

'''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()
__snake_case			:     Tuple										=      logging.get_logger()
def 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		str,		__snake_case					:		LevitConfig,		__snake_case					:		Path,		__snake_case					:		bool = True	)	->							Any:






						"""simple docstring"""





						print(f"Converting {name}..."	)

						with torch.no_grad():
												if hidden_sizes == 128:
																		if name[-1] == "S":
																								A__					:       Tuple     =timm.create_model("""levit_128s""",		pretrained=__snake_case	)
																		else:
																								A__					:       Union[str, Any]     =timm.create_model("""levit_128""",		pretrained=__snake_case	)
												if hidden_sizes == 192:
																		A__					:       Any     =timm.create_model("""levit_192""",		pretrained=__snake_case	)
												if hidden_sizes == 256:
																		A__					:       int     =timm.create_model("""levit_256""",		pretrained=__snake_case	)
												if hidden_sizes == 384:
																		A__					:       Optional[int]     =timm.create_model("""levit_384""",		pretrained=__snake_case	)

												from_model.eval()
												A__					:       int     =LevitForImageClassificationWithTeacher(__snake_case	).eval()
												A__					:       Optional[int]     =OrderedDict()

												A__					:       str     =from_model.state_dict()
												A__					:       str     =list(from_model.state_dict().keys()	)
												A__					:       Union[str, Any]     =list(our_model.state_dict().keys()	)
												print(len(__snake_case	),		len(__snake_case	)	)
												for i in range(len(__snake_case	)	):
																		A__					:       str     =weights[og_keys[i]]
												our_model.load_state_dict(__snake_case	)

												A__					:       Any     =torch.randn((2, 3, 224, 224)	)
												A__					:       List[Any]     =from_model(__snake_case	)
												A__					:       Union[str, Any]     =our_model(__snake_case	).logits

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

						A__					:       Optional[Any]     =name
						print(__snake_case	)

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

												print(f"Pushed {checkpoint_name}"	)
def 						__lowerCamelCase	(       __snake_case					:		Path,		__snake_case					:		str = None,		__snake_case					:		bool = True	)	->							int:






						"""simple docstring"""





						A__					:       Optional[int]     ="""imagenet-1k-id2label.json"""
						A__					:       Optional[int]     =1_000
						A__					:       List[Any]     =(1, num_labels)

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

						A__					:       Optional[Any]     =idalabel
						A__					:       str     ={v: k for k, v in idalabel.items()}

						A__					:       List[str]     =partial(__snake_case,		num_labels=__snake_case,		idalabel=__snake_case,		labelaid=__snake_case	)

						A__					:       str     ={
						    """levit-128S""": 128,
						    """levit-128""": 128,
						    """levit-192""": 192,
						    """levit-256""": 256,
						    """levit-384""": 384,
						}

						A__					:       Tuple     ={
						    """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],		__snake_case,		names_to_config[model_name],		__snake_case,		__snake_case	)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name],		__snake_case,		__snake_case,		__snake_case,		__snake_case	)
						return config, expected_shape


if __name__ == "__main__":
		__snake_case			:     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',
		)

		__snake_case			:     str										=      parser.parse_args()
		__snake_case			:     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'''



import math

__snake_case			:     List[Any]										=      10
__snake_case			:     Dict										=      7
__snake_case			:     str										=      BALLS_PER_COLOUR * NUM_COLOURS
def 						__lowerCamelCase	(       __snake_case					:		int = 20	)	->							str:






						"""simple docstring"""





						A__					:       Union[str, Any]     =math.comb(__snake_case,		__snake_case	)
						A__					:       str     =math.comb(NUM_BALLS - BALLS_PER_COLOUR,		__snake_case	)

						A__					:       Optional[int]     =NUM_COLOURS * (1 - missing_colour / total)

						return f"{result:.9f}"


if __name__ == "__main__":
		print(solution(20))

