

'''simple docstring'''

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 					lowerCAmelCase       ( A				):







       def __init__(							self     :	Optional[int]  , __lowercase     :	AutoencoderKL  , __lowercase     :	CLIPTextModel  , __lowercase     :	CLIPTokenizer  , __lowercase     :	UNetaDConditionModel  , __lowercase     :	DDIMScheduler  , __lowercase     :	float = 0.9_3  , __lowercase     :	bool = True  , ):







            """simple docstring"""







            __lowercase     						=mixing_coeff
            __lowercase     						=leapfrog_steps

            super().__init__()
            self.register_modules(
                vae=__lowercase  , text_encoder=__lowercase  , tokenizer=__lowercase  , unet=__lowercase  , scheduler=__lowercase  , )

            __lowercase     						=2 ** (len(self.vae.config.block_out_channels ) - 1)
            __lowercase     						=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )







       def      snake_case				(							self     :	Dict  , __lowercase     :	str  , __lowercase     :	Optional[str] = None  , __lowercase     :	bool = False ):







            """simple docstring"""







            __lowercase     						=self.tokenizer(
                __lowercase  , padding='max_length'  , max_length=self.tokenizer.model_max_length  , truncation=__lowercase  , return_tensors='pt'  , )

            __lowercase     						=self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

            __lowercase     						=prompt_embeds.to(dtype=self.text_encoder.dtype  , device=self.device )

            if do_classifier_free_guidance:
                 __lowercase     						='' if negative_prompt is None else negative_prompt

                 __lowercase     						=self.tokenizer(
                     __lowercase  , padding='max_length'  , max_length=self.tokenizer.model_max_length  , truncation=__lowercase  , return_tensors='pt'  , )

                 __lowercase     						=self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state

                 __lowercase     						=torch.cat([negative_prompt_embeds, prompt_embeds] )

            return prompt_embeds







       def      snake_case				(							self     :	Optional[int]  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







            __lowercase     						=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
            __lowercase     						=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

            return [x, y]







       def      snake_case				(							self     :	int  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







            __lowercase     						=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
            __lowercase     						=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

            return [x, y]







       def      snake_case				(							self     :	Dict  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







            __lowercase     						=int(__lowercase )

            __lowercase     						=self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

            return alpha_prod, 1 - alpha_prod







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , ):







            """simple docstring"""







            __lowercase     						=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )
            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )

            __lowercase     						=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
            __lowercase     						=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

            __lowercase     						=(base - b_t * model_output) / a_t

            return model_input, next_model_input.to(base.dtype )







       def      snake_case				(							self     :	Dict  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , ):







            """simple docstring"""







            __lowercase     						=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )
            __lowercase						,       __lowercase     						=self._get_alpha_and_beta(__lowercase )

            __lowercase     						=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
            __lowercase     						=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
            __lowercase     						=a_t * base + b_t * model_output

            return model_input, next_model_input.to(base.dtype )







       @torch.no_grad()
       def      snake_case				(							self     :	List[str]  , __lowercase     :	torch.Tensor ):







            """simple docstring"""







            __lowercase     						=1 / self.vae.config.scaling_factor * latents
            __lowercase     						=self.vae.decode(__lowercase ).sample
            __lowercase     						=(image / 2 + 0.5).clamp(0  , 1 )
            return image







       @torch.no_grad()
       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Image.Image  , __lowercase     :	torch.Tensor  , __lowercase     :	torch.Tensor  , __lowercase     :	float  , __lowercase     :	Optional[torch.Generator] = None  , ):







            """simple docstring"""







            __lowercase     						=guidance_scale > 1.0

            __lowercase     						=image.to(device=self.device  , dtype=text_embeds.dtype )
            __lowercase     						=self.vae.encode(__lowercase ).latent_dist.sample(__lowercase )

            __lowercase     						=self.vae.config.scaling_factor * latent

            __lowercase     						=[latent.clone(), latent.clone()]

            for i, t in tqdm(enumerate(__lowercase )  , total=len(__lowercase ) ):
                 __lowercase     						=self.noise_mixing_layer(x=coupled_latents[0]  , y=coupled_latents[1] )

                 # j - model_input index, k - base index
                 for j in range(2 ):
                      __lowercase     						=j ^ 1

                      if self.leapfrog_steps:
                           if i % 2 == 0:
                                __lowercase						,       __lowercase     						=j, k

                      __lowercase     						=coupled_latents[j]
                      __lowercase     						=coupled_latents[k]

                      __lowercase     						=torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                      __lowercase     						=self.unet(__lowercase  , __lowercase  , encoder_hidden_states=__lowercase ).sample

                      if do_classifier_free_guidance:
                           __lowercase						,       __lowercase     						=noise_pred.chunk(2 )
                           __lowercase     						=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      __lowercase						,       __lowercase     						=self.noise_step(
                          base=__lowercase  , model_input=__lowercase  , model_output=__lowercase  , timestep=__lowercase  , )

                      __lowercase     						=model_input

            return coupled_latents







       @torch.no_grad()
       def __call__(							self     :	int  , __lowercase     :	str  , __lowercase     :	str  , __lowercase     :	Image.Image  , __lowercase     :	float = 3.0  , __lowercase     :	int = 50  , __lowercase     :	float = 0.8  , __lowercase     :	Optional[str] = None  , __lowercase     :	Optional[torch.Generator] = None  , __lowercase     :	Optional[str] = "pil"  , ):







            """simple docstring"""







            __lowercase     						=guidance_scale > 1.0

            __lowercase     						=self.image_processor.preprocess(__lowercase )

            __lowercase     						=self._encode_prompt(__lowercase  , __lowercase  , __lowercase )
            __lowercase     						=self._encode_prompt(__lowercase  , __lowercase  , __lowercase )

            self.scheduler.set_timesteps(__lowercase  , self.device )

            __lowercase     						=num_inference_steps - int(num_inference_steps * strength )
            __lowercase     						=self.scheduler.timesteps[t_limit:]
            __lowercase     						=fwd_timesteps.flip(0 )

            __lowercase     						=self.prepare_latents(__lowercase  , __lowercase  , __lowercase  , __lowercase  , __lowercase )

            for i, t in tqdm(enumerate(__lowercase )  , total=len(__lowercase ) ):
                 # j - model_input index, k - base index
                 for k in range(2 ):
                      __lowercase     						=k ^ 1

                      if self.leapfrog_steps:
                           if i % 2 == 1:
                                __lowercase						,       __lowercase     						=j, k

                      __lowercase     						=coupled_latents[j]
                      __lowercase     						=coupled_latents[k]

                      __lowercase     						=torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                      __lowercase     						=self.unet(__lowercase  , __lowercase  , encoder_hidden_states=__lowercase ).sample

                      if do_classifier_free_guidance:
                           __lowercase						,       __lowercase     						=noise_pred.chunk(2 )
                           __lowercase     						=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      __lowercase						,       __lowercase     						=self.denoise_step(
                          base=__lowercase  , model_input=__lowercase  , model_output=__lowercase  , timestep=__lowercase  , )

                      __lowercase     						=model_input

                 __lowercase     						=self.denoise_mixing_layer(x=coupled_latents[0]  , y=coupled_latents[1] )

            # either one is fine
            __lowercase     						=coupled_latents[0]

            if output_type not in ["latent", "pt", "np", "pil"]:
                 __lowercase     						=(
                     f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                     '`pil`, `np`, `pt`, `latent`'
                 )
                 deprecate('Unsupported output_type'  , '1.0.0'  , __lowercase  , standard_warn=__lowercase )
                 __lowercase     						='np'

            if output_type == "latent":
                 __lowercase     						=final_latent
            else:
                 __lowercase     						=self.decode_latents(__lowercase )
                 __lowercase     						=self.image_processor.postprocess(__lowercase  , output_type=__lowercase )

            return image





'''simple docstring'''

from .number_theory.prime_numbers import next_prime


class 					lowerCAmelCase       :







       def __init__(							self     :	int  , __lowercase     :	int  , __lowercase     :	int | None = None  , __lowercase     :	float | None = None  , ):







            """simple docstring"""







            __lowercase     						=size_table
            __lowercase     						=[None] * self.size_table
            __lowercase     						=0.7_5 if lim_charge is None else lim_charge
            __lowercase     						=1 if charge_factor is None else charge_factor
            __lowercase     						=[]
            __lowercase     						={}







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self._keys







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            return sum(1 for slot in self.values if slot is not None ) / (
                self.size_table * self.charge_factor
            )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            return key % self.size_table







       def      snake_case				(							self     :	int  , __lowercase     :	Any ):







            """simple docstring"""







            print(f'''step {step_ord}''' )
            print(list(range(len(self.values ) ) ) )
            print(self.values )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=1
            __lowercase     						=values
            for value in values:
                 self.insert_data(__lowercase )
                 self._step_by_step(__lowercase )
                 i += 1







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	List[str] ):







            """simple docstring"""







            __lowercase     						=data
            __lowercase     						=data







       def      snake_case				(							self     :	int  , __lowercase     :	Any  , __lowercase     :	Optional[Any]=None ):







            """simple docstring"""







            __lowercase     						=self.hash_function(key + 1 )

            while self.values[new_key] is not None and self.values[new_key] != key:
                 if self.values.count(__lowercase ) > 0:
                      __lowercase     						=self.hash_function(new_key + 1 )
                 else:
                      __lowercase     						=None
                      break

            return new_key







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=[value for value in self.values if value is not None]
            __lowercase     						=next_prime(self.size_table  , factor=2 )
            self._keys.clear()
            __lowercase     						=[None] * self.size_table  # hell's pointers D: don't DRY ;/
            for value in survivor_values:
                 self.insert_data(__lowercase )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=self.hash_function(__lowercase )

            if self.values[key] is None:
                 self._set_value(__lowercase  , __lowercase )

            elif self.values[key] == data:
                 pass

            else:
                 __lowercase     						=self._collision_resolution(__lowercase  , __lowercase )
                 if collision_resolution is not None:
                      self._set_value(__lowercase  , __lowercase )
                 else:
                      self.rehashing()
                      self.insert_data(__lowercase )


