
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class       a		:







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								torch.manual_seed(0 )
								__lowerCAmelCase         =							TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

								torch.manual_seed(0 )
								__lowerCAmelCase         =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )

								torch.manual_seed(0 )
								__lowerCAmelCase         =							UNetaDConditionModel(
								    sample_size=32      ,				layers_per_block=1      ,				block_out_channels=[32, 64]      ,				down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]      ,				mid_block_type="UNetMidBlock2DSimpleCrossAttn"      ,				up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]      ,				in_channels=3      ,				out_channels=6      ,				cross_attention_dim=32      ,				encoder_hid_dim=32      ,				attention_head_dim=8      ,				addition_embed_type="text"      ,				addition_embed_type_num_heads=2      ,				cross_attention_norm="group_norm"      ,				resnet_time_scale_shift="scale_shift"      ,				act_fn="gelu"      ,				)
								unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								torch.manual_seed(0 )
								__lowerCAmelCase         =							DDPMScheduler(
								    num_train_timesteps=1_000      ,				beta_schedule="squaredcos_cap_v2"      ,				beta_start=0.0_0_0_1      ,				beta_end=0.0_2      ,				thresholding=snake_case__      ,				dynamic_thresholding_ratio=0.9_5      ,				sample_max_value=1.0      ,				prediction_type="epsilon"      ,				variance_type="learned_range"      ,				)

								torch.manual_seed(0 )
								__lowerCAmelCase         =							IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								torch.manual_seed(0 )
								__lowerCAmelCase         =							TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )

								torch.manual_seed(0 )
								__lowerCAmelCase         =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )

								torch.manual_seed(0 )
								__lowerCAmelCase         =							UNetaDConditionModel(
								    sample_size=32      ,				layers_per_block=[1, 2]      ,				block_out_channels=[32, 64]      ,				down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]      ,				mid_block_type="UNetMidBlock2DSimpleCrossAttn"      ,				up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]      ,				in_channels=6      ,				out_channels=6      ,				cross_attention_dim=32      ,				encoder_hid_dim=32      ,				attention_head_dim=8      ,				addition_embed_type="text"      ,				addition_embed_type_num_heads=2      ,				cross_attention_norm="group_norm"      ,				resnet_time_scale_shift="scale_shift"      ,				act_fn="gelu"      ,				class_embed_type="timestep"      ,				mid_block_scale_factor=1.4_1_4      ,				time_embedding_act_fn="gelu"      ,				time_embedding_dim=32      ,				)
								unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								torch.manual_seed(0 )
								__lowerCAmelCase         =							DDPMScheduler(
								    num_train_timesteps=1_000      ,				beta_schedule="squaredcos_cap_v2"      ,				beta_start=0.0_0_0_1      ,				beta_end=0.0_2      ,				thresholding=snake_case__      ,				dynamic_thresholding_ratio=0.9_5      ,				sample_max_value=1.0      ,				prediction_type="epsilon"      ,				variance_type="learned_range"      ,				)

								torch.manual_seed(0 )
								__lowerCAmelCase         =							DDPMScheduler(
								    num_train_timesteps=1_000      ,				beta_schedule="squaredcos_cap_v2"      ,				beta_start=0.0_0_0_1      ,				beta_end=0.0_2      ,				)

								torch.manual_seed(0 )
								__lowerCAmelCase         =							IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.get_dummy_components()
								__lowerCAmelCase         =							self.pipeline_class(**snake_case__ )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							self.get_dummy_inputs(snake_case__ )

								__lowerCAmelCase         =							inputs["prompt"]
								__lowerCAmelCase         =							inputs["generator"]
								__lowerCAmelCase         =							inputs["num_inference_steps"]
								__lowerCAmelCase         =							inputs["output_type"]

								if "image" in inputs:
									__lowerCAmelCase         =							inputs["image"]
								else:
									__lowerCAmelCase         =							None

								if "mask_image" in inputs:
									__lowerCAmelCase         =							inputs["mask_image"]
								else:
									__lowerCAmelCase         =							None

								if "original_image" in inputs:
									__lowerCAmelCase         =							inputs["original_image"]
								else:
									__lowerCAmelCase         =							None

								__lowerCAmelCase   ,						__lowerCAmelCase         =							pipe.encode_prompt(snake_case__ )

								# inputs with prompt converted to embeddings
								__lowerCAmelCase         =							{
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
									__lowerCAmelCase         =							image

								if mask_image is not None:
									__lowerCAmelCase         =							mask_image

								if original_image is not None:
									__lowerCAmelCase         =							original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
									setattr(snake_case__      ,				snake_case__      ,				snake_case__ )

								__lowerCAmelCase         =							pipe(**snake_case__ )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(snake_case__ )
									__lowerCAmelCase         =							self.pipeline_class.from_pretrained(snake_case__ )
									pipe_loaded.to(snake_case__ )
									pipe_loaded.set_progress_bar_config(disable=snake_case__ )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								for optional_component in pipe._optional_components:
									self.assertTrue(
									    getattr(snake_case__      ,				snake_case__ ) is None      ,				F"`{optional_component}` did not stay set to None after loading."      ,				)

								__lowerCAmelCase         =							self.get_dummy_inputs(snake_case__ )

								__lowerCAmelCase         =							inputs["generator"]
								__lowerCAmelCase         =							inputs["num_inference_steps"]
								__lowerCAmelCase         =							inputs["output_type"]

								# inputs with prompt converted to embeddings
								__lowerCAmelCase         =							{
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
									__lowerCAmelCase         =							image

								if mask_image is not None:
									__lowerCAmelCase         =							mask_image

								if original_image is not None:
									__lowerCAmelCase         =							original_image

								__lowerCAmelCase         =							pipe_loaded(**snake_case__ )[0]

								__lowerCAmelCase         =							np.abs(to_np(snake_case__ ) - to_np(snake_case__ ) ).max()
								self.assertLess(snake_case__      ,				1E-4 )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.get_dummy_components()
								__lowerCAmelCase         =							self.pipeline_class(**snake_case__ )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							self.get_dummy_inputs(snake_case__ )
								__lowerCAmelCase         =							pipe(**snake_case__ )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(snake_case__ )
									__lowerCAmelCase         =							self.pipeline_class.from_pretrained(snake_case__ )
									pipe_loaded.to(snake_case__ )
									pipe_loaded.set_progress_bar_config(disable=snake_case__ )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								__lowerCAmelCase         =							self.get_dummy_inputs(snake_case__ )
								__lowerCAmelCase         =							pipe_loaded(**snake_case__ )[0]

								__lowerCAmelCase         =							np.abs(to_np(snake_case__ ) - to_np(snake_case__ ) ).max()
								self.assertLess(snake_case__      ,				1E-4 )








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=1_6,
        ),
        "generator": 2,
    },
    # 2048-bit
    1_4: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
            base=1_6,
        ),
        "generator": 2,
    },
    # 3072-bit
    1_5: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
            base=1_6,
        ),
        "generator": 2,
    },
    # 4096-bit
    1_6: {
        "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=1_6,
        ),
        "generator": 2,
    },
    # 6144-bit
    1_7: {
        "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=1_6,
        ),
        "generator": 2,
    },
    # 8192-bit
    1_8: {
        "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=1_6,
        ),
        "generator": 2,
    },
}

class       a		:







							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  int = 14 ):
								"""simple docstring"""







								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       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return hex(self.__private_key )[2:]







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  int ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  str ):
								"""simple docstring"""







								__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       UpperCAmelCase__					(						snake_case__  :  int      ,				snake_case__  :  int ):
								"""simple docstring"""







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







							@staticmethod
							def       UpperCAmelCase__					(						snake_case__  :  str      ,				snake_case__  :  str      ,				snake_case__  :  int = 14 ):
								"""simple docstring"""







								__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()






