



import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image







class       __lowerCAmelCase   :



			def __init__(						self       :  Dict			,		A       :  str			,		A       :  List[str]=3			,		A       :  int=32			,		A       :  List[Any]=3			,		A       :  List[Any]=10			,		A       :  int=[8, 16, 32, 64]			,		A       :  Tuple=[1, 1, 2, 1]			,		A       :  Optional[int]=True			,		A       :  Union[str, Any]=True			,		A       :  Union[str, Any]="relu"			,		A       :  str=3			,		A       :  Any=None			,		A       :  Dict=["stage2", "stage3", "stage4"]			,		A       :  Any=[2, 3, 4]			,		A       :  Optional[Any]=1			,		)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       image_size
						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       embeddings_size
						_UpperCAmelCase			=       hidden_sizes
						_UpperCAmelCase			=       depths
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       scope
						_UpperCAmelCase			=       len(A)
						_UpperCAmelCase			=       out_features
						_UpperCAmelCase			=       out_indices
						_UpperCAmelCase			=       num_groups



			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.num_labels)

						_UpperCAmelCase			=       self.get_config()

						return config, pixel_values, labels



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						return BitConfig(
						    num_channels=self.num_channels			,		embeddings_size=self.embeddings_size			,		hidden_sizes=self.hidden_sizes			,		depths=self.depths			,		hidden_act=self.hidden_act			,		num_labels=self.num_labels			,		out_features=self.out_features			,		out_indices=self.out_indices			,		num_groups=self.num_groups			,		)



			def    _lowerCamelCase	(						self       :  str			,		A       :  Optional[int]			,		A       :  Dict			,		A       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       BitModel(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,		)



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  Optional[Any]			,		A       :  Dict			,		A       :  Union[str, Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       BitForImageClassification(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A			,		labels=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



			def    _lowerCamelCase	(						self       :  str			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  List[str])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       BitBackbone(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps)			,		len(config.out_features))
						self.parent.assertListEqual(list(result.feature_maps[0].shape)			,		[self.batch_size, self.hidden_sizes[1], 4, 4])

						# verify channels
						self.parent.assertEqual(len(model.channels)			,		len(config.out_features))
						self.parent.assertListEqual(model.channels			,		config.hidden_sizes[1:])

						# verify backbone works with out_features=None
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       BitBackbone(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps)			,		1)
						self.parent.assertListEqual(list(result.feature_maps[0].shape)			,		[self.batch_size, self.hidden_sizes[-1], 1, 1])

						# verify channels
						self.parent.assertEqual(len(model.channels)			,		1)
						self.parent.assertListEqual(model.channels			,		[config.hidden_sizes[-1]])



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						_UpperCAmelCase			=       config_and_inputs
						_UpperCAmelCase			=       {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class       __lowerCAmelCase   (      __lowercase    ,						__lowercase    ,						unittest.TestCase						):
			UpperCamelCase        =					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			UpperCamelCase        =					(
			    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  Any)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       BitModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=A			,		has_text_modality=A)



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Any:



						"""simple docstring"""






						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						return



			@unittest.skip(reason='Bit does not output attentions')
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""simple docstring"""






						pass



			@unittest.skip(reason='Bit does not use inputs_embeds')
			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						pass



			@unittest.skip(reason='Bit does not support input and output embeddings')
			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase			=       model_class(A)
									_UpperCAmelCase			=       inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_UpperCAmelCase			=       [*signature.parameters.keys()]

									_UpperCAmelCase			=       ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]			,		A)



			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  str)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase			=       model_class(config=A)
									for name, module in model.named_modules():
												if isinstance(A			,		(nn.BatchNormad, nn.GroupNorm)):
															self.assertTrue(
															    torch.all(module.weight == 1)			,		msg=F"Parameter {name} of model {model_class} seems not properly initialized"			,		)
															self.assertTrue(
															    torch.all(module.bias == 0)			,		msg=F"Parameter {name} of model {model_class} seems not properly initialized"			,		)



			def    _lowerCamelCase	(						self       :  Any)       ->   List[Any]:



						"""simple docstring"""






						def check_hidden_states_output(A       :  List[str]			,		A       :  Tuple			,		A       :  Optional[int]):
									_UpperCAmelCase			=       model_class(A)
									model.to(A)
									model.eval()

									with torch.no_grad():
												_UpperCAmelCase			=       model(**self._prepare_for_class(A			,		A))

									_UpperCAmelCase			=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									_UpperCAmelCase			=       self.model_tester.num_stages
									self.assertEqual(len(A)			,		expected_num_stages + 1)

									# Bit's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:])			,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,		)

						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()
						_UpperCAmelCase			=       ["""preactivation""", """bottleneck"""]
						for model_class in self.all_model_classes:
									for layer_type in layers_type:
												_UpperCAmelCase			=       layer_type
												_UpperCAmelCase			=       True
												check_hidden_states_output(A			,		A			,		A)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_UpperCAmelCase			=       True

												check_hidden_states_output(A			,		A			,		A)



			@unittest.skip(reason='Bit does not use feedforward chunking')
			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A)



			@slow
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase			=       BitModel.from_pretrained(A)
									self.assertIsNotNone(A)





def 							A     (					)    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
			return image







@require_torch
@require_vision
class       __lowerCAmelCase   (      unittest.TestCase						):



			@cached_property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Tuple:



						"""simple docstring"""






						return (
						    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
						)



			@slow
			def    _lowerCamelCase	(						self       :  Tuple)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(A)

						_UpperCAmelCase			=       self.default_image_processor
						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)

						# verify the logits
						_UpperCAmelCase			=       torch.Size((1, 10_00))
						self.assertEqual(outputs.logits.shape			,		A)

						_UpperCAmelCase			=       torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]).to(A)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		A			,		atol=1E-4))







@require_torch
class       __lowerCAmelCase   (      __lowercase    ,						unittest.TestCase						):
			UpperCamelCase        =					(BitBackbone,) if is_torch_available() else ()
			UpperCamelCase        =					BitConfig

			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       BitModelTester(self)





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

UpperCAmelCase__       =	{
    # 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       __lowerCAmelCase   :



			def __init__(						self       :  List[str]			,		A       :  int = 14)       ->   None:



						"""simple docstring"""






						if group not in primes:
									raise ValueError('Unsupported Group')
						_UpperCAmelCase			=       primes[group]['prime']
						_UpperCAmelCase			=       primes[group]['generator']

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



			def    _lowerCamelCase	(						self       :  int)       ->   str:



						"""simple docstring"""






						return hex(self.__private_key)[2:]



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       pow(self.generator			,		self.__private_key			,		self.prime)
						return hex(A)[2:]



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  int)       ->   bool:



						"""simple docstring"""






						return (
						    2 <= key <= self.prime - 2
						    and pow(A			,		(self.prime - 1) // 2			,		self.prime) == 1
						)



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       int(A			,		base=16)
						if not self.is_valid_public_key(A):
									raise ValueError('Invalid public key')
						_UpperCAmelCase			=       pow(A			,		self.__private_key			,		self.prime)
						return shaaaa(str(A).encode()).hexdigest()



			@staticmethod
			def    _lowerCamelCase	(						A       :  int			,		A       :  int)       ->   bool:



						"""simple docstring"""






						return (
						    2 <= remote_public_key_str <= prime - 2
						    and pow(A			,		(prime - 1) // 2			,		A) == 1
						)



			@staticmethod
			def    _lowerCamelCase	(						A       :  str			,		A       :  str			,		A       :  int = 14)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       int(A			,		base=16)
						_UpperCAmelCase			=       int(A			,		base=16)
						_UpperCAmelCase			=       primes[group]['prime']
						if not DiffieHellman.is_valid_public_key_static(A			,		A):
									raise ValueError('Invalid public key')
						_UpperCAmelCase			=       pow(A			,		A			,		A)
						return shaaaa(str(A).encode()).hexdigest()


if __name__ == "__main__":
							import doctest

							doctest.testmod()
