


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


snake_case__  				=			logging.get_logger(__name__)  # pylint: disable=invalid-name
def 			__SCREAMING_SNAKE_CASE							(					self	:int   ,    lowercase__	:Optional[int]   ,    lowercase__	:Optional[int]   ,    lowercase__	:int   ,    lowercase__	:Tuple					)							->	Any:
						__magic_name__					:				Dict				=   image.to(device=lowercase__					)
						__magic_name__					:				Optional[Any]				=   image  # take image as image_embeddings
						__magic_name__					:				List[str]				=   image_embeddings.unsqueeze(1					)

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						__magic_name__		,			__magic_name__		,			__magic_name__					:				Any				=   image_embeddings.shape
						__magic_name__					:				Tuple				=   image_embeddings.repeat(1   ,    lowercase__   ,    1					)
						__magic_name__					:				List[str]				=   image_embeddings.view(bs_embed * num_images_per_prompt   ,    lowercase__   ,    -1					)

						if do_classifier_free_guidance:
												__magic_name__					:				str				=   torch.zeros_like(lowercase__					)

												# 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
												__magic_name__					:				Tuple				=   torch.cat([uncond_embeddings, image_embeddings]					)

						return image_embeddings



class      snake_case__	(   _A	):
				def __init__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										super().__init__()

										__magic_name__					:				Optional[Any]				=   {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										__magic_name__					:				Any				=   decoder_pipe_kwargs.get('torch_dtype'  ,   _UpperCAmelCase   ) or prior.dtype

										__magic_name__					:				str				=   StableDiffusionImageVariationPipeline.from_pretrained(
										    'lambdalabs/sd-image-variations-diffusers'  ,   **_UpperCAmelCase   )

										# replace `_encode_image` method
										__magic_name__					:				Tuple				=   types.MethodType(_encode_image  ,   self.decoder_pipe   )

										self.register_modules(
										    prior=_UpperCAmelCase  ,   tokenizer=_UpperCAmelCase  ,   text_encoder=_UpperCAmelCase  ,   prior_scheduler=_UpperCAmelCase  ,   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										if text_model_output is None:
																__magic_name__					:				List[str]				=   len(_UpperCAmelCase   ) if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ) else 1
																# get prompt text embeddings
																__magic_name__					:				Union[str, Any]				=   self.tokenizer(
																    _UpperCAmelCase  ,   padding='max_length'  ,   max_length=self.tokenizer.model_max_length  ,   return_tensors='pt'  ,   )
																__magic_name__					:				int				=   text_inputs.input_ids
																__magic_name__					:				str				=   text_inputs.attention_mask.bool().to(_UpperCAmelCase   )

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						__magic_name__					:				int				=   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}"   )
																						__magic_name__					:				int				=   text_input_ids[:, : self.tokenizer.model_max_length]

																__magic_name__					:				Optional[int]				=   self.text_encoder(text_input_ids.to(_UpperCAmelCase   )   )

																__magic_name__					:				Any				=   text_encoder_output.text_embeds
																__magic_name__					:				Dict				=   text_encoder_output.last_hidden_state

										else:
																__magic_name__					:				Tuple				=   text_model_output[0].shape[0]
																__magic_name__		,			__magic_name__					:				Tuple				=   text_model_output[0], text_model_output[1]
																__magic_name__					:				List[str]				=   text_attention_mask

										__magic_name__					:				Tuple				=   text_embeddings.repeat_interleave(_UpperCAmelCase  ,   dim=0   )
										__magic_name__					:				Any				=   text_encoder_hidden_states.repeat_interleave(_UpperCAmelCase  ,   dim=0   )
										__magic_name__					:				int				=   text_mask.repeat_interleave(_UpperCAmelCase  ,   dim=0   )

										if do_classifier_free_guidance:
																__magic_name__					:				Dict				=   [''] * batch_size

																__magic_name__					:				List[Any]				=   self.tokenizer(
																    _UpperCAmelCase  ,   padding='max_length'  ,   max_length=self.tokenizer.model_max_length  ,   truncation=_UpperCAmelCase  ,   return_tensors='pt'  ,   )
																__magic_name__					:				Dict				=   uncond_input.attention_mask.bool().to(_UpperCAmelCase   )
																__magic_name__					:				int				=   self.text_encoder(uncond_input.input_ids.to(_UpperCAmelCase   )   )

																__magic_name__					:				Union[str, Any]				=   uncond_embeddings_text_encoder_output.text_embeds
																__magic_name__					:				Optional[Any]				=   uncond_embeddings_text_encoder_output.last_hidden_state

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																__magic_name__					:				Any				=   uncond_embeddings.shape[1]
																__magic_name__					:				List[str]				=   uncond_embeddings.repeat(1  ,   _UpperCAmelCase   )
																__magic_name__					:				Tuple				=   uncond_embeddings.view(batch_size * num_images_per_prompt  ,   _UpperCAmelCase   )

																__magic_name__					:				int				=   uncond_text_encoder_hidden_states.shape[1]
																__magic_name__					:				List[str]				=   uncond_text_encoder_hidden_states.repeat(1  ,   _UpperCAmelCase  ,   1   )
																__magic_name__					:				Tuple				=   uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt  ,   _UpperCAmelCase  ,   -1   )
																__magic_name__					:				Tuple				=   uncond_text_mask.repeat_interleave(_UpperCAmelCase  ,   dim=0   )

																# done duplicates

																# 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
																__magic_name__					:				List[Any]				=   torch.cat([uncond_embeddings, text_embeddings]   )
																__magic_name__					:				Union[str, Any]				=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

																__magic_name__					:				Tuple				=   torch.cat([uncond_text_mask, text_mask]   )

										return text_embeddings, text_encoder_hidden_states, text_mask
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										if self.device != torch.device('meta'   ) or not hasattr(self.prior  ,   '_hf_hook'   ):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(_UpperCAmelCase  ,   '_hf_hook'   )
																    and hasattr(module._hf_hook  ,   'execution_device'   )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device   )
										return self.device
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										if latents is None:
																__magic_name__					:				int				=   randn_tensor(_UpperCAmelCase  ,   generator=_UpperCAmelCase  ,   device=_UpperCAmelCase  ,   dtype=_UpperCAmelCase   )
										else:
																if latents.shape != shape:
																						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
																__magic_name__					:				str				=   latents.to(_UpperCAmelCase   )

										__magic_name__					:				Tuple				=   latents * scheduler.init_noise_sigma
										return latents
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										self.decoder_pipe.to(_UpperCAmelCase   )
										super().to(_UpperCAmelCase   )





				@torch.no_grad()
				def __call__(	self  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = 1  ,   _UpperCAmelCase = 25  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = 4.0  ,   _UpperCAmelCase = 8.0  ,   _UpperCAmelCase = 50  ,   _UpperCAmelCase = 1  ,   _UpperCAmelCase = 0.0  ,   _UpperCAmelCase = "pil"  ,   _UpperCAmelCase = True  ,   ):




										"""simple docstring"""




										if prompt is not None:
																if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																						__magic_name__					:				Tuple				=   1
																elif isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																						__magic_name__					:				Optional[Any]				=   len(_UpperCAmelCase   )
																else:
																						raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase   )}"   )
										else:
																__magic_name__					:				Dict				=   text_model_output[0].shape[0]

										__magic_name__					:				Union[str, Any]				=   self._execution_device

										__magic_name__					:				Optional[int]				=   batch_size * num_images_per_prompt

										__magic_name__					:				Optional[Any]				=   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										__magic_name__		,			__magic_name__		,			__magic_name__					:				Any				=   self._encode_prompt(
										    _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )

										# prior

										self.prior_scheduler.set_timesteps(_UpperCAmelCase  ,   device=_UpperCAmelCase   )
										__magic_name__					:				Dict				=   self.prior_scheduler.timesteps

										__magic_name__					:				Optional[Any]				=   self.prior.config.embedding_dim

										__magic_name__					:				Optional[int]				=   self.prepare_latents(
										    (batch_size, embedding_dim)  ,   text_embeddings.dtype  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   self.prior_scheduler  ,   )

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

																__magic_name__					:				Dict				=   self.prior(
																    _UpperCAmelCase  ,   timestep=_UpperCAmelCase  ,   proj_embedding=_UpperCAmelCase  ,   encoder_hidden_states=_UpperCAmelCase  ,   attention_mask=_UpperCAmelCase  ,   ).predicted_image_embedding

																if do_classifier_free_guidance:
																						__magic_name__		,			__magic_name__					:				int				=   predicted_image_embedding.chunk(2   )
																						__magic_name__					:				Any				=   predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						__magic_name__					:				Any				=   None
																else:
																						__magic_name__					:				Tuple				=   prior_timesteps_tensor[i + 1]

																__magic_name__					:				Optional[int]				=   self.prior_scheduler.step(
																    _UpperCAmelCase  ,   timestep=_UpperCAmelCase  ,   sample=_UpperCAmelCase  ,   generator=_UpperCAmelCase  ,   prev_timestep=_UpperCAmelCase  ,   ).prev_sample

										__magic_name__					:				Any				=   self.prior.post_process_latents(_UpperCAmelCase   )

										__magic_name__					:				Union[str, Any]				=   prior_latents

										__magic_name__					:				int				=   self.decoder_pipe(
										    image=_UpperCAmelCase  ,   height=_UpperCAmelCase  ,   width=_UpperCAmelCase  ,   num_inference_steps=_UpperCAmelCase  ,   guidance_scale=_UpperCAmelCase  ,   generator=_UpperCAmelCase  ,   output_type=_UpperCAmelCase  ,   return_dict=_UpperCAmelCase  ,   num_images_per_prompt=_UpperCAmelCase  ,   eta=_UpperCAmelCase  ,   )
										return output






from __future__ import annotations

import random
import string



class      snake_case__	:
				def __init__(	self  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   passcode or self.__passcode_creator()
										__magic_name__					:				Any				=   self.__make_key_list()
										__magic_name__					:				Tuple				=   self.__make_shift_key()
				def __str__(	self   ):




										"""simple docstring"""




										return "".join(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										for i in range(1  ,   len(_UpperCAmelCase   )  ,   2   ):
																iterlist[i] *= -1
										return iterlist
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   string.ascii_letters + string.digits
										__magic_name__					:				Optional[Any]				=   [random.choice(_UpperCAmelCase   ) for _ in range(random.randint(10  ,   20   )   )]
										return password
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   (
										    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
										)

										__magic_name__					:				List[str]				=   []

										# creates points known as breakpoints to break the key_list_options at those
										# points and pivot each substring
										__magic_name__					:				List[Any]				=   sorted(set(self.__passcode   )   )
										__magic_name__					:				list[str]				=   []

										# algorithm for creating a new shuffled list, keys_l, out of key_list_options
										for i in key_list_options:
																temp_list.extend(_UpperCAmelCase   )

																# checking breakpoints at which to pivot temporary sublist and add it into
																# keys_l
																if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1]   )
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
										return keys_l
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   sum(self.__neg_pos([ord(_UpperCAmelCase   ) for x in self.__passcode]   )   )
										return num if num > 0 else len(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# decoding shift like Caesar cipher algorithm implementing negative shift or
										# reverse shift or left shift
										for i in encoded_message:
																__magic_name__					:				int				=   self.__key_list.index(_UpperCAmelCase   )
																decoded_message += self.__key_list[
																    (position - self.__shift_key) % -len(self.__key_list   )
																]

										return decoded_message





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# encoding shift like Caesar cipher algorithm implementing positive shift or
										# forward shift or right shift
										for i in plaintext:
																__magic_name__					:				Dict				=   self.__key_list.index(_UpperCAmelCase   )
																encoded_message += self.__key_list[
																    (position + self.__shift_key) % len(self.__key_list   )
																]

										return encoded_message






def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str = "Hello, this is a modified Caesar cipher"					)							->	str:
						__magic_name__					:				List[str]				=   ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(lowercase__					)					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


