






'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A     :      Optional[Any]   					=logging.get_logger()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = True				)				->	Optional[Any]:
							print(f'''Converting {name}...'''				)

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												lowerCamelCase__ :     str	  =	timm.create_model("""levit_128s"""    ,      pretrained=UpperCamelCase				)
																					else:
																												lowerCamelCase__ :     Union[str, Any]	  =	timm.create_model("""levit_128"""    ,      pretrained=UpperCamelCase				)
														if hidden_sizes == 192:
																					lowerCamelCase__ :     Dict	  =	timm.create_model("""levit_192"""    ,      pretrained=UpperCamelCase				)
														if hidden_sizes == 256:
																					lowerCamelCase__ :     List[str]	  =	timm.create_model("""levit_256"""    ,      pretrained=UpperCamelCase				)
														if hidden_sizes == 384:
																					lowerCamelCase__ :     List[Any]	  =	timm.create_model("""levit_384"""    ,      pretrained=UpperCamelCase				)

														from_model.eval()
														lowerCamelCase__ :     int	  =	LevitForImageClassificationWithTeacher(UpperCamelCase				).eval()
														lowerCamelCase__ :     str	  =	OrderedDict()

														lowerCamelCase__ :     Optional[int]	  =	from_model.state_dict()
														lowerCamelCase__ :     str	  =	list(from_model.state_dict().keys()				)
														lowerCamelCase__ :     List[str]	  =	list(our_model.state_dict().keys()				)
														print(len(UpperCamelCase				)    ,      len(UpperCamelCase				)				)
														for i in range(len(UpperCamelCase				)				):
																					lowerCamelCase__ :     Union[str, Any]	  =	weights[og_keys[i]]
														our_model.load_state_dict(UpperCamelCase				)

														lowerCamelCase__ :     Dict	  =	torch.randn((2, 3, 224, 224)				)
														lowerCamelCase__ :     Tuple	  =	from_model(UpperCamelCase				)
														lowerCamelCase__ :     Union[str, Any]	  =	our_model(UpperCamelCase				).logits

							assert torch.allclose(UpperCamelCase    ,      UpperCamelCase				), "The model logits don't match the original one."

							lowerCamelCase__ :     Any	  =	name
							print(UpperCamelCase				)

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name				)
														lowerCamelCase__ :     Optional[Any]	  =	LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name				)

														print(f'''Pushed {checkpoint_name}'''				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = None    ,      UpperCamelCase = True				)				->	Optional[Any]:
							lowerCamelCase__ :     str	  =	"""imagenet-1k-id2label.json"""
							lowerCamelCase__ :     List[str]	  =	1000
							lowerCamelCase__ :     Dict	  =	(1, num_labels)

							lowerCamelCase__ :     Optional[int]	  =	"""huggingface/label-files"""
							lowerCamelCase__ :     str	  =	num_labels
							lowerCamelCase__ :     Dict	  =	json.load(open(hf_hub_download(UpperCamelCase    ,      UpperCamelCase    ,      repo_type="""dataset"""				)    ,      """r"""				)				)
							lowerCamelCase__ :     List[str]	  =	{int(UpperCamelCase				): v for k, v in idalabel.items()}

							lowerCamelCase__ :     int	  =	idalabel
							lowerCamelCase__ :     List[Any]	  =	{v: k for k, v in idalabel.items()}

							lowerCamelCase__ :     int	  =	partial(UpperCamelCase    ,      num_labels=UpperCamelCase    ,      idalabel=UpperCamelCase    ,      labelaid=UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	{
							    """levit-128S""": 128,
							    """levit-128""": 128,
							    """levit-192""": 192,
							    """levit-256""": 256,
							    """levit-384""": 384,
							}

							lowerCamelCase__ :     List[Any]	  =	{
							    """levit-128S""": ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,      num_attention_heads=[4, 6, 8]    ,      depths=[2, 3, 4]    ,      key_dim=[16, 16, 16]    ,      drop_path_rate=0    ,      ),
							    """levit-128""": ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,      num_attention_heads=[4, 8, 12]    ,      depths=[4, 4, 4]    ,      key_dim=[16, 16, 16]    ,      drop_path_rate=0    ,      ),
							    """levit-192""": ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]    ,      num_attention_heads=[3, 5, 6]    ,      depths=[4, 4, 4]    ,      key_dim=[32, 32, 32]    ,      drop_path_rate=0    ,      ),
							    """levit-256""": ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]    ,      num_attention_heads=[4, 6, 8]    ,      depths=[4, 4, 4]    ,      key_dim=[32, 32, 32]    ,      drop_path_rate=0    ,      ),
							    """levit-384""": ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]    ,      num_attention_heads=[6, 9, 12]    ,      depths=[4, 4, 4]    ,      key_dim=[32, 32, 32]    ,      drop_path_rate=0.1    ,      ),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]    ,      UpperCamelCase    ,      names_to_config[model_name]    ,      UpperCamelCase    ,      UpperCamelCase				)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							return config, expected_shape


if __name__ == "__main__":
			_A     :      List[str]   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default=None,
			    type=str,
			    help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''levit-dump-folder/''',
			    type=Path,
			    required=False,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
			parser.add_argument(
			    '''--no-push_to_hub''',
			    dest='''push_to_hub''',
			    action='''store_false''',
			    help='''Do not push model and image processor to the hub''',
			)

			_A     :      List[Any]   					=parser.parse_args()
			_A     :      Path   					=args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

