
"""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()
UpperCamelCase_       :    Optional[int]						      =			logging.get_logger()
def 				A_  (__a					, __a					, __a					, __a					, __a = True					):

					'''simple docstring'''





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

					with torch.no_grad():
										if hidden_sizes == 128:
															if name[-1] == "S":
																				A_								=    timm.create_model("levit_128s"					, pretrained=__a					)
															else:
																				A_								=    timm.create_model("levit_128"					, pretrained=__a					)
										if hidden_sizes == 192:
															A_								=    timm.create_model("levit_192"					, pretrained=__a					)
										if hidden_sizes == 256:
															A_								=    timm.create_model("levit_256"					, pretrained=__a					)
										if hidden_sizes == 384:
															A_								=    timm.create_model("levit_384"					, pretrained=__a					)

										from_model.eval()
										A_								=    LevitForImageClassificationWithTeacher(__a					).eval()
										A_								=    OrderedDict()

										A_								=    from_model.state_dict()
										A_								=    list(from_model.state_dict().keys()					)
										A_								=    list(our_model.state_dict().keys()					)
										print(len(__a					)					, len(__a					)					)
										for i in range(len(__a					)					):
															A_								=    weights[og_keys[i]]
										our_model.load_state_dict(__a					)

										A_								=    torch.randn((2, 3, 224, 224)					)
										A_								=    from_model(__a					)
										A_								=    our_model(__a					).logits

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

					A_								=    name
					print(__a					)

					if push_to_hub:
										our_model.save_pretrained(save_directory / checkpoint_name					)
										A_								=    LevitImageProcessor()
										image_processor.save_pretrained(save_directory / checkpoint_name					)

										print(f'Pushed {checkpoint_name}'					)
def 				A_  (__a					, __a = None					, __a = True					):

					'''simple docstring'''





					A_								=    "imagenet-1k-id2label.json"
					A_								=    1000
					A_								=    (1, num_labels)

					A_								=    "huggingface/label-files"
					A_								=    num_labels
					A_								=    json.load(open(hf_hub_download(__a					, __a					, repo_type="dataset"					)					, "r"					)					)
					A_								=    {int(__a					): v for k, v in idalabel.items()}

					A_								=    idalabel
					A_								=    {v: k for k, v in idalabel.items()}

					A_								=    partial(__a					, num_labels=__a					, idalabel=__a					, labelaid=__a					)

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

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


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

		UpperCamelCase_       :    Dict						      =			parser.parse_args()
		UpperCamelCase_       :    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 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




