

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__    (				lowercase_    ):







  """simple docstring"""




  def __init__(     self      :	Union[str, Any]			,			UpperCAmelCase__      :	AutoencoderKL			,			UpperCAmelCase__      :	CLIPTextModel			,			UpperCAmelCase__      :	CLIPTokenizer			,			UpperCAmelCase__      :	UNetaDConditionModel			,			UpperCAmelCase__      :	DDIMScheduler			,			UpperCAmelCase__      :	float = 0.93			,			UpperCAmelCase__      :	bool = True			,			)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				mixing_coeff
        SCREAMING_SNAKE_CASE	: List[Any]					       =				leapfrog_steps

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

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				2 ** (len(self.vae.config.block_out_channels						) - 1)
        SCREAMING_SNAKE_CASE	: int					       =				VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[str] = None			,			UpperCAmelCase__      :	bool = False						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				self.tokenizer(
            UpperCamelCase_			,			padding="""max_length"""			,			max_length=self.tokenizer.model_max_length			,			truncation=UpperCamelCase_			,			return_tensors="""pt"""			,			)

        SCREAMING_SNAKE_CASE	: List[str]					       =				self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				prompt_embeds.to(dtype=self.text_encoder.dtype			,			device=self.device						)

        if do_classifier_free_guidance:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				"""""" if negative_prompt is None else negative_prompt

              SCREAMING_SNAKE_CASE	: Dict					       =				self.tokenizer(
                  UpperCamelCase_			,			padding="""max_length"""			,			max_length=self.tokenizer.model_max_length			,			truncation=UpperCamelCase_			,			return_tensors="""pt"""			,			)

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

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

        return prompt_embeds


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]


  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        SCREAMING_SNAKE_CASE	: List[Any]					       =				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	torch.Tensor						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				int(UpperCamelCase_						)

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

        return alpha_prod, 1 - alpha_prod


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self._get_alpha_and_beta(UpperCamelCase_						)
        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[Any]					       =				self._get_alpha_and_beta(UpperCamelCase_						)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        SCREAMING_SNAKE_CASE	: List[Any]					       =				-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

        SCREAMING_SNAKE_CASE	: List[Any]					       =				(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype						)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[int]					       =				self._get_alpha_and_beta(UpperCamelCase_						)
        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				self._get_alpha_and_beta(UpperCamelCase_						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        SCREAMING_SNAKE_CASE	: Any					       =				-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				a_t * base + b_t * model_output

        return model_input, next_model_input.to(base.dtype						)


  @torch.no_grad()
  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	torch.Tensor						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				1 / self.vae.config.scaling_factor * latents
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.vae.decode(UpperCamelCase_						).sample
        SCREAMING_SNAKE_CASE	: int					       =				(image / 2 + 0.5).clamp(0			,			1						)
        return image


  @torch.no_grad()
  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	Image.Image			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	torch.Tensor			,			UpperCAmelCase__      :	float			,			UpperCAmelCase__      :	Optional[torch.Generator] = None			,			)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				guidance_scale > 1.0

        SCREAMING_SNAKE_CASE	: List[str]					       =				image.to(device=self.device			,			dtype=text_embeds.dtype						)
        SCREAMING_SNAKE_CASE	: int					       =				self.vae.encode(UpperCamelCase_						).latent_dist.sample(UpperCamelCase_						)

        SCREAMING_SNAKE_CASE	: List[str]					       =				self.vae.config.scaling_factor * latent

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

        for i, t in tqdm(enumerate(UpperCamelCase_						)			,			total=len(UpperCamelCase_						)						):
              SCREAMING_SNAKE_CASE	: List[Any]					       =				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	: int					       =				j ^ 1

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

                    SCREAMING_SNAKE_CASE	: int					       =				coupled_latents[j]
                    SCREAMING_SNAKE_CASE	: int					       =				coupled_latents[k]

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

                    SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.unet(UpperCamelCase_			,			UpperCamelCase_			,			encoder_hidden_states=UpperCamelCase_						).sample

                    if do_classifier_free_guidance:
                          SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				noise_pred.chunk(2						)
                          SCREAMING_SNAKE_CASE	: Tuple					       =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                    SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Dict					       =				self.noise_step(
                        base=UpperCamelCase_			,			model_input=UpperCamelCase_			,			model_output=UpperCamelCase_			,			timestep=UpperCamelCase_			,			)

                    SCREAMING_SNAKE_CASE	: List[Any]					       =				model_input

        return coupled_latents




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







        SCREAMING_SNAKE_CASE	: List[Any]					       =				guidance_scale > 1.0

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.image_processor.preprocess(UpperCamelCase_						)

        SCREAMING_SNAKE_CASE	: int					       =				self._encode_prompt(UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_						)
        SCREAMING_SNAKE_CASE	: Any					       =				self._encode_prompt(UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_						)

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

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

        SCREAMING_SNAKE_CASE	: Dict					       =				self.prepare_latents(UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_						)

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

                    if self.leapfrog_steps:
                          if i % 2 == 1:
                                SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[Any]					       =				j, k

                    SCREAMING_SNAKE_CASE	: Optional[Any]					       =				coupled_latents[j]
                    SCREAMING_SNAKE_CASE	: Any					       =				coupled_latents[k]

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

                    SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.unet(UpperCamelCase_			,			UpperCamelCase_			,			encoder_hidden_states=UpperCamelCase_						).sample

                    if do_classifier_free_guidance:
                          SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				noise_pred.chunk(2						)
                          SCREAMING_SNAKE_CASE	: List[str]					       =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                    SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.denoise_step(
                        base=UpperCamelCase_			,			model_input=UpperCamelCase_			,			model_output=UpperCamelCase_			,			timestep=UpperCamelCase_			,			)

                    SCREAMING_SNAKE_CASE	: List[Any]					       =				model_input

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

        # either one is fine
        SCREAMING_SNAKE_CASE	: List[str]					       =				coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				(
                  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"""			,			UpperCamelCase_			,			standard_warn=UpperCamelCase_						)
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				"""np"""

        if output_type == "latent":
              SCREAMING_SNAKE_CASE	: Any					       =				final_latent
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.decode_latents(UpperCamelCase_						)
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self.image_processor.postprocess(UpperCamelCase_			,			output_type=UpperCamelCase_						)

        return image







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__	:       int			      =      {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__	:       int			      =      [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__	:       int			      =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




