



'''simple docstring'''

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							__lowerCAmelCase						      =       '''0.12'''  # assumed parallelism: 8


if is_torch_available():
							import torch



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None  )      ->				Union[str, Any]:
		if rng is None:
				_a					:				List[str]				=							random.Random()

		_a					:				Optional[Any]				=							1
		for dim in shape:
				total_dims *= dim

		_a					:				Optional[int]				=							[]
		for _ in range(lowerCAmelCase_  ):
				values.append(rng.randint(0						,      vocab_size - 1  )  )

		_a					:				Any				=							np.array(lowerCAmelCase_						,      dtype=jnp.intaa  ).reshape(lowerCAmelCase_  )

		return output



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=None  )      ->				Optional[Any]:
		_a					:				Union[str, Any]				=							ids_tensor(lowerCAmelCase_						,      vocab_size=2						,      rng=lowerCAmelCase_  )
		# make sure that at least one token is attended to for each batch
		_a					:				int				=							1
		return attn_mask

@require_flax
class      __magic_name__						:
							lowerCAmelCase	:   Any           = None
							lowerCAmelCase	:   List[Any]           = ()







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a	, _a					:				List[str]				=							self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									_a					:				str				=							2
									_a					:				Union[str, Any]				=							inputs['input_ids'].shape[-1] // 2
									_a					:				List[Any]				=							inputs['input_ids'][:max_batch_size, :sequence_length]

									_a					:				Dict				=							jnp.ones_like(_UpperCAmelCase		)
									_a					:				List[Any]				=							attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									_a					:				Union[str, Any]				=							input_ids.shape[-1] + 5
									if config.eos_token_id is not None and config.pad_token_id is None:
											# hack to allow generate for models such as GPT2 as is done in `generate()`
											_a					:				List[str]				=							config.eos_token_id
									return config, input_ids, attention_mask, max_length







							@is_pt_flax_cross_test
							def 							__lowercase      (							self :	Optional[int]		):
									_a	, _a	, _a	, _a					:				Union[str, Any]				=							self._get_input_ids_and_config()
									_a					:				Union[str, Any]				=							False
									_a					:				List[Any]				=							max_length
									_a					:				Tuple				=							0

									for model_class in self.all_generative_model_classes:
											_a					:				Dict				=							model_class(_UpperCAmelCase		)

											_a					:				List[str]				=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
											_a					:				Tuple				=							getattr(_UpperCAmelCase     ,_UpperCAmelCase		)
											_a					:				str				=							pt_model_class(_UpperCAmelCase		).eval()
											_a					:				int				=							load_flax_weights_in_pytorch_model(_UpperCAmelCase     ,flax_model.params		)

											_a					:				List[Any]				=							flax_model.generate(_UpperCAmelCase		).sequences
											_a					:				Optional[Any]				=							pt_model.generate(torch.tensor(_UpperCAmelCase     ,dtype=torch.long		)		)

											if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
													_a					:				Tuple				=							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

											self.assertListEqual(pt_generation_outputs.numpy().tolist()     ,flax_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Any		):
									_a	, _a	, _a	, _a					:				Optional[Any]				=							self._get_input_ids_and_config()
									_a					:				Tuple				=							False
									_a					:				List[str]				=							max_length

									for model_class in self.all_generative_model_classes:
											_a					:				str				=							model_class(_UpperCAmelCase		)

											_a					:				Optional[Any]				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				int				=							jit(model.generate		)
											_a					:				List[Any]				=							jit_generate(_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a	, _a	, _a	, _a					:				Any				=							self._get_input_ids_and_config()
									_a					:				Tuple				=							True
									_a					:				List[Any]				=							max_length

									for model_class in self.all_generative_model_classes:
											_a					:				List[str]				=							model_class(_UpperCAmelCase		)

											_a					:				Tuple				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				Optional[Any]				=							jit(model.generate		)
											_a					:				Any				=							jit_generate(_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	int		):
									_a	, _a	, _a	, _a					:				List[Any]				=							self._get_input_ids_and_config()
									_a					:				Any				=							False
									_a					:				str				=							max_length
									_a					:				Dict				=							2

									for model_class in self.all_generative_model_classes:
											_a					:				List[Any]				=							model_class(_UpperCAmelCase		)

											_a					:				str				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				int				=							jit(model.generate		)
											_a					:				Tuple				=							jit_generate(_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Dict		):
									_a	, _a	, _a	, _a					:				str				=							self._get_input_ids_and_config()
									_a					:				int				=							False
									_a					:				Optional[Any]				=							max_length
									_a					:				Union[str, Any]				=							2
									_a					:				str				=							2

									for model_class in self.all_generative_model_classes:
											_a					:				Any				=							model_class(_UpperCAmelCase		)

											_a					:				int				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[0]     ,input_ids.shape[0] * config.num_return_sequences		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a	, _a	, _a	, _a					:				Tuple				=							self._get_input_ids_and_config()
									_a					:				Optional[Any]				=							True
									_a					:				int				=							max_length
									_a					:				List[Any]				=							0.8
									_a					:				Dict				=							10
									_a					:				Union[str, Any]				=							0.3
									_a					:				Tuple				=							1
									_a					:				List[str]				=							8
									_a					:				Union[str, Any]				=							9

									for model_class in self.all_generative_model_classes:
											_a					:				Dict				=							model_class(_UpperCAmelCase		)

											_a					:				Tuple				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				List[str]				=							jit(model.generate		)
											_a					:				Tuple				=							jit_generate(_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a	, _a	, _a	, _a					:				Tuple				=							self._get_input_ids_and_config()
									_a					:				int				=							max_length
									_a					:				Dict				=							1
									_a					:				Union[str, Any]				=							8
									_a					:				int				=							9

									for model_class in self.all_generative_model_classes:
											_a					:				str				=							model_class(_UpperCAmelCase		)

											_a					:				Tuple				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				Any				=							jit(model.generate		)
											_a					:				Optional[Any]				=							jit_generate(_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Tuple		):
									_a	, _a	, _a	, _a					:				Dict				=							self._get_input_ids_and_config()
									_a					:				Optional[int]				=							max_length
									_a					:				Tuple				=							2
									_a					:				Tuple				=							1
									_a					:				int				=							8
									_a					:				int				=							9

									for model_class in self.all_generative_model_classes:
											_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)

											_a					:				Dict				=							model.generate(_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				Optional[Any]				=							jit(model.generate		)
											_a					:				Dict				=							jit_generate(_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Dict		):
									_a	, _a	, _a	, _a					:				Any				=							self._get_input_ids_and_config()

									# pad attention mask on the left
									_a					:				Any				=							attention_mask.at[(0, 0)].set(0		)

									_a					:				Optional[int]				=							False
									_a					:				int				=							max_length

									for model_class in self.all_generative_model_classes:
											_a					:				str				=							model_class(_UpperCAmelCase		)

											_a					:				List[str]				=							model.generate(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				Dict				=							jit(model.generate		)
											_a					:				Optional[int]				=							jit_generate(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Tuple		):
									_a	, _a	, _a	, _a					:				str				=							self._get_input_ids_and_config()

									# pad attention mask on the left
									_a					:				Optional[int]				=							attention_mask.at[(0, 0)].set(0		)

									_a					:				Optional[int]				=							True
									_a					:				List[Any]				=							max_length

									for model_class in self.all_generative_model_classes:
											_a					:				Tuple				=							model_class(_UpperCAmelCase		)

											_a					:				int				=							model.generate(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				Any				=							jit(model.generate		)
											_a					:				Dict				=							jit_generate(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a	, _a	, _a	, _a					:				Dict				=							self._get_input_ids_and_config()

									# pad attention mask on the left
									_a					:				Optional[int]				=							attention_mask.at[(0, 0)].set(0		)

									_a					:				Dict				=							2
									_a					:				Tuple				=							max_length

									for model_class in self.all_generative_model_classes:
											_a					:				List[str]				=							model_class(_UpperCAmelCase		)

											_a					:				Optional[int]				=							model.generate(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).sequences
											self.assertEqual(generation_outputs.shape[-1]     ,_UpperCAmelCase		)

											_a					:				Optional[Any]				=							jit(model.generate		)
											_a					:				Any				=							jit_generate(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).sequences

											self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist()		)

@require_flax
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	int		):
									_a					:				Union[str, Any]				=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'		)
									_a					:				str				=							FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'		)

									_a					:				Any				=							'Hello world'
									_a					:				Union[str, Any]				=							tokenizer(_UpperCAmelCase     ,return_tensors='np'		).input_ids

									# typos are quickly detected (the correct argument is `do_sample`)
									with self.assertRaisesRegex(_UpperCAmelCase     ,'do_samples'		):
											model.generate(_UpperCAmelCase     ,do_samples=_UpperCAmelCase		)

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(_UpperCAmelCase     ,'foo'		):
											_a					:				List[str]				=							{'foo': 'bar'}
											model.generate(_UpperCAmelCase     ,**_UpperCAmelCase		)









'''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()




