


def       _UpperCamelCase (						UpperCamelCase__ = 4_0_0_0_0_0_0   ):
			UpperCAmelCase__					:   List[str]       				=       [0, 1]
			UpperCAmelCase__					:   Any       				=       0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1]   )
						if fib[i + 2] > n:
									break
						i += 1
			UpperCAmelCase__					:   str       				=       0
			for j in range(len(UpperCamelCase__   ) - 1   ):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


if __name__ == "__main__":
		print(f"""{solution() = }""")



'''simple docstring'''



import argparse
import json
from pathlib import Path

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

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


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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=False	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   str       				=       """backbone.""" if is_semantic else """"""

			UpperCAmelCase__					:   Optional[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"""),
			        (f'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
			    ]   )

			if has_lm_head:
						# mask token + layernorm
						rename_keys.extend(
						    [
						        ("""mask_token""", """beit.embeddings.mask_token"""),
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.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__					:   Optional[Any]       				=       """backbone.""" if is_semantic else """"""
						# queries, keys and values
						UpperCAmelCase__					:   Any       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''   )
						UpperCAmelCase__					:   List[str]       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''   )
						UpperCAmelCase__					:   int       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''   )

						UpperCAmelCase__					:   Optional[Any]       				=       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCAmelCase__					:   Any       				=       q_bias
						UpperCAmelCase__					:   Dict       				=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase__					:   Any       				=       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase__					:   Any       				=       v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						UpperCAmelCase__					:   Dict       				=       state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''   )
						UpperCAmelCase__					:   Dict       				=       state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''   )

						UpperCAmelCase__					:   Union[str, Any]       				=       gamma_a
						UpperCAmelCase__					:   str       				=       gamma_a







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       dct.pop(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       val







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Any       				=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase__					:   Dict       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   )
			return im







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   Optional[Any]       				=       False if """rvlcdip""" in checkpoint_url else True
			UpperCAmelCase__					:   Any       				=       BeitConfig(use_absolute_position_embeddings=UpperCamelCase__	, use_mask_token=UpperCamelCase__   )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						UpperCAmelCase__					:   Optional[Any]       				=       1_0_2_4
						UpperCAmelCase__					:   Dict       				=       4_0_9_6
						UpperCAmelCase__					:   Any       				=       2_4
						UpperCAmelCase__					:   Tuple       				=       1_6

			# labels
			if "rvlcdip" in checkpoint_url:
						UpperCAmelCase__					:   int       				=       1_6
						UpperCAmelCase__					:   List[str]       				=       """huggingface/label-files"""
						UpperCAmelCase__					:   Optional[Any]       				=       """rvlcdip-id2label.json"""
						UpperCAmelCase__					:   Optional[int]       				=       json.load(open(hf_hub_download(UpperCamelCase__	, UpperCamelCase__	, repo_type="""dataset"""   )	, """r"""   )   )
						UpperCAmelCase__					:   Union[str, Any]       				=       {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
						UpperCAmelCase__					:   Optional[Any]       				=       idalabel
						UpperCAmelCase__					:   List[Any]       				=       {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			UpperCAmelCase__					:   Optional[Any]       				=       torch.hub.load_state_dict_from_url(UpperCamelCase__	, map_location="""cpu"""   )["""model"""]

			UpperCAmelCase__					:   List[str]       				=       create_rename_keys(UpperCamelCase__	, has_lm_head=UpperCamelCase__   )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			read_in_q_k_v(UpperCamelCase__	, UpperCamelCase__	, has_lm_head=UpperCamelCase__   )

			# load HuggingFace model
			UpperCAmelCase__					:   str       				=       BeitForMaskedImageModeling(UpperCamelCase__   ) if has_lm_head else BeitForImageClassification(UpperCamelCase__   )
			model.eval()
			model.load_state_dict(UpperCamelCase__   )

			# Check outputs on an image
			UpperCAmelCase__					:   List[str]       				=       BeitImageProcessor(
			    size=config.image_size	, resample=PILImageResampling.BILINEAR	, do_center_crop=UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       prepare_img()

			UpperCAmelCase__					:   Optional[Any]       				=       image_processor(images=UpperCamelCase__	, return_tensors="""pt"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       encoding["""pixel_values"""]

			UpperCAmelCase__					:   Optional[Any]       				=       model(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       outputs.logits

			# verify logits
			UpperCAmelCase__					:   int       				=       [1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
			assert logits.shape == torch.Size(UpperCamelCase__   ), "Shape of logits 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 push_to_hub:
						if has_lm_head:
									UpperCAmelCase__					:   Union[str, Any]       				=       """dit-base""" if """base""" in checkpoint_url else """dit-large"""
						else:
									UpperCAmelCase__					:   Tuple       				=       """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
						image_processor.push_to_hub(
						    repo_path_or_name=Path(UpperCamelCase__	, UpperCamelCase__   )	, organization="""nielsr"""	, commit_message="""Add image processor"""	, use_temp_dir=UpperCamelCase__	, )
						model.push_to_hub(
						    repo_path_or_name=Path(UpperCamelCase__	, UpperCamelCase__   )	, organization="""nielsr"""	, commit_message="""Add model"""	, use_temp_dir=UpperCamelCase__	, )


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

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		__A		=parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)