




"""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  (     _lowerCAmelCase				):

      """simple docstring"""

      def __init__(       self   :	List[Any]	,    snake_case   :	AutoencoderKL	,    snake_case   :	CLIPTextModel	,    snake_case   :	CLIPTokenizer	,    snake_case   :	UNetaDConditionModel	,    snake_case   :	DDIMScheduler	,    snake_case   :	float = 0.93	,    snake_case   :	bool = True	,    ):
          __UpperCamelCase											=  mixing_coeff
          __UpperCamelCase											=  leapfrog_steps

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

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







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	str	,    snake_case   :	Optional[str] = None	,    snake_case   :	bool = False			):
          __UpperCamelCase											=  self.tokenizer(
              _lowerCamelCase	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=_lowerCamelCase	,    return_tensors='''pt'''	,    )

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

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

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

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

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

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

          return prompt_embeds







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor			):
          __UpperCamelCase											=  self.mixing_coeff * x + (1 - self.mixing_coeff) * y
          __UpperCamelCase											=  self.mixing_coeff * y + (1 - self.mixing_coeff) * x

          return [x, y]







      def  snake_case		(       self   :	Dict	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor			):
          __UpperCamelCase											=  (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
          __UpperCamelCase											=  (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

          return [x, y]







      def  snake_case		(       self   :	Optional[int]	,    snake_case   :	torch.Tensor			):
          __UpperCamelCase											=  int(_lowerCamelCase			)

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

          return alpha_prod, 1 - alpha_prod







      def  snake_case		(       self   :	Optional[Any]	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    ):
          __UpperCamelCase											=  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

          __UpperCamelCase						, __UpperCamelCase											=  self._get_alpha_and_beta(_lowerCamelCase			)
          __UpperCamelCase						, __UpperCamelCase											=  self._get_alpha_and_beta(_lowerCamelCase			)

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

          __UpperCamelCase											=  (base - b_t * model_output) / a_t

          return model_input, next_model_input.to(base.dtype			)







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    ):
          __UpperCamelCase											=  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

          __UpperCamelCase						, __UpperCamelCase											=  self._get_alpha_and_beta(_lowerCamelCase			)
          __UpperCamelCase						, __UpperCamelCase											=  self._get_alpha_and_beta(_lowerCamelCase			)

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

          return model_input, next_model_input.to(base.dtype			)







      @torch.no_grad()
      def  snake_case		(       self   :	Optional[int]	,    snake_case   :	torch.Tensor			):
          __UpperCamelCase											=  1 / self.vae.config.scaling_factor * latents
          __UpperCamelCase											=  self.vae.decode(_lowerCamelCase			).sample
          __UpperCamelCase											=  (image / 2 + 0.5).clamp(0	,    1			)
          return image







      @torch.no_grad()
      def  snake_case		(       self   :	Tuple	,    snake_case   :	Image.Image	,    snake_case   :	torch.Tensor	,    snake_case   :	torch.Tensor	,    snake_case   :	float	,    snake_case   :	Optional[torch.Generator] = None	,    ):
          __UpperCamelCase											=  guidance_scale > 1.0

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

          __UpperCamelCase											=  self.vae.config.scaling_factor * latent

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

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

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

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

                  __UpperCamelCase											=  coupled_latents[j]
                  __UpperCamelCase											=  coupled_latents[k]

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

                  __UpperCamelCase											=  self.unet(_lowerCamelCase	,    _lowerCamelCase	,    encoder_hidden_states=_lowerCamelCase			).sample

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

                  __UpperCamelCase						, __UpperCamelCase											=  self.noise_step(
                      base=_lowerCamelCase	,    model_input=_lowerCamelCase	,    model_output=_lowerCamelCase	,    timestep=_lowerCamelCase	,    )

                  __UpperCamelCase											=  model_input

          return coupled_latents







      @torch.no_grad()
      def __call__(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str	,    snake_case   :	Image.Image	,    snake_case   :	float = 3.0	,    snake_case   :	int = 50	,    snake_case   :	float = 0.8	,    snake_case   :	Optional[str] = None	,    snake_case   :	Optional[torch.Generator] = None	,    snake_case   :	Optional[str] = "pil"	,    ):
          __UpperCamelCase											=  guidance_scale > 1.0

          __UpperCamelCase											=  self.image_processor.preprocess(_lowerCamelCase			)

          __UpperCamelCase											=  self._encode_prompt(_lowerCamelCase	,    _lowerCamelCase	,    _lowerCamelCase			)
          __UpperCamelCase											=  self._encode_prompt(_lowerCamelCase	,    _lowerCamelCase	,    _lowerCamelCase			)

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

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

          __UpperCamelCase											=  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			):
                  __UpperCamelCase											=  k ^ 1

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

                  __UpperCamelCase											=  coupled_latents[j]
                  __UpperCamelCase											=  coupled_latents[k]

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

                  __UpperCamelCase											=  self.unet(_lowerCamelCase	,    _lowerCamelCase	,    encoder_hidden_states=_lowerCamelCase			).sample

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

                  __UpperCamelCase						, __UpperCamelCase											=  self.denoise_step(
                      base=_lowerCamelCase	,    model_input=_lowerCamelCase	,    model_output=_lowerCamelCase	,    timestep=_lowerCamelCase	,    )

                  __UpperCamelCase											=  model_input

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

          # either one is fine
          __UpperCamelCase											=  coupled_latents[0]

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

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

          return image












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

 a_										=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






