

"""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				:     int,   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Dict = 0.93,   UpperCAmelCase__				:     Optional[int] = True,   ):
    __lowercase								=		mixing_coeff
    __lowercase								=		leapfrog_steps

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

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


 def    _lowercase					(     self				:     str,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Dict = None,   UpperCAmelCase__				:     Optional[int] = False      ):
    __lowercase								=		self.tokenizer(
        _SCREAMING_SNAKE_CASE,   padding="max_length",   max_length=self.tokenizer.model_max_length,   truncation=_SCREAMING_SNAKE_CASE,   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(
           _SCREAMING_SNAKE_CASE,   padding="max_length",   max_length=self.tokenizer.model_max_length,   truncation=_SCREAMING_SNAKE_CASE,   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    _lowercase					(     self				:     Union[str, Any],   UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     Optional[int]      ):
    __lowercase								=		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
    __lowercase								=		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

    return [x, y]


 def    _lowercase					(     self				:     Union[str, Any],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     List[Any]      ):
    __lowercase								=		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
    __lowercase								=		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

    return [x, y]


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     Dict      ):
    # as self.alphas_cumprod is always in cpu
    __lowercase								=		int(_SCREAMING_SNAKE_CASE      )

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

    return alpha_prod, 1 - alpha_prod


 def    _lowercase					(     self				:     List[str],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Tuple,   ):
    __lowercase								=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

    __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    _lowercase					(     self				:     Union[str, Any],   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Dict,   ):
    __lowercase								=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

    __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    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     Union[str, Any]      ):
    __lowercase								=		1 / self.vae.config.scaling_factor * latents
    __lowercase								=		self.vae.decode(_SCREAMING_SNAKE_CASE      ).sample
    __lowercase								=		(image / 2 + 0.5).clamp(0,   1      )
    return image


 @torch.no_grad()
 def    _lowercase					(     self				:     int,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     List[Any] = None,   ):
    __lowercase								=		guidance_scale > 1.0

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

    __lowercase								=		self.vae.config.scaling_factor * latent

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

    for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE      ),   total=len(_SCREAMING_SNAKE_CASE      )      ):
       __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(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ).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=_SCREAMING_SNAKE_CASE,   model_input=_SCREAMING_SNAKE_CASE,   model_output=_SCREAMING_SNAKE_CASE,   timestep=_SCREAMING_SNAKE_CASE,   )

          __lowercase								=		model_input

    return coupled_latents



 @torch.no_grad()
 def __call__(     self				:     Any,   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str = 3.0,   UpperCAmelCase__				:     str = 5_0,   UpperCAmelCase__				:     Dict = 0.8,   UpperCAmelCase__				:     List[str] = None,   UpperCAmelCase__				:     int = None,   UpperCAmelCase__				:     Tuple = "pil",   ):
    __lowercase								=		guidance_scale > 1.0

    __lowercase								=		self.image_processor.preprocess(_SCREAMING_SNAKE_CASE      )

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

    self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE,   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(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE      )

    for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE      ),   total=len(_SCREAMING_SNAKE_CASE      )      ):
       # 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(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ).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=_SCREAMING_SNAKE_CASE,   model_input=_SCREAMING_SNAKE_CASE,   model_output=_SCREAMING_SNAKE_CASE,   timestep=_SCREAMING_SNAKE_CASE,   )

          __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",   _SCREAMING_SNAKE_CASE,   standard_warn=_SCREAMING_SNAKE_CASE      )
       __lowercase								=		"np"

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

    return image







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__A	:   Optional[int]	=					{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:   Tuple	=					[
	    'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MobileViTV2ForImageClassification',
	    'MobileViTV2ForSemanticSegmentation',
	    'MobileViTV2Model',
	    'MobileViTV2PreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mobilevitva import (
	    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    MobileViTVaConfig,
	    MobileViTVaOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MobileViTVaForImageClassification,
		    MobileViTVaForSemanticSegmentation,
		    MobileViTVaModel,
		    MobileViTVaPreTrainedModel,
		)

else:
	import sys

	__A	:   List[str]	=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
