



'''simple docstring'''

import math



def 					__lowerCamelCase					(				lowerCAmelCase_ = 100  )      ->				int:
		_a					:				List[Any]				=							sum(i * i for i in range(1						,      n + 1  )  )
		_a					:				Dict				=							int(math.pow(sum(range(1						,      n + 1  )  )						,      2  )  )
		return square_of_sum - sum_of_squares


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()
__lowerCAmelCase						      =       logging.get_logger(__name__)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				Any:
		_a					:				Dict				=							'backbone.' if is_semantic else ''

		_a					:				Optional[int]				=							[]
		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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				int:
		for i in range(config.num_hidden_layers  ):
				_a					:				Union[str, Any]				=							'backbone.' if is_semantic else ''
				# queries, keys and values
				_a					:				Any				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

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

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

				_a					:				Tuple				=							gamma_a
				_a					:				Optional[Any]				=							gamma_a



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Dict:
		_a					:				List[Any]				=							dct.pop(lowerCAmelCase_  )
		_a					:				List[Any]				=							val



def 					__lowerCamelCase					(				)      ->				Dict:
		_a					:				Optional[Any]				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a					:				Dict				=							Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Union[str, Any]:
		_a					:				Optional[Any]				=							False if 'rvlcdip' in checkpoint_url else True
		_a					:				List[Any]				=							BeitConfig(use_absolute_position_embeddings=lowerCAmelCase_						,      use_mask_token=lowerCAmelCase_  )

		# size of the architecture
		if "large" in checkpoint_url or "dit-l" in checkpoint_url:
				_a					:				Optional[Any]				=							1024
				_a					:				Tuple				=							4096
				_a					:				Any				=							24
				_a					:				Optional[int]				=							16

		# labels
		if "rvlcdip" in checkpoint_url:
				_a					:				Any				=							16
				_a					:				Tuple				=							'huggingface/label-files'
				_a					:				List[Any]				=							'rvlcdip-id2label.json'
				_a					:				Optional[int]				=							json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='dataset'  )						,      'r'  )  )
				_a					:				int				=							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
				_a					:				int				=							idalabel
				_a					:				int				=							{v: k for k, v in idalabel.items()}

		# load state_dict of original model, remove and rename some keys
		_a					:				List[Any]				=							torch.hub.load_state_dict_from_url(lowerCAmelCase_						,      map_location='cpu'  )['model']

		_a					:				Dict				=							create_rename_keys(lowerCAmelCase_						,      has_lm_head=lowerCAmelCase_  )
		for src, dest in rename_keys:
				rename_key(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		read_in_q_k_v(lowerCAmelCase_						,      lowerCAmelCase_						,      has_lm_head=lowerCAmelCase_  )

		# load HuggingFace model
		_a					:				Dict				=							BeitForMaskedImageModeling(lowerCAmelCase_  ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_  )
		model.eval()
		model.load_state_dict(lowerCAmelCase_  )

		# Check outputs on an image
		_a					:				Dict				=							BeitImageProcessor(
		    size=config.image_size						,      resample=PILImageResampling.BILINEAR						,      do_center_crop=lowerCAmelCase_  )
		_a					:				Dict				=							prepare_img()

		_a					:				str				=							image_processor(images=lowerCAmelCase_						,      return_tensors='pt'  )
		_a					:				Optional[Any]				=							encoding['pixel_values']

		_a					:				Optional[Any]				=							model(lowerCAmelCase_  )
		_a					:				Optional[Any]				=							outputs.logits

		# verify logits
		_a					:				int				=							[1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
		assert logits.shape == torch.Size(lowerCAmelCase_  ), "Shape of logits not as expected"

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

		if push_to_hub:
				if has_lm_head:
						_a					:				Tuple				=							'dit-base' if 'base' in checkpoint_url else 'dit-large'
				else:
						_a					:				int				=							'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(lowerCAmelCase_						,      lowerCAmelCase_  )						,      organization='nielsr'						,      commit_message='Add image processor'						,      use_temp_dir=lowerCAmelCase_						,      )
				model.push_to_hub(
				    repo_path_or_name=Path(lowerCAmelCase_						,      lowerCAmelCase_  )						,      organization='nielsr'						,      commit_message='Add model'						,      use_temp_dir=lowerCAmelCase_						,      )


if __name__ == "__main__":
							__lowerCAmelCase						      =       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''',
							)
							__lowerCAmelCase						      =       parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




