



'''simple docstring'''

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__lowerCAmelCase						      =       {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		_a					:				List[str]				=							list(s_dict.keys()  )
		for key in keys:
				_a					:				List[Any]				=							r'.*/layers_(\d+)'
				_a					:				Union[str, Any]				=							key
				if re.match(lowerCAmelCase_						,      lowerCAmelCase_  ):
						_a					:				int				=							re.sub(r'layers_(\d+)'						,      r'block/\1/layer'						,      lowerCAmelCase_  )

				_a					:				Dict				=							r'(encoder|decoder)\/'

				if re.match(lowerCAmelCase_						,      lowerCAmelCase_  ):
						_a					:				Union[str, Any]				=							re.match(lowerCAmelCase_						,      lowerCAmelCase_  ).groups()
						if groups[0] == "encoder":
								_a					:				Dict				=							re.sub(r'/mlp/'						,      r'/1/mlp/'						,      lowerCAmelCase_  )
								_a					:				Union[str, Any]				=							re.sub(r'/pre_mlp_layer_norm/'						,      r'/1/layer_norm/'						,      lowerCAmelCase_  )

						elif groups[0] == "decoder":
								_a					:				Dict				=							re.sub(r'/mlp/'						,      r'/2/mlp/'						,      lowerCAmelCase_  )
								_a					:				Dict				=							re.sub(r'/pre_mlp_layer_norm/'						,      r'/2/layer_norm/'						,      lowerCAmelCase_  )

        # 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								_a					:				int				=							new_key.replace(lowerCAmelCase_						,      lowerCAmelCase_  )

				print(f"""{key} -> {new_key}"""  )
				_a					:				Optional[int]				=							s_dict.pop(lowerCAmelCase_  )

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				_a					:				int				=							s_dict[
				    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
				].T
		if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				_a					:				Optional[int]				=							s_dict[
				    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
				].T

		# 3. Take extra care of the EXPERTS layer
		for key in list(s_dict.keys()  ):
				if "expert" in key:
						_a					:				Any				=							s_dict[key].shape[0]
						_a					:				str				=							s_dict[key]
						for idx in range(lowerCAmelCase_  ):
								_a					:				Any				=							expert_weihts[idx]
								print(f"""{key} -> {key.replace('expert/'						,      'nested fstring'  )}"""  )

						s_dict.pop(lowerCAmelCase_  )

		return s_dict


__lowerCAmelCase						      =       {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[int]:
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(lowerCAmelCase_						,      'r'  ) as f:
				_a					:				List[str]				=							f.read()

		_a					:				Union[str, Any]				=							re.findall(r'(.*) = ([0-9.]*)'						,      lowerCAmelCase_  )
		_a					:				Tuple				=							{}
		for param, value in regex_match:
				if param in GIN_TO_CONFIG_MAPPING and value != "":
						_a					:				Union[str, Any]				=							float(lowerCAmelCase_  ) if '.' in value else int(lowerCAmelCase_  )

		_a					:				Any				=							re.findall(r'(.*activations) = \(\'(.*)\',\)'						,      lowerCAmelCase_  )[0]
		_a					:				Tuple				=							str(activation[1]  )

		_a					:				Optional[Any]				=							num_experts
		_a					:				List[Any]				=							SwitchTransformersConfig(**lowerCAmelCase_  )
		return config



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_="./"						,      lowerCAmelCase_=8  )      ->				Dict:
		# Initialise PyTorch model

		print(f"""Loading flax weights from : {flax_checkpoint_path}"""  )
		_a					:				Union[str, Any]				=							checkpoints.load_tax_checkpoint(lowerCAmelCase_  )

		if gin_file is not None:
				_a					:				int				=							convert_gin_to_config(lowerCAmelCase_						,      lowerCAmelCase_  )
		else:
				_a					:				Dict				=							SwitchTransformersConfig.from_pretrained(lowerCAmelCase_  )

		_a					:				Optional[int]				=							SwitchTransformersForConditionalGeneration(lowerCAmelCase_  )

		_a					:				Union[str, Any]				=							flax_params['target']
		_a					:				Optional[Any]				=							flatten_dict(lowerCAmelCase_						,      sep='/'  )
		_a					:				List[Any]				=							rename_keys(lowerCAmelCase_  )
		_a					:				Dict				=							unflatten_dict(lowerCAmelCase_						,      sep='/'  )

		# Load the flax params in the PT model
		load_flax_weights_in_pytorch_model(lowerCAmelCase_						,      lowerCAmelCase_  )

		print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
		pt_model.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase						      =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--switch_t5x_checkpoint_path''',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
							        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
							    ),
							)
							parser.add_argument(
							    '''--gin_file''',
							    default=None,
							    type=str,
							    required=False,
							    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
							)
							parser.add_argument(
							    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
							)
							parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
							__lowerCAmelCase						      =       parser.parse_args()
							convert_flax_checkpoint_to_pytorch(
							    args.switch_tax_checkpoint_path,
							    args.config_name,
							    args.gin_file,
							    args.pytorch_dump_folder_path,
							    args.num_experts,
							)









'''simple docstring'''

from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

__lowerCAmelCase						      =       {
    # 1536-bit
    5: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 2048-bit
    14: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AACAA68FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 3072-bit
    15: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
            + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
            + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
            + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
            + '''43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 4096-bit
    16: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
            + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
            + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
            + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
            + '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
            + '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
            + '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
            + '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
            + '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
            + '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'''
            + '''FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 6144-bit
    17: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'''
            + '''8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'''
            + '''302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'''
            + '''A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'''
            + '''49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'''
            + '''FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'''
            + '''180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'''
            + '''3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'''
            + '''04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'''
            + '''B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'''
            + '''1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'''
            + '''E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'''
            + '''99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'''
            + '''04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'''
            + '''233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'''
            + '''D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
            + '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'''
            + '''AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'''
            + '''DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'''
            + '''2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'''
            + '''F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'''
            + '''BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
            + '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'''
            + '''B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'''
            + '''387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'''
            + '''6DCC4024FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 8192-bit
    18: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
            + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
            + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
            + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
            + '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
            + '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
            + '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
            + '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
            + '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
            + '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
            + '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'''
            + '''F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'''
            + '''179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'''
            + '''DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'''
            + '''5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'''
            + '''D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'''
            + '''23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
            + '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'''
            + '''06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'''
            + '''DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'''
            + '''12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'''
            + '''38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'''
            + '''741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'''
            + '''3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'''
            + '''22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'''
            + '''4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'''
            + '''062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'''
            + '''4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'''
            + '''B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'''
            + '''4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'''
            + '''9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'''
            + '''60C980DD98EDD3DFFFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
}

class      __magic_name__						:







							def __init__(							self :	Union[str, Any]     ,_UpperCAmelCase :	int = 14		):
									if group not in primes:
											raise ValueError('Unsupported Group'		)
									_a					:				str				=							primes[group]['prime']
									_a					:				Optional[int]				=							primes[group]['generator']

									_a					:				Tuple				=							int(hexlify(urandom(32		)		)     ,base=16		)







							def 							__lowercase      (							self :	Dict		):
									return hex(self.__private_key		)[2:]







							def 							__lowercase      (							self :	List[str]		):
									_a					:				int				=							pow(self.generator     ,self.__private_key     ,self.prime		)
									return hex(_UpperCAmelCase		)[2:]







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	int		):
									# check if the other public key is valid based on NIST SP800-56
									return (
									    2 <= key <= self.prime - 2
									    and pow(_UpperCAmelCase     ,(self.prime - 1) // 2     ,self.prime		) == 1
									)







							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	str		):
									_a					:				List[Any]				=							int(_UpperCAmelCase     ,base=16		)
									if not self.is_valid_public_key(_UpperCAmelCase		):
											raise ValueError('Invalid public key'		)
									_a					:				Any				=							pow(_UpperCAmelCase     ,self.__private_key     ,self.prime		)
									return shaaaa(str(_UpperCAmelCase		).encode()		).hexdigest()







							@staticmethod
							def 							__lowercase      (							_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									# check if the other public key is valid based on NIST SP800-56
									return (
									    2 <= remote_public_key_str <= prime - 2
									    and pow(_UpperCAmelCase     ,(prime - 1) // 2     ,_UpperCAmelCase		) == 1
									)







							@staticmethod
							def 							__lowercase      (							_UpperCAmelCase :	str     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int = 14		):
									_a					:				str				=							int(_UpperCAmelCase     ,base=16		)
									_a					:				int				=							int(_UpperCAmelCase     ,base=16		)
									_a					:				Any				=							primes[group]['prime']
									if not DiffieHellman.is_valid_public_key_static(_UpperCAmelCase     ,_UpperCAmelCase		):
											raise ValueError('Invalid public key'		)
									_a					:				List[str]				=							pow(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									return shaaaa(str(_UpperCAmelCase		).encode()		).hexdigest()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




