"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			__lowerCAmelCase    :							Any													=						"platform"

			import jax
			import jax.numpy as jnp

			from transformers import BlenderbotTokenizer
			from transformers.models.blenderbot.modeling_flax_blenderbot import (
			    FlaxBlenderbotForConditionalGeneration,
			    FlaxBlenderbotModel,
			    shift_tokens_right,
			)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  ):





		"""simple docstring"""




		if attention_mask is None:
				lowerCAmelCase__        =   np.where(input_ids != config.pad_token_id			,  1			,  0     )
		if decoder_attention_mask is None:
				lowerCAmelCase__        =   np.where(decoder_input_ids != config.pad_token_id			,  1			,  0     )
		if head_mask is None:
				lowerCAmelCase__        =   np.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				lowerCAmelCase__        =   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				lowerCAmelCase__        =   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     List[Any]=13  ,						snake_case__						:     List[str]=7  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     Optional[int]=False  ,						snake_case__						:     Any=99  ,						snake_case__						:     List[str]=16  ,						snake_case__						:     Dict=2  ,						snake_case__						:     Union[str, Any]=4  ,						snake_case__						:     Dict=4  ,						snake_case__						:     Dict="gelu"  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     List[Any]=0.1  ,						snake_case__						:     Any=32  ,						snake_case__						:     Tuple=2  ,						snake_case__						:     List[str]=1  ,						snake_case__						:     Dict=0  ,						snake_case__						:     Optional[int]=0.02  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   seq_length
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   max_position_embeddings
							lowerCAmelCase__        =   eos_token_id
							lowerCAmelCase__        =   pad_token_id
							lowerCAmelCase__        =   bos_token_id
							lowerCAmelCase__        =   initializer_range




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,						self.vocab_size     )  ,						3  ,						self.vocab_size     )
							lowerCAmelCase__        =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,						dtype=np.intaa     ))  ,						-1     )

							lowerCAmelCase__        =   shift_tokens_right(snake_case__  ,						1  ,						2     )

							lowerCAmelCase__        =   BlenderbotConfig(
							    vocab_size=self.vocab_size  ,						d_model=self.hidden_size  ,						encoder_layers=self.num_hidden_layers  ,						decoder_layers=self.num_hidden_layers  ,						encoder_attention_heads=self.num_attention_heads  ,						decoder_attention_heads=self.num_attention_heads  ,						encoder_ffn_dim=self.intermediate_size  ,						decoder_ffn_dim=self.intermediate_size  ,						dropout=self.hidden_dropout_prob  ,						attention_dropout=self.attention_probs_dropout_prob  ,						max_position_embeddings=self.max_position_embeddings  ,						eos_token_id=self.eos_token_id  ,						bos_token_id=self.bos_token_id  ,						pad_token_id=self.pad_token_id  ,						initializer_range=self.initializer_range  ,						use_cache=snake_case__  ,						)
							lowerCAmelCase__        =   prepare_blenderbot_inputs_dict(snake_case__  ,						snake_case__  ,						snake_case__     )
							return config, inputs_dict




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.prepare_config_and_inputs()
							return config, inputs_dict




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   20
							lowerCAmelCase__        =   model_class_name(snake_case__     )

							lowerCAmelCase__        =   model.encode(inputs_dict["""input_ids"""]     )

							lowerCAmelCase__   ,					lowerCAmelCase__        =   (
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							lowerCAmelCase__        =   model.init_cache(decoder_input_ids.shape[0]  ,						snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,						dtype="""i4"""     )

							lowerCAmelCase__        =   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]  ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,						)
							lowerCAmelCase__        =   model.decode(
							    decoder_input_ids[:, :-1]  ,						snake_case__  ,						decoder_attention_mask=snake_case__  ,						past_key_values=snake_case__  ,						decoder_position_ids=snake_case__  ,						)

							lowerCAmelCase__        =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,						dtype="""i4"""     )
							lowerCAmelCase__        =   model.decode(
							    decoder_input_ids[:, -1:]  ,						snake_case__  ,						decoder_attention_mask=snake_case__  ,						past_key_values=outputs_cache.past_key_values  ,						decoder_position_ids=snake_case__  ,						)

							lowerCAmelCase__        =   model.decode(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
							self.parent.assertTrue(diff < 1E-3  ,						msg=F"""Max diff is {diff}"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   20
							lowerCAmelCase__        =   model_class_name(snake_case__     )

							lowerCAmelCase__        =   model.encode(inputs_dict["""input_ids"""]     )

							lowerCAmelCase__   ,					lowerCAmelCase__        =   (
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							lowerCAmelCase__        =   jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
							    ]  ,						axis=-1  ,						)

							lowerCAmelCase__        =   model.init_cache(decoder_input_ids.shape[0]  ,						snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]  ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,						)

							lowerCAmelCase__        =   model.decode(
							    decoder_input_ids[:, :-1]  ,						snake_case__  ,						decoder_attention_mask=snake_case__  ,						past_key_values=snake_case__  ,						decoder_position_ids=snake_case__  ,						)
							lowerCAmelCase__        =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,						dtype="""i4"""     )
							lowerCAmelCase__        =   model.decode(
							    decoder_input_ids[:, -1:]  ,						snake_case__  ,						past_key_values=outputs_cache.past_key_values  ,						decoder_attention_mask=snake_case__  ,						decoder_position_ids=snake_case__  ,						)

							lowerCAmelCase__        =   model.decode(snake_case__  ,						snake_case__  ,						decoder_attention_mask=snake_case__     )

							lowerCAmelCase__        =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
							self.parent.assertTrue(diff < 1E-3  ,						msg=F"""Max diff is {diff}"""     )

@require_flax
class       a_      (     unittest.TestCase			):
					UpperCamelCase_ :	int			=    99




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]  ,						dtype=np.intaa  ,						)

							lowerCAmelCase__        =   input_ids.shape[0]
							lowerCAmelCase__        =   BlenderbotConfig(
							    vocab_size=self.vocab_size  ,						d_model=24  ,						encoder_layers=2  ,						decoder_layers=2  ,						encoder_attention_heads=2  ,						decoder_attention_heads=2  ,						encoder_ffn_dim=32  ,						decoder_ffn_dim=32  ,						max_position_embeddings=48  ,						eos_token_id=2  ,						pad_token_id=1  ,						bos_token_id=0  ,						)
							return config, input_ids, batch_size




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_config_and_data()
							lowerCAmelCase__        =   FlaxBlenderbotForConditionalGeneration(snake_case__     )
							lowerCAmelCase__        =   lm_model(input_ids=snake_case__     )
							lowerCAmelCase__        =   (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   BlenderbotConfig(
							    vocab_size=self.vocab_size  ,						d_model=14  ,						encoder_layers=2  ,						decoder_layers=2  ,						encoder_attention_heads=2  ,						decoder_attention_heads=2  ,						encoder_ffn_dim=8  ,						decoder_ffn_dim=8  ,						max_position_embeddings=48  ,						)
							lowerCAmelCase__        =   FlaxBlenderbotForConditionalGeneration(snake_case__     )
							lowerCAmelCase__        =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,						dtype=np.intaa     )
							lowerCAmelCase__        =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,						dtype=np.intaa     )
							lowerCAmelCase__        =   lm_model(input_ids=snake_case__  ,						decoder_input_ids=snake_case__     )
							lowerCAmelCase__        =   (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,						dtype=np.intaa     )
							lowerCAmelCase__        =   shift_tokens_right(snake_case__  ,						1  ,						2     )
							lowerCAmelCase__        =   np.equal(snake_case__  ,						1     ).astype(np.floataa     ).sum()
							lowerCAmelCase__        =   np.equal(snake_case__  ,						1     ).astype(np.floataa     ).sum()
							self.assertEqual(shifted.shape  ,						input_ids.shape     )
							self.assertEqual(snake_case__  ,						n_pad_before - 1     )
							self.assertTrue(np.equal(shifted[:, 0]  ,						2     ).all()     )

@require_flax
class       a_      (     __UpperCamelCase ,		unittest.TestCase ,		__UpperCamelCase			):
					UpperCamelCase_ :	Optional[int]			=    True
					UpperCamelCase_ :	Union[str, Any]			=    (
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					UpperCamelCase_ :	Optional[int]			=    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   FlaxBlenderbotModelTester(self     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__     ):
											lowerCAmelCase__        =   self._prepare_for_class(snake_case__  ,						snake_case__     )
											lowerCAmelCase__        =   model_class(snake_case__     )

											@jax.jit
											def encode_jitted(snake_case__						:     List[Any]  ,						snake_case__						:     Any=None  ,						**snake_case__						:     Any     ):
													return model.encode(input_ids=snake_case__  ,						attention_mask=snake_case__     )

											with self.subTest("""JIT Enabled"""     ):
													lowerCAmelCase__        =   encode_jitted(**snake_case__     ).to_tuple()

											with self.subTest("""JIT Disabled"""     ):
													with jax.disable_jit():
															lowerCAmelCase__        =   encode_jitted(**snake_case__     ).to_tuple()

											self.assertEqual(len(snake_case__     )  ,						len(snake_case__     )     )
											for jitted_output, output in zip(snake_case__  ,						snake_case__     ):
													self.assertEqual(jitted_output.shape  ,						output.shape     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__     ):
											lowerCAmelCase__        =   model_class(snake_case__     )
											lowerCAmelCase__        =   model.encode(inputs_dict["""input_ids"""]  ,						inputs_dict["""attention_mask"""]     )

											lowerCAmelCase__        =   {
											    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
											    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
											    """encoder_outputs""": encoder_outputs,
											}

											@jax.jit
											def decode_jitted(snake_case__						:     List[Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[Any]     ):
													return model.decode(
													    decoder_input_ids=snake_case__  ,						decoder_attention_mask=snake_case__  ,						encoder_outputs=snake_case__  ,						)

											with self.subTest("""JIT Enabled"""     ):
													lowerCAmelCase__        =   decode_jitted(**snake_case__     ).to_tuple()

											with self.subTest("""JIT Disabled"""     ):
													with jax.disable_jit():
															lowerCAmelCase__        =   decode_jitted(**snake_case__     ).to_tuple()

											self.assertEqual(len(snake_case__     )  ,						len(snake_case__     )     )
											for jitted_output, output in zip(snake_case__  ,						snake_case__     ):
													self.assertEqual(jitted_output.shape  ,						output.shape     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							for model_class_name in self.all_model_classes:
									lowerCAmelCase__        =   model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""     )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									lowerCAmelCase__        =   np.ones((1, 1)     ) * model.config.eos_token_id
									lowerCAmelCase__        =   model(snake_case__     )
									self.assertIsNotNone(snake_case__     )




					@unittest.skipUnless(jax_device != """cpu"""  ,						"""3B test too slow on CPU."""     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
							lowerCAmelCase__        =   {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

							lowerCAmelCase__        =   FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""  ,						from_pt=snake_case__     )
							lowerCAmelCase__        =   BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )

							lowerCAmelCase__        =   ["""Sam"""]
							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""jax"""     )

							lowerCAmelCase__        =   model.generate(**snake_case__  ,						**snake_case__     )
							lowerCAmelCase__        =   """Sam is a great name. It means \"sun\" in Gaelic."""

							lowerCAmelCase__        =   tokenizer.batch_decode(snake_case__  ,						**snake_case__     )
							assert generated_txt[0].strip() == tgt_text





"""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    :							Any													=						{
    # 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       a_      :




					def __init__(	self						:     List[str]  ,						snake_case__						:     int = 14     ):
							if group not in primes:
									raise ValueError("""Unsupported Group"""     )
							lowerCAmelCase__        =   primes[group]["""prime"""]
							lowerCAmelCase__        =   primes[group]["""generator"""]

							lowerCAmelCase__        =   int(hexlify(urandom(32     )     )  ,						base=16     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return hex(self.__private_key     )[2:]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   pow(self.generator  ,						self.__private_key  ,						self.prime     )
							return hex(snake_case__     )[2:]




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							if not self.is_valid_public_key(snake_case__     ):
									raise ValueError("""Invalid public key"""     )
							lowerCAmelCase__        =   pow(snake_case__  ,						self.__private_key  ,						self.prime     )
							return shaaaa(str(snake_case__     ).encode()     ).hexdigest()




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




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     str  ,						snake_case__						:     str  ,						snake_case__						:     int = 14     ):
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							lowerCAmelCase__        =   primes[group]["""prime"""]
							if not DiffieHellman.is_valid_public_key_static(snake_case__  ,						snake_case__     ):
									raise ValueError("""Invalid public key"""     )
							lowerCAmelCase__        =   pow(snake_case__  ,						snake_case__  ,						snake_case__     )
							return shaaaa(str(snake_case__     ).encode()     ).hexdigest()


if __name__ == "__main__":
			import doctest

			doctest.testmod()




