



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     s.rsplit(__UpperCamelCase			,						__UpperCamelCase       )
			return new.join(__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   Dict:
			# encoder.embeddings are double copied in original FLAVA
			return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()       )


def A    (							__UpperCamelCase       )    ->   List[str]:
			A__	  =     {}

			A__	  =     ['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:
												A__	  =     key.replace(f'''{group_key}.'''			,						f'''{group_key}.group.'''       )

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

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

						A__	  =     value.float()

			return upgrade


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=True       )    ->   Optional[Any]:
			from dall_e import Encoder

			A__	  =     Encoder()
			if os.path.exists(__UpperCamelCase       ):
						A__	  =     torch.load(__UpperCamelCase       )
			else:
						A__	  =     torch.hub.load_state_dict_from_url(__UpperCamelCase       )

			if isinstance(__UpperCamelCase			,						__UpperCamelCase       ):
						A__	  =     ckpt.state_dict()
			encoder.load_state_dict(__UpperCamelCase       )

			if config_path is not None:
						A__	  =     FlavaImageCodebookConfig.from_pretrained(__UpperCamelCase       )
			else:
						A__	  =     FlavaImageCodebookConfig()

			A__	  =     FlavaImageCodebook(__UpperCamelCase       ).eval()
			A__	  =     encoder.state_dict()

			A__	  =     upgrade_state_dict(__UpperCamelCase       )
			hf_model.load_state_dict(__UpperCamelCase       )
			A__	  =     hf_model.state_dict()
			A__	  =     count_parameters(__UpperCamelCase       )
			A__	  =     count_parameters(__UpperCamelCase       )

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

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









from __future__ import annotations

from fractions import Fraction


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   bool:
			return (
			    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
			)


def A    (							__UpperCamelCase       )    ->   list[str]:
			A__	  =     []
			A__	  =     11
			A__	  =     int('1' + '0' * digit_len       )
			for num in range(__UpperCamelCase			,						__UpperCamelCase       ):
						while den <= 99:
									if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
												if is_digit_cancelling(__UpperCamelCase			,						__UpperCamelCase       ):
															solutions.append(f'''{num}/{den}'''       )
									den += 1
						num += 1
						A__	  =     10
			return solutions


def A    (							__UpperCamelCase = 2       )    ->   int:
			A__	  =     1.0
			for fraction in fraction_list(__UpperCamelCase       ):
						A__	  =     Fraction(__UpperCamelCase       )
						result *= frac.denominator / frac.numerator
			return int(__UpperCamelCase       )


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




