				__lowerCAmelCase: Union[str, Any]					   =    idalabel
				__lowerCAmelCase: Optional[int]					   =    {v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						__lowerCAmelCase: Any					   =    3_8_4
				if "512" in checkpoint_url:
						__lowerCAmelCase: Optional[int]					   =    5_1_2
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				__lowerCAmelCase: str					   =    True
				__lowerCAmelCase: Optional[int]					   =    1_5_0
				__lowerCAmelCase: Optional[int]					   =    "ade20k-id2label.json"
				__lowerCAmelCase: Any					   =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				repo_type="dataset"				)      ,				"r"				)				)
				__lowerCAmelCase: Union[str, Any]					   =    {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				__lowerCAmelCase: List[Any]					   =    idalabel
				__lowerCAmelCase: Optional[Any]					   =    {v: k for k, v in idalabel.items()}
				__lowerCAmelCase: str					   =    6_4_0
				__lowerCAmelCase: Optional[Any]					   =    True
		else:
				raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"				)

		# size of the architecture
		if "base" in checkpoint_url:
				pass
		elif "large" in checkpoint_url:
				__lowerCAmelCase: Dict					   =    1_0_2_4
				__lowerCAmelCase: Union[str, Any]					   =    4_0_9_6
				__lowerCAmelCase: Dict					   =    2_4
				__lowerCAmelCase: Union[str, Any]					   =    1_6
				if "ade20k" in checkpoint_url:
						__lowerCAmelCase: int					   =    6_4_0
						__lowerCAmelCase: Optional[Any]					   =    [7, 1_1, 1_5, 2_3]
		else:
				raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

		# load state_dict of original model, remove and rename some keys
		__lowerCAmelCase: Optional[int]					   =    torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE      ,				map_location="cpu"      ,				check_hash=__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[Any]					   =    state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

		__lowerCAmelCase: List[Any]					   =    create_rename_keys(__SCREAMING_SNAKE_CASE      ,				has_lm_head=__SCREAMING_SNAKE_CASE      ,				is_semantic=__SCREAMING_SNAKE_CASE				)
		for src, dest in rename_keys:
				rename_key(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		read_in_q_k_v(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				has_lm_head=__SCREAMING_SNAKE_CASE      ,				is_semantic=__SCREAMING_SNAKE_CASE				)
		if is_semantic:
				# add prefix to decoder keys
				for key, val in state_dict.copy().items():
						__lowerCAmelCase: List[str]					   =    state_dict.pop(__SCREAMING_SNAKE_CASE				)
						if key.startswith("backbone.fpn"				):
								__lowerCAmelCase: Tuple					   =    key.replace("backbone.fpn"      ,				"fpn"				)
						__lowerCAmelCase: List[str]					   =    val

    # load HuggingFace model
		if checkpoint_url[-9:-4] == "pt22k":
				__lowerCAmelCase: str					   =    BeitForMaskedImageModeling(__SCREAMING_SNAKE_CASE				)
		elif "ade20k" in checkpoint_url:
				__lowerCAmelCase: Optional[int]					   =    BeitForSemanticSegmentation(__SCREAMING_SNAKE_CASE				)
		else:
				__lowerCAmelCase: Optional[int]					   =    BeitForImageClassification(__SCREAMING_SNAKE_CASE				)
		model.eval()
		model.load_state_dict(__SCREAMING_SNAKE_CASE				)

		# Check outputs on an image
		if is_semantic:
				__lowerCAmelCase: Union[str, Any]					   =    BeitImageProcessor(size=config.image_size      ,				do_center_crop=__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Any					   =    load_dataset("hf-internal-testing/fixtures_ade20k"      ,				split="test"				)
				__lowerCAmelCase: int					   =    Image.open(ds[0]["file"]				)
		else:
				__lowerCAmelCase: Tuple					   =    BeitImageProcessor(
				    size=config.image_size      ,				resample=PILImageResampling.BILINEAR      ,				do_center_crop=__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Union[str, Any]					   =    prepare_img()

		__lowerCAmelCase: Union[str, Any]					   =    image_processor(images=__SCREAMING_SNAKE_CASE      ,				return_tensors="pt"				)
		__lowerCAmelCase: Tuple					   =    encoding["pixel_values"]

		__lowerCAmelCase: str					   =    model(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Tuple					   =    outputs.logits

		# verify logits
		__lowerCAmelCase: Optional[Any]					   =    torch.Size([1, 1_0_0_0]				)
		if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"				):
				__lowerCAmelCase: Optional[Any]					   =    torch.Size([1, 1_9_6, 8_1_9_2]				)
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"				):
				__lowerCAmelCase: Optional[int]					   =    torch.Size([1, 1_9_6, 8_1_9_2]				)
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"				):
				__lowerCAmelCase: Dict					   =    torch.Size([1, 2_1_8_4_1]				)
				__lowerCAmelCase: Optional[int]					   =    torch.tensor([2.2288, 2.4671, 0.7395]				)
				__lowerCAmelCase: str					   =    2_3_9_7
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"				):
				__lowerCAmelCase: Optional[Any]					   =    torch.Size([1, 2_1_8_4_1]				)
				__lowerCAmelCase: Optional[Any]					   =    torch.tensor([1.6881, -0.2787, 0.5901]				)
				__lowerCAmelCase: Any					   =    2_3_9_6
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"				):
				__lowerCAmelCase: int					   =    torch.tensor([0.1241, 0.0798, -0.6569]				)
				__lowerCAmelCase: str					   =    2_8_5
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Optional[int]					   =    torch.tensor([-1.2385, -1.0987, -1.0108]				)
				__lowerCAmelCase: str					   =    2_8_1
		elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"				):
				__lowerCAmelCase: List[Any]					   =    torch.tensor([-1.5303, -0.9484, -0.3147]				)
				__lowerCAmelCase: Union[str, Any]					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"				):
				__lowerCAmelCase: Tuple					   =    torch.tensor([0.4610, -0.0928, 0.2086]				)
				__lowerCAmelCase: str					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Dict					   =    torch.tensor([-0.4804, 0.6257, -0.1837]				)
				__lowerCAmelCase: List[str]					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Union[str, Any]					   =    torch.tensor([[-0.5122, 0.5117, -0.2113]]				)
				__lowerCAmelCase: Any					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"				):
				__lowerCAmelCase: Union[str, Any]					   =    torch.tensor([-0.3062, 0.7261, 0.4852]				)
				__lowerCAmelCase: Optional[Any]					   =    7_6_1
		elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"				):
				__lowerCAmelCase: Dict					   =    (1, 1_5_0, 1_6_0, 1_6_0)
				__lowerCAmelCase: Dict					   =    torch.tensor(
				    [
				        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
				        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
				        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
				    ]				)
		elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"				):
				__lowerCAmelCase: List[Any]					   =    (1, 1_5_0, 1_6_0, 1_6_0)
				__lowerCAmelCase: Any					   =    torch.tensor(
				    [
				        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
				        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
				        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
				    ]				)
		else:
				raise ValueError("Can't verify logits as model is not supported"				)

		assert logits.shape == expected_shape, "Shape of logits not as expected"
		if not has_lm_head:
				if is_semantic:
						assert torch.allclose(
						    logits[0, :3, :3, :3]      ,				__SCREAMING_SNAKE_CASE      ,				atol=1E-3				), "First elements of logits not as expected"
				else:
						print("Predicted class idx:"      ,				logits.argmax(-1				).item()				)
						assert torch.allclose(
						    logits[0, :3]      ,				__SCREAMING_SNAKE_CASE      ,				atol=1E-3				), "First elements of logits not as expected"
						assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

		Path(__SCREAMING_SNAKE_CASE				).mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)
		print(F"Saving model to {pytorch_dump_folder_path}"				)
		model.save_pretrained(__SCREAMING_SNAKE_CASE				)
		print(F"Saving image processor to {pytorch_dump_folder_path}"				)
		image_processor.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			__A		     =    argparse.ArgumentParser()

			parser.add_argument(
			    "--checkpoint_url",
			    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
			    type=str,
			    help="URL to the original PyTorch checkpoint (.pth file).",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
			)
			__A		     =    parser.parse_args()
			convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""


import random
import sys

__A		     =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def 					a__					(					)			->			None:
		__lowerCAmelCase: int					   =    input("Enter message: "				)
		__lowerCAmelCase: Any					   =    "LFWOAYUISVKMNXPBDCRJTQEGHZ"
		__lowerCAmelCase: int					   =    input("Encrypt/Decrypt [e/d]: "				)

		check_valid_key(__SCREAMING_SNAKE_CASE				)

		if resp.lower().startswith("e"				):
				__lowerCAmelCase: Tuple					   =    "encrypt"
				__lowerCAmelCase: int					   =    encrypt_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		elif resp.lower().startswith("d"				):
				__lowerCAmelCase: str					   =    "decrypt"
				__lowerCAmelCase: List[str]					   =    decrypt_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

		print(F"\n{mode.title()}ion: \n{translated}"				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			None:
		__lowerCAmelCase: List[Any]					   =    list(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[int]					   =    list(__SCREAMING_SNAKE_CASE				)
		key_list.sort()
		letters_list.sort()

		if key_list != letters_list:
				sys.exit("Error in the key or symbol set."				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		return translate_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				"encrypt"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		return translate_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				"decrypt"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase: Union[str, Any]					   =    ""
		__lowerCAmelCase: List[str]					   =    LETTERS
		__lowerCAmelCase: Optional[int]					   =    key

		if mode == "decrypt":
				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    chars_b, chars_a

		for symbol in message:
				if symbol.upper() in chars_a:
						__lowerCAmelCase: Union[str, Any]					   =    chars_a.find(symbol.upper()				)
						if symbol.isupper():
								translated += chars_b[sym_index].upper()
						else:
								translated += chars_b[sym_index].lower()
				else:
						translated += symbol

		return translated






def 					a__					(					)			->			str:
		__lowerCAmelCase: int					   =    list(__SCREAMING_SNAKE_CASE				)
		random.shuffle(__SCREAMING_SNAKE_CASE				)
		return "".join(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			main()


