





"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =0
							__lowerCamelCase      :      Any         =number
							while duplicate > 0:
														__lowerCamelCase    ,						__lowerCamelCase      :      List[Any]         =divmod(_UpperCAmelCase    ,						10   )
														fact_sum += factorial(_UpperCAmelCase   )
							return fact_sum == number


if __name__ == "__main__":
		print('Program to check whether a number is a Krisnamurthy Number or not.')
		_UpperCamelCase							=      int(input('Enter number: ').strip())
		print(
		    f'''{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'''
		)











"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =DPTConfig()

							if "large" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =1024
														__lowerCamelCase      :      Dict         =4096
														__lowerCamelCase      :      Tuple         =24
														__lowerCamelCase      :      str         =16
														__lowerCamelCase      :      Optional[Any]         =[5, 11, 17, 23]
														__lowerCamelCase      :      List[Any]         =[256, 512, 1024, 1024]
														__lowerCamelCase      :      List[str]         =(1, 384, 384)

							if "ade" in checkpoint_url:
														__lowerCamelCase      :      List[Any]         =True

														__lowerCamelCase      :      Union[str, Any]         =150
														__lowerCamelCase      :      Any         ='''huggingface/label-files'''
														__lowerCamelCase      :      List[str]         ='''ade20k-id2label.json'''
														__lowerCamelCase      :      Optional[Any]         =json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						repo_type='''dataset'''   )   )    ,						'''r'''   )   )
														__lowerCamelCase      :      Union[str, Any]         ={int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
														__lowerCamelCase      :      List[Any]         =idalabel
														__lowerCamelCase      :      Dict         ={v: k for k, v in idalabel.items()}
														__lowerCamelCase      :      str         =[1, 150, 480, 480]

							return config, expected_shape



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							if (
							    "pretrained.model" in name
							    and "cls_token" not in name
							    and "pos_embed" not in name
							    and "patch_embed" not in name
							):
														__lowerCamelCase      :      str         =name.replace('''pretrained.model'''    ,						'''dpt.encoder'''   )
							if "pretrained.model" in name:
														__lowerCamelCase      :      str         =name.replace('''pretrained.model'''    ,						'''dpt.embeddings'''   )
							if "patch_embed" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''patch_embed'''    ,						'''patch_embeddings'''   )
							if "pos_embed" in name:
														__lowerCamelCase      :      str         =name.replace('''pos_embed'''    ,						'''position_embeddings'''   )
							if "attn.proj" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''attn.proj'''    ,						'''attention.output.dense'''   )
							if "proj" in name and "project" not in name:
														__lowerCamelCase      :      Any         =name.replace('''proj'''    ,						'''projection'''   )
							if "blocks" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''blocks'''    ,						'''layer'''   )
							if "mlp.fc1" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''mlp.fc1'''    ,						'''intermediate.dense'''   )
							if "mlp.fc2" in name:
														__lowerCamelCase      :      Optional[Any]         =name.replace('''mlp.fc2'''    ,						'''output.dense'''   )
							if "norm1" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''norm1'''    ,						'''layernorm_before'''   )
							if "norm2" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''norm2'''    ,						'''layernorm_after'''   )
							if "scratch.output_conv" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''scratch.output_conv'''    ,						'''head'''   )
							if "scratch" in name:
														__lowerCamelCase      :      Optional[int]         =name.replace('''scratch'''    ,						'''neck'''   )
							if "layer1_rn" in name:
														__lowerCamelCase      :      str         =name.replace('''layer1_rn'''    ,						'''convs.0'''   )
							if "layer2_rn" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''layer2_rn'''    ,						'''convs.1'''   )
							if "layer3_rn" in name:
														__lowerCamelCase      :      Any         =name.replace('''layer3_rn'''    ,						'''convs.2'''   )
							if "layer4_rn" in name:
														__lowerCamelCase      :      int         =name.replace('''layer4_rn'''    ,						'''convs.3'''   )
							if "refinenet" in name:
														__lowerCamelCase      :      int         =int(name[len('''neck.refinenet'''   ) : len('''neck.refinenet'''   ) + 1]   )
														# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
														__lowerCamelCase      :      Dict         =name.replace(F'refinenet{layer_idx}'    ,						F'fusion_stage.layers.{abs(layer_idx-4   )}'   )
							if "out_conv" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''out_conv'''    ,						'''projection'''   )
							if "resConfUnit1" in name:
														__lowerCamelCase      :      Optional[Any]         =name.replace('''resConfUnit1'''    ,						'''residual_layer1'''   )
							if "resConfUnit2" in name:
														__lowerCamelCase      :      str         =name.replace('''resConfUnit2'''    ,						'''residual_layer2'''   )
							if "conv1" in name:
														__lowerCamelCase      :      Any         =name.replace('''conv1'''    ,						'''convolution1'''   )
							if "conv2" in name:
														__lowerCamelCase      :      Optional[int]         =name.replace('''conv2'''    ,						'''convolution2'''   )
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														__lowerCamelCase      :      Dict         =name.replace('''pretrained.act_postprocess1.0.project.0'''    ,						'''neck.reassemble_stage.readout_projects.0.0'''   )
							if "pretrained.act_postprocess2.0.project.0" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''pretrained.act_postprocess2.0.project.0'''    ,						'''neck.reassemble_stage.readout_projects.1.0'''   )
							if "pretrained.act_postprocess3.0.project.0" in name:
														__lowerCamelCase      :      str         =name.replace('''pretrained.act_postprocess3.0.project.0'''    ,						'''neck.reassemble_stage.readout_projects.2.0'''   )
							if "pretrained.act_postprocess4.0.project.0" in name:
														__lowerCamelCase      :      Optional[Any]         =name.replace('''pretrained.act_postprocess4.0.project.0'''    ,						'''neck.reassemble_stage.readout_projects.3.0'''   )
							# resize blocks
							if "pretrained.act_postprocess1.3" in name:
														__lowerCamelCase      :      List[Any]         =name.replace('''pretrained.act_postprocess1.3'''    ,						'''neck.reassemble_stage.layers.0.projection'''   )
							if "pretrained.act_postprocess1.4" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''pretrained.act_postprocess1.4'''    ,						'''neck.reassemble_stage.layers.0.resize'''   )
							if "pretrained.act_postprocess2.3" in name:
														__lowerCamelCase      :      List[Any]         =name.replace('''pretrained.act_postprocess2.3'''    ,						'''neck.reassemble_stage.layers.1.projection'''   )
							if "pretrained.act_postprocess2.4" in name:
														__lowerCamelCase      :      List[Any]         =name.replace('''pretrained.act_postprocess2.4'''    ,						'''neck.reassemble_stage.layers.1.resize'''   )
							if "pretrained.act_postprocess3.3" in name:
														__lowerCamelCase      :      Tuple         =name.replace('''pretrained.act_postprocess3.3'''    ,						'''neck.reassemble_stage.layers.2.projection'''   )
							if "pretrained.act_postprocess4.3" in name:
														__lowerCamelCase      :      int         =name.replace('''pretrained.act_postprocess4.3'''    ,						'''neck.reassemble_stage.layers.3.projection'''   )
							if "pretrained.act_postprocess4.4" in name:
														__lowerCamelCase      :      int         =name.replace('''pretrained.act_postprocess4.4'''    ,						'''neck.reassemble_stage.layers.3.resize'''   )
							if "pretrained" in name:
														__lowerCamelCase      :      Dict         =name.replace('''pretrained'''    ,						'''dpt'''   )
							if "bn" in name:
														__lowerCamelCase      :      str         =name.replace('''bn'''    ,						'''batch_norm'''   )
							if "head" in name:
														__lowerCamelCase      :      str         =name.replace('''head'''    ,						'''head.head'''   )
							if "encoder.norm" in name:
														__lowerCamelCase      :      Union[str, Any]         =name.replace('''encoder.norm'''    ,						'''layernorm'''   )
							if "auxlayer" in name:
														__lowerCamelCase      :      List[str]         =name.replace('''auxlayer'''    ,						'''auxiliary_head.head'''   )

							return name



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							for i in range(config.num_hidden_layers   ):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__lowerCamelCase      :      int         =state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'   )
														__lowerCamelCase      :      Union[str, Any]         =state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'   )
														# next, add query, keys and values (in that order) to the state dict
														__lowerCamelCase      :      Tuple         =in_proj_weight[: config.hidden_size, :]
														__lowerCamelCase      :      List[str]         =in_proj_bias[: config.hidden_size]
														__lowerCamelCase      :      Optional[Any]         =in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowerCamelCase      :      Dict         =in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__lowerCamelCase      :      List[Any]         =in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowerCamelCase      :      Union[str, Any]         =in_proj_bias[-config.hidden_size :]



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase      :      Optional[Any]         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =get_dpt_config(SCREAMING_SNAKE_CASE   )
							# load original state_dict from URL
							__lowerCamelCase      :      List[str]         =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,						map_location='''cpu'''   )
							# remove certain keys
							remove_ignore_keys_(SCREAMING_SNAKE_CASE   )
							# rename keys
							for key in state_dict.copy().keys():
														__lowerCamelCase      :      str         =state_dict.pop(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      Any         =val
							# read in qkv matrices
							read_in_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# load HuggingFace model
							__lowerCamelCase      :      Dict         =DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE   ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE   )
							model.load_state_dict(SCREAMING_SNAKE_CASE   )
							model.eval()

							# Check outputs on an image
							__lowerCamelCase      :      Union[str, Any]         =480 if '''ade''' in checkpoint_url else 384
							__lowerCamelCase      :      Dict         =DPTImageProcessor(size=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[int]         =prepare_img()
							__lowerCamelCase      :      int         =image_processor(SCREAMING_SNAKE_CASE    ,						return_tensors='''pt'''   )

							# forward pass
							__lowerCamelCase      :      Tuple         =model(**SCREAMING_SNAKE_CASE   ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE   ).predicted_depth

							# Assert logits
							__lowerCamelCase      :      Optional[Any]         =torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]   )
							if "ade" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]   )
							assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE   )
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-4   )
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]    ,						SCREAMING_SNAKE_CASE   )
							)

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

							if push_to_hub:
														print('''Pushing model to hub...'''   )
														model.push_to_hub(
														    repo_path_or_name=Path(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )    ,						organization='''nielsr'''    ,						commit_message='''Add model'''    ,						use_temp_dir=SCREAMING_SNAKE_CASE    ,						)
														image_processor.push_to_hub(
														    repo_path_or_name=Path(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )    ,						organization='''nielsr'''    ,						commit_message='''Add image processor'''    ,						use_temp_dir=SCREAMING_SNAKE_CASE    ,						)


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint_url',
							    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
							    type=str,
							    help='URL of the original DPT checkpoint you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							)
							parser.add_argument(
							    '--model_name',
							    default='dpt-large',
							    type=str,
							    help='Name of the model, in case you\'re pushing to the hub.',
							)

							_UpperCamelCase							=      parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




