




"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Any     							=							logging.get_logger(__name__)



def     a__ (				snake_case__       , snake_case__=False				)						->					str:
			lowerCamelCase			      =					[]
			for i in range(config.num_hidden_layers				):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F'blocks.{i}.norm1.weight', F'deit.encoder.layer.{i}.layernorm_before.weight')				)
						rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')				)
						rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')				)
						rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')				)
						rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')				)
						rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')				)
						rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')				)
						rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')				)
						rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')				)
						rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')				)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("""cls_token""", """deit.embeddings.cls_token"""),
			        ("""dist_token""", """deit.embeddings.distillation_token"""),
			        ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
			        ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
			        ("""pos_embed""", """deit.embeddings.position_embeddings"""),
			    ]				)

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
						        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
						    ]				)

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						lowerCamelCase			      =					[(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""				) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("""norm.weight""", """deit.layernorm.weight"""),
						        ("""norm.bias""", """deit.layernorm.bias"""),
						        ("""head.weight""", """cls_classifier.weight"""),
						        ("""head.bias""", """cls_classifier.bias"""),
						        ("""head_dist.weight""", """distillation_classifier.weight"""),
						        ("""head_dist.bias""", """distillation_classifier.bias"""),
						    ]				)

			return rename_keys



def     a__ (				snake_case__       , snake_case__       , snake_case__=False				)						->					Dict:
			for i in range(config.num_hidden_layers				):
						if base_model:
									lowerCamelCase			      =					""""""
						else:
									lowerCamelCase			      =					"""deit."""
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCamelCase			      =					state_dict.pop(F'blocks.{i}.attn.qkv.weight'				)
						lowerCamelCase			      =					state_dict.pop(F'blocks.{i}.attn.qkv.bias'				)
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase			      =					in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase			      =					in_proj_bias[: config.hidden_size]
						lowerCamelCase			      =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase			      =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCamelCase			      =					in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase			      =					in_proj_bias[-config.hidden_size :]



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					str:
			lowerCamelCase			      =					dct.pop(snake_case__				)
			lowerCamelCase			      =					val



def     a__ (				)						->					str:
			lowerCamelCase			      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase			      =					Image.open(requests.get(snake_case__       , stream=snake_case__				).raw				)
			return im



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__				)						->					Dict:
			lowerCamelCase			      =					DeiTConfig()
			# all deit models have fine-tuned heads
			lowerCamelCase			      =					False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCamelCase			      =					10_00
			lowerCamelCase			      =					"""huggingface/label-files"""
			lowerCamelCase			      =					"""imagenet-1k-id2label.json"""
			lowerCamelCase			      =					json.load(open(hf_hub_download(snake_case__       , snake_case__       , repo_type="""dataset"""				)       , """r"""				)				)
			lowerCamelCase			      =					{int(snake_case__				): v for k, v in idalabel.items()}
			lowerCamelCase			      =					idalabel
			lowerCamelCase			      =					{v: k for k, v in idalabel.items()}
			lowerCamelCase			      =					int(deit_name[-6:-4]				)
			lowerCamelCase			      =					int(deit_name[-3:]				)
			# size of the architecture
			if deit_name[9:].startswith("""tiny"""				):
						lowerCamelCase			      =					1_92
						lowerCamelCase			      =					7_68
						lowerCamelCase			      =					12
						lowerCamelCase			      =					3
			elif deit_name[9:].startswith("""small"""				):
						lowerCamelCase			      =					3_84
						lowerCamelCase			      =					15_36
						lowerCamelCase			      =					12
						lowerCamelCase			      =					6
			if deit_name[9:].startswith("""base"""				):
						pass
			elif deit_name[4:].startswith("""large"""				):
						lowerCamelCase			      =					10_24
						lowerCamelCase			      =					40_96
						lowerCamelCase			      =					24
						lowerCamelCase			      =					16

			# load original model from timm
			lowerCamelCase			      =					timm.create_model(snake_case__       , pretrained=snake_case__				)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase			      =					timm_model.state_dict()
			lowerCamelCase			      =					create_rename_keys(snake_case__       , snake_case__				)
			for src, dest in rename_keys:
						rename_key(snake_case__       , snake_case__       , snake_case__				)
			read_in_q_k_v(snake_case__       , snake_case__       , snake_case__				)

			# load HuggingFace model
			lowerCamelCase			      =					DeiTForImageClassificationWithTeacher(snake_case__				).eval()
			model.load_state_dict(snake_case__				)

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowerCamelCase			      =					int(
			    (2_56 / 2_24) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			lowerCamelCase			      =					DeiTImageProcessor(size=snake_case__       , crop_size=config.image_size				)
			lowerCamelCase			      =					image_processor(images=prepare_img()       , return_tensors="""pt"""				)
			lowerCamelCase			      =					encoding["""pixel_values"""]
			lowerCamelCase			      =					model(snake_case__				)

			lowerCamelCase			      =					timm_model(snake_case__				)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(snake_case__       , outputs.logits       , atol=1E-3				)

			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			print(F'Saving model {deit_name} to {pytorch_dump_folder_path}'				)
			model.save_pretrained(snake_case__				)
			print(F'Saving image processor to {pytorch_dump_folder_path}'				)
			image_processor.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			Optional[Any]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--deit_name""",
					    default="""vit_deit_base_distilled_patch16_224""",
					    type=str,
					    help="""Name of the DeiT timm 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."""
					)

					lowerCAmelCase							:			Dict     							=							parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









"""simple docstring"""




from collections.abc import Generator



def     a__ (				)						->					Generator[int, None, None]:
			lowerCamelCase      ,   lowerCamelCase			      =					0, 1
			while True:
						lowerCamelCase      ,   lowerCamelCase			      =					b, a + b
						yield b



def     a__ (				snake_case__ = 10_00				)						->					int:
			lowerCamelCase			      =					1
			lowerCamelCase			      =					fibonacci_generator()
			while len(str(next(snake_case__				)				)				) < n:
						answer += 1
			return answer + 1


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



