
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()
lowercase   =	logging.get_logger()



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   LevitConfig      ,					UpperCAmelCase__					:   Path      ,					UpperCAmelCase__					:   bool = True							)	->     Optional[Any]:
							print(F'''Converting {name}...'''							)

							with torch.no_grad():
														if hidden_sizes == 1_2_8:
																					if name[-1] == "S":
																												lowerCamelCase_					=       timm.create_model("""levit_128s"""      ,					pretrained=UpperCAmelCase__							)
																					else:
																												lowerCamelCase_					=       timm.create_model("""levit_128"""      ,					pretrained=UpperCAmelCase__							)
														if hidden_sizes == 1_9_2:
																					lowerCamelCase_					=       timm.create_model("""levit_192"""      ,					pretrained=UpperCAmelCase__							)
														if hidden_sizes == 2_5_6:
																					lowerCamelCase_					=       timm.create_model("""levit_256"""      ,					pretrained=UpperCAmelCase__							)
														if hidden_sizes == 3_8_4:
																					lowerCamelCase_					=       timm.create_model("""levit_384"""      ,					pretrained=UpperCAmelCase__							)

														from_model.eval()
														lowerCamelCase_					=       LevitForImageClassificationWithTeacher(UpperCAmelCase__							).eval()
														lowerCamelCase_					=       OrderedDict()

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

														lowerCamelCase_					=       torch.randn((2, 3, 2_2_4, 2_2_4)							)
														lowerCamelCase_					=       from_model(UpperCAmelCase__							)
														lowerCamelCase_					=       our_model(UpperCAmelCase__							).logits

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

							lowerCamelCase_					=       name
							print(UpperCAmelCase__							)

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

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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Path      ,					UpperCAmelCase__					:   str = None      ,					UpperCAmelCase__					:   bool = True							)	->     str:
							lowerCamelCase_					=       """imagenet-1k-id2label.json"""
							lowerCamelCase_					=       1_0_0_0
							lowerCamelCase_					=       (1, num_labels)

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

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

							lowerCamelCase_					=       partial(UpperCAmelCase__      ,					num_labels=UpperCAmelCase__      ,					idalabel=UpperCAmelCase__      ,					labelaid=UpperCAmelCase__							)

							lowerCamelCase_					=       {
							    """levit-128S""": 1_2_8,
							    """levit-128""": 1_2_8,
							    """levit-192""": 1_9_2,
							    """levit-256""": 2_5_6,
							    """levit-384""": 3_8_4,
							}

							lowerCamelCase_					=       {
							    """levit-128S""": ImageNetPreTrainedConfig(
							        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]      ,					num_attention_heads=[4, 6, 8]      ,					depths=[2, 3, 4]      ,					key_dim=[1_6, 1_6, 1_6]      ,					drop_path_rate=0      ,					),
							    """levit-128""": ImageNetPreTrainedConfig(
							        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]      ,					num_attention_heads=[4, 8, 1_2]      ,					depths=[4, 4, 4]      ,					key_dim=[1_6, 1_6, 1_6]      ,					drop_path_rate=0      ,					),
							    """levit-192""": ImageNetPreTrainedConfig(
							        hidden_sizes=[1_9_2, 2_8_8, 3_8_4]      ,					num_attention_heads=[3, 5, 6]      ,					depths=[4, 4, 4]      ,					key_dim=[3_2, 3_2, 3_2]      ,					drop_path_rate=0      ,					),
							    """levit-256""": ImageNetPreTrainedConfig(
							        hidden_sizes=[2_5_6, 3_8_4, 5_1_2]      ,					num_attention_heads=[4, 6, 8]      ,					depths=[4, 4, 4]      ,					key_dim=[3_2, 3_2, 3_2]      ,					drop_path_rate=0      ,					),
							    """levit-384""": ImageNetPreTrainedConfig(
							        hidden_sizes=[3_8_4, 5_1_2, 7_6_8]      ,					num_attention_heads=[6, 9, 1_2]      ,					depths=[4, 4, 4]      ,					key_dim=[3_2, 3_2, 3_2]      ,					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__":
		lowercase   =	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''',
		)

		lowercase   =	parser.parse_args()
		lowercase   =	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)







lowercase   =	8.314_4598



def   __lowerCAmelCase    (				UpperCAmelCase__					:   float      ,					UpperCAmelCase__					:   float							)	->     float:
							if temperature < 0:
														raise Exception("""Temperature cannot be less than 0 K"""							)
							if molar_mass <= 0:
														raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		lowercase   =	3_0_0
		lowercase   =	2_8
		lowercase   =	rms_speed_of_molecule(temperature, molar_mass)
		print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





