




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 					a_(						SCREAMING_SNAKE_CASE__					):



     """simple docstring"""



     def __init__(						self			:					int       ,       lowerCAmelCase__			:					AutoencoderKL       ,       lowerCAmelCase__			:					CLIPTextModel       ,       lowerCAmelCase__			:					CLIPTokenizer       ,       lowerCAmelCase__			:					UNetaDConditionModel       ,       lowerCAmelCase__			:					DDIMScheduler       ,       lowerCAmelCase__			:					float = 0.93       ,       lowerCAmelCase__			:					bool = True       ,       )    ->					List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						mixing_coeff
      SCREAMING_SNAKE_CASE							  =						leapfrog_steps

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

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





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					Optional[str] = None       ,       lowerCAmelCase__			:					bool = False)    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.tokenizer(
          lowerCAmelCase__       ,       padding='max_length'       ,       max_length=self.tokenizer.model_max_length       ,       truncation=lowerCAmelCase__       ,       return_tensors='pt'       ,       )

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

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

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

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

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

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

      return prompt_embeds





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor)    ->					int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.mixing_coeff * x + (1 - self.mixing_coeff) * y
      SCREAMING_SNAKE_CASE							  =						self.mixing_coeff * y + (1 - self.mixing_coeff) * x

      return [x, y]





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor)    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
      SCREAMING_SNAKE_CASE							  =						(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

      return [x, y]





     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					torch.Tensor)    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						int(lowerCAmelCase__)

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

      return alpha_prod, 1 - alpha_prod





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       )    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self._get_alpha_and_beta(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self._get_alpha_and_beta(lowerCAmelCase__)

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

      SCREAMING_SNAKE_CASE							  =						(base - b_t * model_output) / a_t

      return model_input, next_model_input.to(base.dtype)





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       )    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self._get_alpha_and_beta(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self._get_alpha_and_beta(lowerCAmelCase__)

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

      return model_input, next_model_input.to(base.dtype)





     @torch.no_grad()
     def   __UpperCamelCase		(						self			:					str       ,       lowerCAmelCase__			:					torch.Tensor)    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE							  =						self.vae.decode(lowerCAmelCase__).sample
      SCREAMING_SNAKE_CASE							  =						(image / 2 + 0.5).clamp(0       ,       1)
      return image





     @torch.no_grad()
     def   __UpperCamelCase		(						self			:					str       ,       lowerCAmelCase__			:					Image.Image       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					torch.Tensor       ,       lowerCAmelCase__			:					float       ,       lowerCAmelCase__			:					Optional[torch.Generator] = None       ,       )    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						guidance_scale > 1.0

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

      SCREAMING_SNAKE_CASE							  =						self.vae.config.scaling_factor * latent

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

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

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

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

        SCREAMING_SNAKE_CASE							  =						coupled_latents[j]
        SCREAMING_SNAKE_CASE							  =						coupled_latents[k]

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

        SCREAMING_SNAKE_CASE							  =						self.unet(lowerCAmelCase__       ,       lowerCAmelCase__       ,       encoder_hidden_states=lowerCAmelCase__).sample

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

        SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self.noise_step(
            base=lowerCAmelCase__       ,       model_input=lowerCAmelCase__       ,       model_output=lowerCAmelCase__       ,       timestep=lowerCAmelCase__       ,       )

        SCREAMING_SNAKE_CASE							  =						model_input

      return coupled_latents





     @torch.no_grad()
     def __call__(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					Image.Image       ,       lowerCAmelCase__			:					float = 3.0       ,       lowerCAmelCase__			:					int = 5_0       ,       lowerCAmelCase__			:					float = 0.8       ,       lowerCAmelCase__			:					Optional[str] = None       ,       lowerCAmelCase__			:					Optional[torch.Generator] = None       ,       lowerCAmelCase__			:					Optional[str] = "pil"       ,       )    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						guidance_scale > 1.0

      SCREAMING_SNAKE_CASE							  =						self.image_processor.preprocess(lowerCAmelCase__)

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

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

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

      SCREAMING_SNAKE_CASE							  =						self.prepare_latents(lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__)

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

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

        SCREAMING_SNAKE_CASE							  =						coupled_latents[j]
        SCREAMING_SNAKE_CASE							  =						coupled_latents[k]

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

        SCREAMING_SNAKE_CASE							  =						self.unet(lowerCAmelCase__       ,       lowerCAmelCase__       ,       encoder_hidden_states=lowerCAmelCase__).sample

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

        SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self.denoise_step(
            base=lowerCAmelCase__       ,       model_input=lowerCAmelCase__       ,       model_output=lowerCAmelCase__       ,       timestep=lowerCAmelCase__       ,       )

        SCREAMING_SNAKE_CASE							  =						model_input

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

      # either one is fine
      SCREAMING_SNAKE_CASE							  =						coupled_latents[0]

      if output_type not in ["latent", "pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE							  =						(
           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'       ,       lowerCAmelCase__       ,       standard_warn=lowerCAmelCase__)
       SCREAMING_SNAKE_CASE							  =						'np'

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

      return image












import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def 		A_			(		lowercase_					,       lowercase_ = False	)							->Union[str, Any]:







 """simple docstring"""

 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(lowercase_	)
 np.random.seed(lowercase_	)
 torch.manual_seed(lowercase_	)
 if is_xpu_available():
  torch.xpu.manual_seed_all(lowercase_	)
 elif is_npu_available():
  torch.npu.manual_seed_all(lowercase_	)
 else:
  torch.cuda.manual_seed_all(lowercase_	)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(lowercase_	)





def 		A_			(		lowercase_ = None					,       lowercase_ = None	)							->Optional[Any]:







 """simple docstring"""

 if rng_type == RNGType.TORCH:
  SCREAMING_SNAKE_CASE							  =						torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  SCREAMING_SNAKE_CASE							  =						torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  SCREAMING_SNAKE_CASE							  =						torch.tensor(xm.get_rng_state()	)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  SCREAMING_SNAKE_CASE							  =						torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  SCREAMING_SNAKE_CASE							  =						torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  SCREAMING_SNAKE_CASE							  =						generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 SCREAMING_SNAKE_CASE							  =						AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  SCREAMING_SNAKE_CASE							  =						rng_state.to(xm.xla_device()	)
  xm.collective_broadcast([rng_state]	)
  xm.mark_step()
  SCREAMING_SNAKE_CASE							  =						rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  SCREAMING_SNAKE_CASE							  =						rng_state.to(state.device	)
  torch.distributed.broadcast(lowercase_					,       0	)
  SCREAMING_SNAKE_CASE							  =						rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(lowercase_					,       0	)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(lowercase_	)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(lowercase_	)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(lowercase_	)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(lowercase_	)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()	)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = None	)							->str:







 """simple docstring"""

 for rng_type in rng_types:
  synchronize_rng_state(RNGType(lowercase_	)					,       generator=lowercase_	)




