






'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					StableDiffusionSAGPipeline
			snake_case__	=					TEXT_TO_IMAGE_PARAMS
			snake_case__	=					TEXT_TO_IMAGE_BATCH_PARAMS
			snake_case__	=					TEXT_TO_IMAGE_IMAGE_PARAMS
			snake_case__	=					TEXT_TO_IMAGE_IMAGE_PARAMS
			snake_case__	=					False






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      List[str]:
							torch.manual_seed(0       )
							a_		:	List[Any]									=   UNetaDConditionModel(
							    block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=4	,    out_channels=4	,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,    cross_attention_dim=32	,    )
							a_		:	Union[str, Any]									=   DDIMScheduler(
							    beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule='''scaled_linear'''	,    clip_sample=__SCREAMING_SNAKE_CASE	,    set_alpha_to_one=__SCREAMING_SNAKE_CASE	,    )
							torch.manual_seed(0       )
							a_		:	Optional[Any]									=   AutoencoderKL(
							    block_out_channels=[32, 64]	,    in_channels=3	,    out_channels=3	,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,    latent_channels=4	,    )
							torch.manual_seed(0       )
							a_		:	List[Any]									=   CLIPTextConfig(
							    bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    )
							a_		:	str									=   CLIPTextModel(__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

							a_		:	Union[str, Any]									=   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      str=0       )      ->      int:
							if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
											a_		:	Optional[Any]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							else:
											a_		:	List[Any]									=   torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   {
							    '''prompt''': '''.''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 1.0,
							    '''sag_scale''': 1.0,
							    '''output_type''': '''numpy''',
							}
							return inputs







			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Tuple:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3       )





@slow
@require_torch_gpu
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      int:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      str:
							a_		:	int									=   StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''       )
							a_		:	Any									=   sag_pipe.to(__SCREAMING_SNAKE_CASE       )
							sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	int									=   '''.'''
							a_		:	List[Any]									=   torch.manual_seed(0       )
							a_		:	Union[str, Any]									=   sag_pipe(
							    [prompt]	,    generator=__SCREAMING_SNAKE_CASE	,    guidance_scale=7.5	,    sag_scale=1.0	,    num_inference_steps=20	,    output_type='''np'''       )

							a_		:	Dict									=   output.images

							a_		:	Optional[Any]									=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							a_		:	Optional[Any]									=   np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]       )

							assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-2






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Dict:
							a_		:	int									=   StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''       )
							a_		:	List[str]									=   sag_pipe.to(__SCREAMING_SNAKE_CASE       )
							sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   '''.'''
							a_		:	int									=   torch.manual_seed(0       )
							a_		:	Optional[Any]									=   sag_pipe(
							    [prompt]	,    generator=__SCREAMING_SNAKE_CASE	,    guidance_scale=7.5	,    sag_scale=1.0	,    num_inference_steps=20	,    output_type='''np'''       )

							a_		:	Union[str, Any]									=   output.images

							a_		:	Tuple									=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							a_		:	List[str]									=   np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]       )

							assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-2







			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							a_		:	List[Any]									=   StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''       )
							a_		:	List[str]									=   sag_pipe.to(__SCREAMING_SNAKE_CASE       )
							sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	int									=   '''.'''
							a_		:	Dict									=   torch.manual_seed(0       )
							a_		:	Optional[Any]									=   sag_pipe(
							    [prompt]	,    width=768	,    height=512	,    generator=__SCREAMING_SNAKE_CASE	,    guidance_scale=7.5	,    sag_scale=1.0	,    num_inference_steps=20	,    output_type='''np'''	,    )

							a_		:	Optional[Any]									=   output.images

							assert image.shape == (1, 512, 768, 3)












'''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 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      None:
							if group not in primes:
											raise ValueError('''Unsupported Group'''       )
							a_		:	Union[str, Any]									=   primes[group]['''prime''']
							a_		:	List[str]									=   primes[group]['''generator''']

							a_		:	str									=   int(hexlify(urandom(32       )       )	,    base=16       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							return hex(self.__private_key       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							a_		:	Dict									=   pow(self.generator	,    self.__private_key	,    self.prime       )
							return hex(__SCREAMING_SNAKE_CASE       )[2:]






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      str:
							a_		:	str									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    self.__private_key	,    self.prime       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()






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







			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      str:
							a_		:	Tuple									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	Any									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	List[str]									=   primes[group]['''prime''']
							if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()


if __name__ == "__main__":
		import doctest

		doctest.testmod()




