


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= s.rsplit(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		return new.join(SCREAMING_SNAKE_CASE	)
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()	)
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= {}

		lowercase__				= ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
		for key, value in state_dict.items():
				for group_key in group_keys:
						if group_key in key:
								lowercase__				= key.replace(f'{group_key}.'		,							f'{group_key}.group.'	)

				if "res_path" in key:
						lowercase__				= key.replace('''res_path.'''		,							'''res_path.path.'''	)

				if key.endswith('''.w'''	):
						lowercase__				= rreplace(SCREAMING_SNAKE_CASE		,							'''.w'''		,							'''.weight'''		,							1	)
				if key.endswith('''.b'''	):
						lowercase__				= rreplace(SCREAMING_SNAKE_CASE		,							'''.b'''		,							'''.bias'''		,							1	)

				lowercase__				= value.float()

		return upgrade
@torch.no_grad()
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=True	):


		"""simple docstring"""




		from dall_e import Encoder

		lowercase__				= Encoder()
		if os.path.exists(SCREAMING_SNAKE_CASE	):
				lowercase__				= torch.load(SCREAMING_SNAKE_CASE	)
		else:
				lowercase__				= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE	)

		if isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):
				lowercase__				= ckpt.state_dict()
		encoder.load_state_dict(SCREAMING_SNAKE_CASE	)

		if config_path is not None:
				lowercase__				= FlavaImageCodebookConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
		else:
				lowercase__				= FlavaImageCodebookConfig()

		lowercase__				= FlavaImageCodebook(SCREAMING_SNAKE_CASE	).eval()
		lowercase__				= encoder.state_dict()

		lowercase__				= upgrade_state_dict(SCREAMING_SNAKE_CASE	)
		hf_model.load_state_dict(SCREAMING_SNAKE_CASE	)
		lowercase__				= hf_model.state_dict()
		lowercase__				= count_parameters(SCREAMING_SNAKE_CASE	)
		lowercase__				= count_parameters(SCREAMING_SNAKE_CASE	)

		assert torch.allclose(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							atol=1E-3	)

		if save_checkpoint:
				hf_model.save_pretrained(SCREAMING_SNAKE_CASE	)
		else:
				return hf_state_dict


if __name__ == "__main__":
			lowerCAmelCase			=				argparse.ArgumentParser()
			parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
			parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
			parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
			lowerCAmelCase			=				parser.parse_args()

			convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






'''simple docstring'''
from __future__ import annotations

from math import pi, sqrt


def      a (     __a					,   __a  )    ->      tuple:



					'''simple docstring'''






					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''  )

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''  )

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance  )))  ),
										)


if __name__ == "__main__":
		import doctest

		doctest.testmod()