





'''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()
lowerCAmelCase__      :   Tuple      	=			logging.get_logger(__name__)


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=False,       _UpperCAmelCase=False  ):
		__UpperCAmelCase	:							Tuple     =     "backbone." if is_semantic else ""

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

				__UpperCAmelCase	:							str     =     in_proj_weight[
				    : config.hidden_size, :
				]
				__UpperCAmelCase	:							List[Any]     =     q_bias
				__UpperCAmelCase	:							List[str]     =     in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				__UpperCAmelCase	:							Tuple     =     in_proj_weight[
				    -config.hidden_size :, :
				]
				__UpperCAmelCase	:							List[str]     =     v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				__UpperCAmelCase	:							Tuple     =     state_dict.pop(F"{prefix}blocks.{i}.gamma_1"  )
				__UpperCAmelCase	:							str     =     state_dict.pop(F"{prefix}blocks.{i}.gamma_2"  )

				__UpperCAmelCase	:							str     =     gamma_a
				__UpperCAmelCase	:							Optional[Any]     =     gamma_a

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

						__UpperCAmelCase	:							Tuple     =     table
						__UpperCAmelCase	:							Optional[Any]     =     index


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Tuple     =     dct.pop(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     val


def 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							int     =     "http://images.cocodataset.org/val2017/000000039769.jpg"
		__UpperCAmelCase	:							Optional[int]     =     Image.open(requests.get(_UpperCAmelCase,       stream=_UpperCAmelCase  ).raw  )
		return im


@torch.no_grad()
def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Tuple     =     BeitConfig()
		__UpperCAmelCase	:							Dict     =     False
		__UpperCAmelCase	:							List[str]     =     False
		__UpperCAmelCase	:							Dict     =     "huggingface/label-files"
		# set config parameters based on URL
		if checkpoint_url[-9:-4] == "pt22k":
				# masked image modeling
				__UpperCAmelCase	:							Tuple     =     True
				__UpperCAmelCase	:							Union[str, Any]     =     True
				__UpperCAmelCase	:							Optional[Any]     =     True
		elif checkpoint_url[-9:-4] == "ft22k":
				# intermediate fine-tuning on ImageNet-22k
				__UpperCAmelCase	:							Union[str, Any]     =     True
				__UpperCAmelCase	:							Tuple     =     21841
				__UpperCAmelCase	:							List[Any]     =     "imagenet-22k-id2label.json"
				__UpperCAmelCase	:							List[str]     =     json.load(open(hf_hub_download(_UpperCAmelCase,       _UpperCAmelCase,       repo_type="dataset"  ),       "r"  )  )
				__UpperCAmelCase	:							List[Any]     =     {int(_UpperCAmelCase  ): 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[9205]
				del idalabel[15027]
				__UpperCAmelCase	:							Dict     =     idalabel
				__UpperCAmelCase	:							int     =     {v: k for k, v in idalabel.items()}
		elif checkpoint_url[-8:-4] == "to1k":
				# fine-tuning on ImageNet-1k
				__UpperCAmelCase	:							Optional[int]     =     True
				__UpperCAmelCase	:							str     =     1000
				__UpperCAmelCase	:							Union[str, Any]     =     "imagenet-1k-id2label.json"
				__UpperCAmelCase	:							Dict     =     json.load(open(hf_hub_download(_UpperCAmelCase,       _UpperCAmelCase,       repo_type="dataset"  ),       "r"  )  )
				__UpperCAmelCase	:							int     =     {int(_UpperCAmelCase  ): v for k, v in idalabel.items()}
				__UpperCAmelCase	:							Any     =     idalabel
				__UpperCAmelCase	:							List[str]     =     {v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						__UpperCAmelCase	:							int     =     384
				if "512" in checkpoint_url:
						__UpperCAmelCase	:							List[Any]     =     512
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				__UpperCAmelCase	:							Optional[int]     =     True
				__UpperCAmelCase	:							Dict     =     150
				__UpperCAmelCase	:							int     =     "ade20k-id2label.json"
				__UpperCAmelCase	:							int     =     json.load(open(hf_hub_download(_UpperCAmelCase,       _UpperCAmelCase,       repo_type="dataset"  ),       "r"  )  )
				__UpperCAmelCase	:							List[str]     =     {int(_UpperCAmelCase  ): v for k, v in idalabel.items()}
				__UpperCAmelCase	:							str     =     idalabel
				__UpperCAmelCase	:							List[str]     =     {v: k for k, v in idalabel.items()}
				__UpperCAmelCase	:							Optional[Any]     =     640
				__UpperCAmelCase	:							Tuple     =     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:
				__UpperCAmelCase	:							List[Any]     =     1024
				__UpperCAmelCase	:							Any     =     4096
				__UpperCAmelCase	:							List[str]     =     24
				__UpperCAmelCase	:							Optional[int]     =     16
				if "ade20k" in checkpoint_url:
						__UpperCAmelCase	:							Union[str, Any]     =     640
						__UpperCAmelCase	:							Any     =     [7, 11, 15, 23]
		else:
				raise ValueError("Should either find 'base' or 'large' in checkpoint URL"  )

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

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

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

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

		__UpperCAmelCase	:							Tuple     =     image_processor(images=_UpperCAmelCase,       return_tensors="pt"  )
		__UpperCAmelCase	:							str     =     encoding["pixel_values"]

		__UpperCAmelCase	:							Union[str, Any]     =     model(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     outputs.logits

		# verify logits
		__UpperCAmelCase	:							Optional[Any]     =     torch.Size([1, 1000]  )
		if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"  ):
				__UpperCAmelCase	:							int     =     torch.Size([1, 196, 8192]  )
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"  ):
				__UpperCAmelCase	:							List[str]     =     torch.Size([1, 196, 8192]  )
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"  ):
				__UpperCAmelCase	:							Union[str, Any]     =     torch.Size([1, 21841]  )
				__UpperCAmelCase	:							Tuple     =     torch.tensor([2.2_288, 2.4_671, 0.7_395]  )
				__UpperCAmelCase	:							str     =     2397
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"  ):
				__UpperCAmelCase	:							List[str]     =     torch.Size([1, 21841]  )
				__UpperCAmelCase	:							Any     =     torch.tensor([1.6_881, -0.2_787, 0.5_901]  )
				__UpperCAmelCase	:							Union[str, Any]     =     2396
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"  ):
				__UpperCAmelCase	:							Any     =     torch.tensor([0.1_241, 0.0_798, -0.6_569]  )
				__UpperCAmelCase	:							Tuple     =     285
		elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"  ):
				__UpperCAmelCase	:							Dict     =     torch.tensor([-1.2_385, -1.0_987, -1.0_108]  )
				__UpperCAmelCase	:							Dict     =     281
		elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"  ):
				__UpperCAmelCase	:							List[Any]     =     torch.tensor([-1.5_303, -0.9_484, -0.3_147]  )
				__UpperCAmelCase	:							List[Any]     =     761
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"  ):
				__UpperCAmelCase	:							Any     =     torch.tensor([0.4_610, -0.0_928, 0.2_086]  )
				__UpperCAmelCase	:							Optional[Any]     =     761
		elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"  ):
				__UpperCAmelCase	:							Tuple     =     torch.tensor([-0.4_804, 0.6_257, -0.1_837]  )
				__UpperCAmelCase	:							Tuple     =     761
		elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"  ):
				__UpperCAmelCase	:							int     =     torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]  )
				__UpperCAmelCase	:							Any     =     761
		elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"  ):
				__UpperCAmelCase	:							Any     =     torch.tensor([-0.3_062, 0.7_261, 0.4_852]  )
				__UpperCAmelCase	:							Dict     =     761
		elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"  ):
				__UpperCAmelCase	:							str     =     (1, 150, 160, 160)
				__UpperCAmelCase	:							str     =     torch.tensor(
				    [
				        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
				        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
				        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
				    ]  )
		elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"  ):
				__UpperCAmelCase	:							str     =     (1, 150, 160, 160)
				__UpperCAmelCase	:							List[Any]     =     torch.tensor(
				    [
				        [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
				        [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
				        [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
				    ]  )
		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],       _UpperCAmelCase,       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],       _UpperCAmelCase,       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(_UpperCAmelCase  ).mkdir(exist_ok=_UpperCAmelCase  )
		print(F"Saving model to {pytorch_dump_folder_path}"  )
		model.save_pretrained(_UpperCAmelCase  )
		print(F"Saving image processor to {pytorch_dump_folder_path}"  )
		image_processor.save_pretrained(_UpperCAmelCase  )


if __name__ == "__main__":
			lowerCAmelCase__      :   List[str]      	=			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."
			)
			lowerCAmelCase__      :   int      	=			parser.parse_args()
			convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











'''simple docstring'''

from __future__ import annotations

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


if __name__ == "__main__":
			print(solution(int(input().strip())))




