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       )->       int:



			"""simple docstring"""







			lowercase							=		'''backbone.''' if is_semantic else ''''''

			lowercase							=		[]
			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       )->       Union[str, Any]:



			"""simple docstring"""







			for i in range(config.num_hidden_layers       ):
						lowercase							=		'''backbone.''' if is_semantic else ''''''
						# queries, keys and values
						lowercase							=		state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'       )
						lowercase							=		state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'       )
						lowercase							=		state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'       )

						lowercase							=		in_proj_weight[
						    : config.hidden_size, :
						]
						lowercase							=		q_bias
						lowercase							=		in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowercase							=		in_proj_weight[
						    -config.hidden_size :, :
						]
						lowercase							=		v_bias

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

						lowercase							=		gamma_a
						lowercase							=		gamma_a






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			lowercase							=		dct.pop(UpperCAmelCase       )
			lowercase							=		val






def        __UpperCAmelCase    (       )->       int:



			"""simple docstring"""







			lowercase							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowercase							=		Image.open(requests.get(UpperCAmelCase,		stream=UpperCAmelCase       ).raw       )
			return im






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=False       )->       Dict:



			"""simple docstring"""







			lowercase							=		False if '''rvlcdip''' in checkpoint_url else True
			lowercase							=		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:
						lowercase							=		1024
						lowercase							=		4096
						lowercase							=		24
						lowercase							=		16

			# labels
			if "rvlcdip" in checkpoint_url:
						lowercase							=		16
						lowercase							=		'''huggingface/label-files'''
						lowercase							=		'''rvlcdip-id2label.json'''
						lowercase							=		json.load(open(hf_hub_download(UpperCAmelCase,		UpperCAmelCase,		repo_type='''dataset'''       ),		'''r'''       )       )
						lowercase							=		{int(UpperCAmelCase       ): v for k, v in idalabel.items()}
						lowercase							=		idalabel
						lowercase							=		{v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			lowercase							=		torch.hub.load_state_dict_from_url(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			lowercase							=		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
			lowercase							=		BeitForMaskedImageModeling(UpperCAmelCase       ) if has_lm_head else BeitForImageClassification(UpperCAmelCase       )
			model.eval()
			model.load_state_dict(UpperCAmelCase       )

			# Check outputs on an image
			lowercase							=		BeitImageProcessor(
			    size=config.image_size,		resample=PILImageResampling.BILINEAR,		do_center_crop=UpperCAmelCase       )
			lowercase							=		prepare_img()

			lowercase							=		image_processor(images=UpperCAmelCase,		return_tensors='''pt'''       )
			lowercase							=		encoding['''pixel_values''']

			lowercase							=		model(UpperCAmelCase       )
			lowercase							=		outputs.logits

			# verify logits
			lowercase							=		[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
			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:
									lowercase							=		'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
						else:
									lowercase							=		'''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)






from __future__ import annotations

import math






def        __UpperCAmelCase    (       UpperCAmelCase       )->       list[int]:



			"""simple docstring"""







			if num <= 0:
						lowercase							=		f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(UpperCAmelCase       )

			lowercase							=		[True] * (num + 1)
			lowercase							=		[]
			lowercase							=		2
			lowercase							=		int(math.sqrt(UpperCAmelCase       )       )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(UpperCAmelCase       )

									# Set multiples of start be False
									for i in range(start * start,		num + 1,		UpperCAmelCase       ):
												if sieve[i] is True:
															lowercase							=		False

						start += 1

			for j in range(end + 1,		num + 1       ):
						if sieve[j] is True:
									prime.append(UpperCAmelCase       )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))





