





'''simple docstring'''

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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase__						:      Union[str, Any]	=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class     __lowerCAmelCase					(   __magic_name__						,						__magic_name__			):






       """simple docstring"""





       @register_to_config
       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		bool	,			lowerCAmelCase__       :		Optional[int] = None	,			lowerCAmelCase__       :		Optional[int] = None					)   ->					Optional[Any]:

            '''simple docstring'''




            super().__init__()

            _UpperCamelCase       =	learnable

            if self.learnable:
                 assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                 assert length is not None, "learnable=True requires `length` to be set"

                 _UpperCamelCase       =	torch.zeros(lowerCAmelCase__	,			lowerCAmelCase__					)
            else:
                 _UpperCamelCase       =	None

            _UpperCamelCase       =	torch.nn.Parameter(lowerCAmelCase__					)

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					VQModel
       _snake_case     :					CLIPTextModel
       _snake_case     :					CLIPTokenizer
       _snake_case     :					TransformeraDModel
       _snake_case     :					LearnedClassifierFreeSamplingEmbeddings
       _snake_case     :					VQDiffusionScheduler





       def __init__(							self       :		Dict	,			lowerCAmelCase__       :		VQModel	,			lowerCAmelCase__       :		CLIPTextModel	,			lowerCAmelCase__       :		CLIPTokenizer	,			lowerCAmelCase__       :		TransformeraDModel	,			lowerCAmelCase__       :		VQDiffusionScheduler	,			lowerCAmelCase__       :		LearnedClassifierFreeSamplingEmbeddings	,			)   ->					Optional[Any]:

            '''simple docstring'''




            super().__init__()

            self.register_modules(
                vqvae=lowerCAmelCase__	,			transformer=lowerCAmelCase__	,			text_encoder=lowerCAmelCase__	,			tokenizer=lowerCAmelCase__	,			scheduler=lowerCAmelCase__	,			learned_classifier_free_sampling_embeddings=lowerCAmelCase__	,			)





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Any					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	len(lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else 1

            # get prompt text embeddings
            _UpperCamelCase       =	self.tokenizer(
                lowerCAmelCase__	,			padding='''max_length'''	,			max_length=self.tokenizer.model_max_length	,			return_tensors='''pt'''	,			)
            _UpperCamelCase       =	text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 _UpperCamelCase       =	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}"""					)
                 _UpperCamelCase       =	text_input_ids[:, : self.tokenizer.model_max_length]
            _UpperCamelCase       =	self.text_encoder(text_input_ids.to(self.device					)					)[0]

            # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
            # While CLIP does normalize the pooled output of the text transformer when combining
            # the image and text embeddings, CLIP does not directly normalize the last hidden state.
            #
            # CLIP normalizing the pooled output.
            # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
            _UpperCamelCase       =	prompt_embeds / prompt_embeds.norm(dim=-1	,			keepdim=lowerCAmelCase__					)

            # duplicate text embeddings for each generation per prompt
            _UpperCamelCase       =	prompt_embeds.repeat_interleave(lowerCAmelCase__	,			dim=0					)

            if do_classifier_free_guidance:
                 if self.learned_classifier_free_sampling_embeddings.learnable:
                      _UpperCamelCase       =	self.learned_classifier_free_sampling_embeddings.embeddings
                      _UpperCamelCase       =	negative_prompt_embeds.unsqueeze(0					).repeat(lowerCAmelCase__	,			1	,			1					)
                 else:
                      _UpperCamelCase       =	[''''''] * batch_size

                      _UpperCamelCase       =	text_input_ids.shape[-1]
                      _UpperCamelCase       =	self.tokenizer(
                          lowerCAmelCase__	,			padding='''max_length'''	,			max_length=lowerCAmelCase__	,			truncation=lowerCAmelCase__	,			return_tensors='''pt'''	,			)
                      _UpperCamelCase       =	self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
                      # See comment for normalizing text embeddings
                      _UpperCamelCase       =	negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1	,			keepdim=lowerCAmelCase__					)

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _UpperCamelCase       =	negative_prompt_embeds.shape[1]
                 _UpperCamelCase       =	negative_prompt_embeds.repeat(1	,			lowerCAmelCase__	,			1					)
                 _UpperCamelCase       =	negative_prompt_embeds.view(batch_size * num_images_per_prompt	,			lowerCAmelCase__	,			-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
                 _UpperCamelCase       =	torch.cat([negative_prompt_embeds, prompt_embeds]					)

            return prompt_embeds





       @torch.no_grad()
       def __call__(							self       :		str	,			lowerCAmelCase__       :		Union[str, List[str]]	,			lowerCAmelCase__       :		int = 100	,			lowerCAmelCase__       :		float = 5.0	,			lowerCAmelCase__       :		float = 1.0	,			lowerCAmelCase__       :		int = 1	,			lowerCAmelCase__       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			lowerCAmelCase__       :		Optional[torch.FloatTensor] = None	,			lowerCAmelCase__       :		Optional[str] = "pil"	,			lowerCAmelCase__       :		bool = True	,			lowerCAmelCase__       :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,			lowerCAmelCase__       :		int = 1	,			)   ->					Union[ImagePipelineOutput, Tuple]:

            '''simple docstring'''




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

            _UpperCamelCase       =	batch_size * num_images_per_prompt

            _UpperCamelCase       =	guidance_scale > 1.0

            _UpperCamelCase       =	self._encode_prompt(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)

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

            # get the initial completely masked latents unless the user supplied it

            _UpperCamelCase       =	(batch_size, self.transformer.num_latent_pixels)
            if latents is None:
                 _UpperCamelCase       =	self.transformer.num_vector_embeds - 1
                 _UpperCamelCase       =	torch.full(lowerCAmelCase__	,			lowerCAmelCase__					).to(self.device					)
            else:
                 if latents.shape != latents_shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""					)
                 if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                      raise ValueError(
                          '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
                          f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""					)
                 _UpperCamelCase       =	latents.to(self.device					)

            # set timesteps
            self.scheduler.set_timesteps(lowerCAmelCase__	,			device=self.device					)

            _UpperCamelCase       =	self.scheduler.timesteps.to(self.device					)

            _UpperCamelCase       =	latents

            for i, t in enumerate(self.progress_bar(lowerCAmelCase__					)					):
                 # expand the sample if we are doing classifier free guidance
                 _UpperCamelCase       =	torch.cat([sample] * 2					) if do_classifier_free_guidance else sample

                 # predict the un-noised image
                 # model_output == `log_p_x_0`
                 _UpperCamelCase       =	self.transformer(lowerCAmelCase__	,			encoder_hidden_states=lowerCAmelCase__	,			timestep=lowerCAmelCase__					).sample

                 if do_classifier_free_guidance:
                      _UpperCamelCase   ,    _UpperCamelCase       =	model_output.chunk(2					)
                      _UpperCamelCase       =	model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                      model_output -= torch.logsumexp(lowerCAmelCase__	,			dim=1	,			keepdim=lowerCAmelCase__					)

                 _UpperCamelCase       =	self.truncate(lowerCAmelCase__	,			lowerCAmelCase__					)

                 # remove `log(0)`'s (`-inf`s)
                 _UpperCamelCase       =	model_output.clamp(-70					)

                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCamelCase       =	self.scheduler.step(lowerCAmelCase__	,			timestep=lowerCAmelCase__	,			sample=lowerCAmelCase__	,			generator=lowerCAmelCase__					).prev_sample

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

            _UpperCamelCase       =	self.vqvae.config.vq_embed_dim
            _UpperCamelCase       =	(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
            _UpperCamelCase       =	self.vqvae.quantize.get_codebook_entry(lowerCAmelCase__	,			shape=lowerCAmelCase__					)
            _UpperCamelCase       =	self.vqvae.decode(lowerCAmelCase__	,			force_not_quantize=lowerCAmelCase__					).sample

            _UpperCamelCase       =	(image / 2 + 0.5).clamp(0	,			1					)
            _UpperCamelCase       =	image.cpu().permute(0	,			2	,			3	,			1					).numpy()

            if output_type == "pil":
                 _UpperCamelCase       =	self.numpy_to_pil(lowerCAmelCase__					)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__					)







       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		torch.FloatTensor	,			lowerCAmelCase__       :		float					)   ->					torch.FloatTensor:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	torch.sort(lowerCAmelCase__	,			1	,			descending=lowerCAmelCase__					)
            _UpperCamelCase       =	torch.exp(lowerCAmelCase__					)
            _UpperCamelCase       =	sorted_p_x_0.cumsum(dim=1					) < truncation_rate

            # Ensure that at least the largest probability is not zeroed out
            _UpperCamelCase       =	torch.full_like(keep_mask[:, 0:1, :]	,			lowerCAmelCase__					)
            _UpperCamelCase       =	torch.cat((all_true, keep_mask)	,			dim=1					)
            _UpperCamelCase       =	keep_mask[:, :-1, :]

            _UpperCamelCase       =	keep_mask.gather(1	,			indices.argsort(1					)					)

            _UpperCamelCase       =	log_p_x_0.clone()

            _UpperCamelCase       =	-torch.inf  # -inf = log(0)

            return rv










'''simple docstring'''

import random
class     __lowerCAmelCase					:






       """simple docstring"""





       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str					)   ->					tuple[list[int], list[int]]:

            '''simple docstring'''




            _UpperCamelCase       =	[ord(lowerCAmelCase__					) for i in text]
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            for i in plain:
                 _UpperCamelCase       =	random.randint(1	,			300					)
                 _UpperCamelCase       =	(i + k) * k
                 cipher.append(lowerCAmelCase__					)
                 key.append(lowerCAmelCase__					)
            return cipher, key







       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		list[int]	,			lowerCAmelCase__       :		list[int]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[]
            for i in range(len(lowerCAmelCase__					)					):
                 _UpperCamelCase       =	int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(lowerCAmelCase__					)					)
            return "".join(lowerCAmelCase__					)


if __name__ == "__main__":
   lowercase__	,       lowercase__						:      List[str]	=    Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))



