


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

A	:		List[str]    =       {
    # 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   :
					'''simple docstring'''






					def __init__(self   :						List[Any]				, _UpperCAmelCase   :						int = 14		)						->       str:




							"""simple docstring"""


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

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

					def   lowerCamelCase__					(self   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							return hex(self.__private_key		)[2:]

					def   lowerCamelCase__					(self   :						Dict		)						->       Tuple:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						int		)						->       List[Any]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str		)						->       List[Any]:




							"""simple docstring"""


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

					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						int				, _UpperCAmelCase   :						int		)						->       List[Any]:




							"""simple docstring"""


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


					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						str				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						int = 14		)						->       List[str]:




							"""simple docstring"""


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


if __name__ == "__main__":
	import doctest

	doctest.testmod()





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


A	:		str    =       logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						List[str]				, _UpperCAmelCase   :						AutoencoderKL				, _UpperCAmelCase   :						CLIPTextModel				, _UpperCAmelCase   :						CLIPTokenizer				, _UpperCAmelCase   :						UNetaDConditionModel				, _UpperCAmelCase   :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				, _UpperCAmelCase   :						StableDiffusionSafetyChecker				, _UpperCAmelCase   :						CLIPImageProcessor				, )						->       Dict:




							"""simple docstring"""


							super().__init__()
							self.register_modules(
							    vae=_UpperCAmelCase				, text_encoder=_UpperCAmelCase				, tokenizer=_UpperCAmelCase				, unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, safety_checker=_UpperCAmelCase				, feature_extractor=_UpperCAmelCase				, )

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Optional[Union[str, int]] = "auto"		)						->       Tuple:




							"""simple docstring"""


							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowercase__					      =		self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Optional[Any]:




							"""simple docstring"""


							self.enable_attention_slicing(_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Any				, _UpperCAmelCase   :						Union[str, List[str]]				, _UpperCAmelCase   :						int = 512				, _UpperCAmelCase   :						int = 512				, _UpperCAmelCase   :						int = 50				, _UpperCAmelCase   :						float = 7.5				, _UpperCAmelCase   :						Optional[Union[str, List[str]]] = None				, _UpperCAmelCase   :						Optional[int] = 1				, _UpperCAmelCase   :						float = 0.0				, _UpperCAmelCase   :						Optional[torch.Generator] = None				, _UpperCAmelCase   :						Optional[torch.FloatTensor] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						Optional[torch.FloatTensor] = None				, **_UpperCAmelCase   :						Any				, )						->       Tuple:




							"""simple docstring"""


							if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
									lowercase__					      =		1
							elif isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
									lowercase__					      =		len(_UpperCAmelCase		)
							else:
									raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase		)}'''		)

							if height % 8 != 0 or width % 8 != 0:
									raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''		)

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(_UpperCAmelCase				, _UpperCAmelCase		) or callback_steps <= 0)
							):
									raise ValueError(
									    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
									    f''' {type(_UpperCAmelCase		)}.'''		)

							# get prompt text embeddings
							lowercase__					      =		self.tokenizer(
							    _UpperCAmelCase				, padding="""max_length"""				, max_length=self.tokenizer.model_max_length				, return_tensors="""pt"""				, )
							lowercase__					      =		text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									lowercase__					      =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''		)
									lowercase__					      =		text_input_ids[:, : self.tokenizer.model_max_length]

							if text_embeddings is None:
									lowercase__					      =		self.text_encoder(text_input_ids.to(self.device		)		)[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							lowercase__      ,     lowercase__      ,     lowercase__					      =		text_embeddings.shape
							lowercase__					      =		text_embeddings.repeat(1				, _UpperCAmelCase				, 1		)
							lowercase__					      =		text_embeddings.view(bs_embed * num_images_per_prompt				, _UpperCAmelCase				, -1		)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							lowercase__					      =		guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
									lowercase__					      =		42
									if negative_prompt is None:
											lowercase__					      =		[""""""]
									elif type(_UpperCAmelCase		) is not type(_UpperCAmelCase		):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(_UpperCAmelCase		)} !='''
											    f''' {type(_UpperCAmelCase		)}.'''		)
									elif isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
											lowercase__					      =		[negative_prompt]
									elif batch_size != len(_UpperCAmelCase		):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(_UpperCAmelCase		)}, but `prompt`:'''
											    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
											    """ the batch size of `prompt`."""		)
									else:
											lowercase__					      =		negative_prompt

									lowercase__					      =		text_input_ids.shape[-1]
									lowercase__					      =		self.tokenizer(
									    _UpperCAmelCase				, padding="""max_length"""				, max_length=_UpperCAmelCase				, truncation=_UpperCAmelCase				, return_tensors="""pt"""				, )
									lowercase__					      =		self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									lowercase__					      =		uncond_embeddings.shape[1]
									lowercase__					      =		uncond_embeddings.repeat(_UpperCAmelCase				, _UpperCAmelCase				, 1		)
									lowercase__					      =		uncond_embeddings.view(batch_size * num_images_per_prompt				, _UpperCAmelCase				, -1		)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowercase__					      =		torch.cat([uncond_embeddings, text_embeddings]		)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							lowercase__					      =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							lowercase__					      =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							lowercase__					      =		text_embeddings.dtype
							if latents is None:
									if self.device.type == "mps":
											# randn does not exist on mps
											lowercase__					      =		torch.randn(
											    _UpperCAmelCase				, generator=_UpperCAmelCase				, device="""cpu"""				, dtype=_UpperCAmelCase		).to(self.device		)
											lowercase__					      =		torch.randn(_UpperCAmelCase				, generator=_UpperCAmelCase				, device="""cpu"""				, dtype=_UpperCAmelCase		).to(
											    self.device		)
									else:
											lowercase__					      =		torch.randn(
											    _UpperCAmelCase				, generator=_UpperCAmelCase				, device=self.device				, dtype=_UpperCAmelCase		)
											lowercase__					      =		torch.randn(_UpperCAmelCase				, generator=_UpperCAmelCase				, device=self.device				, dtype=_UpperCAmelCase		)
							else:
									if latents_reference.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''		)
									lowercase__					      =		latents_reference.to(self.device		)
									lowercase__					      =		latents.to(self.device		)

							# This is the key part of the pipeline where we
							# try to ensure that the generated images w/ the same seed
							# but different sizes actually result in similar images
							lowercase__					      =		(latents_shape[3] - latents_shape_reference[3]) // 2
							lowercase__					      =		(latents_shape[2] - latents_shape_reference[2]) // 2
							lowercase__					      =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							lowercase__					      =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							lowercase__					      =		0 if dx < 0 else dx
							lowercase__					      =		0 if dy < 0 else dy
							lowercase__					      =		max(-dx				, 0		)
							lowercase__					      =		max(-dy				, 0		)
							# import pdb
							# pdb.set_trace()
							lowercase__					      =		latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(_UpperCAmelCase		)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							lowercase__					      =		self.scheduler.timesteps.to(self.device		)

							# scale the initial noise by the standard deviation required by the scheduler
							lowercase__					      =		latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							lowercase__					      =		"""eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							lowercase__					      =		{}
							if accepts_eta:
									lowercase__					      =		eta

							for i, t in enumerate(self.progress_bar(_UpperCAmelCase		)		):
									# expand the latents if we are doing classifier free guidance
									lowercase__					      =		torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
									lowercase__					      =		self.scheduler.scale_model_input(_UpperCAmelCase				, _UpperCAmelCase		)

									# predict the noise residual
									lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase				, encoder_hidden_states=_UpperCAmelCase		).sample

									# perform guidance
									if do_classifier_free_guidance:
											lowercase__      ,     lowercase__					      =		noise_pred.chunk(2		)
											lowercase__					      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									lowercase__					      =		self.scheduler.step(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, **_UpperCAmelCase		).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
											callback(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

							lowercase__					      =		1 / 0.18_215 * latents
							lowercase__					      =		self.vae.decode(_UpperCAmelCase		).sample

							lowercase__					      =		(image / 2 + 0.5).clamp(0				, 1		)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		).float().numpy()

							if self.safety_checker is not None:
									lowercase__					      =		self.feature_extractor(self.numpy_to_pil(_UpperCAmelCase		)				, return_tensors="""pt"""		).to(
									    self.device		)
									lowercase__      ,     lowercase__					      =		self.safety_checker(
									    images=_UpperCAmelCase				, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
							else:
									lowercase__					      =		None

							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=_UpperCAmelCase				, nsfw_content_detected=_UpperCAmelCase		)

