






'''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 					a					(				lowerCamelCase__     ):



      """simple docstring"""


      def __init__(   self      :    str	,   __lowercase      :    Optional[int]	,   __lowercase      :    List[Any]	,   __lowercase      :    Dict	,   __lowercase      :    str	,   __lowercase      :    Optional[int]	,   __lowercase      :    Optional[int] = 0.93	,   __lowercase      :    List[str] = True	,   )							->   Any:
        __UpperCAmelCase							:			str     =						mixing_coeff
        __UpperCAmelCase							:			Optional[Any]     =						leapfrog_steps

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

        __UpperCAmelCase							:			Any     =						2 ** (len(self.vae.config.block_out_channels      ) - 1)
        __UpperCAmelCase							:			Union[str, Any]     =						VaeImageProcessor(vae_scale_factor=self.vae_scale_factor      )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    List[str]	,   __lowercase      :    Tuple = None	,   __lowercase      :    Any = False      )							->   Union[str, Any]:
        __UpperCAmelCase							:			List[Any]     =						self.tokenizer(
            lowercase_	,   padding="""max_length"""	,   max_length=self.tokenizer.model_max_length	,   truncation=lowercase_	,   return_tensors="""pt"""	,   )

        __UpperCAmelCase							:			Dict     =						self.text_encoder(text_inputs.input_ids.to(self.device      )      ).last_hidden_state

        __UpperCAmelCase							:			int     =						prompt_embeds.to(dtype=self.text_encoder.dtype	,   device=self.device      )

        if do_classifier_free_guidance:
          __UpperCAmelCase							:			str     =						"""""" if negative_prompt is None else negative_prompt

          __UpperCAmelCase							:			List[str]     =						self.tokenizer(
              lowercase_	,   padding="""max_length"""	,   max_length=self.tokenizer.model_max_length	,   truncation=lowercase_	,   return_tensors="""pt"""	,   )

          __UpperCAmelCase							:			Union[str, Any]     =						self.text_encoder(uncond_input.input_ids.to(self.device      )      ).last_hidden_state

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

        return prompt_embeds
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Tuple	,   __lowercase      :    Tuple      )							->   Optional[int]:
        __UpperCAmelCase							:			int     =						self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        __UpperCAmelCase							:			Optional[int]     =						self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]
      def 			UpperCAmelCase   (   self      :    Union[str, Any]	,   __lowercase      :    Union[str, Any]	,   __lowercase      :    str      )							->   Tuple:
        __UpperCAmelCase							:			Any     =						(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        __UpperCAmelCase							:			Optional[int]     =						(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    List[str]      )							->   Any:
        __UpperCAmelCase							:			List[str]     =						int(lowercase_      )

        __UpperCAmelCase							:			str     =						self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

        return alpha_prod, 1 - alpha_prod
      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    Tuple	,   __lowercase      :    str	,   __lowercase      :    List[Any]	,   __lowercase      :    Union[str, Any]	,   )							->   Dict:
        __UpperCAmelCase							:			str     =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        __UpperCAmelCase   ,				__UpperCAmelCase							:			Union[str, Any]     =						self._get_alpha_and_beta(lowercase_      )
        __UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[int]     =						self._get_alpha_and_beta(lowercase_      )

        __UpperCAmelCase							:			Dict     =						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        __UpperCAmelCase							:			Tuple     =						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

        __UpperCAmelCase							:			Optional[Any]     =						(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    Optional[Any]	,   __lowercase      :    List[Any]	,   __lowercase      :    Tuple	,   __lowercase      :    Dict	,   )							->   Optional[int]:
        __UpperCAmelCase							:			List[Any]     =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        __UpperCAmelCase   ,				__UpperCAmelCase							:			int     =						self._get_alpha_and_beta(lowercase_      )
        __UpperCAmelCase   ,				__UpperCAmelCase							:			List[Any]     =						self._get_alpha_and_beta(lowercase_      )

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

        return model_input, next_model_input.to(base.dtype      )
      @torch.no_grad()
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Optional[int]      )							->   Dict:
        __UpperCAmelCase							:			Tuple     =						1 / self.vae.config.scaling_factor * latents
        __UpperCAmelCase							:			List[str]     =						self.vae.decode(lowercase_      ).sample
        __UpperCAmelCase							:			int     =						(image / 2 + 0.5).clamp(0	,   1      )
        return image
      @torch.no_grad()
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Any	,   __lowercase      :    Optional[int]	,   __lowercase      :    Optional[Any]	,   __lowercase      :    Union[str, Any]	,   __lowercase      :    Optional[Any] = None	,   )							->   str:
        __UpperCAmelCase							:			Any     =						guidance_scale > 1.0

        __UpperCAmelCase							:			Dict     =						image.to(device=self.device	,   dtype=text_embeds.dtype      )
        __UpperCAmelCase							:			Optional[Any]     =						self.vae.encode(lowercase_      ).latent_dist.sample(lowercase_      )

        __UpperCAmelCase							:			int     =						self.vae.config.scaling_factor * latent

        __UpperCAmelCase							:			Dict     =						[latent.clone(), latent.clone()]

        for i, t in tqdm(enumerate(lowercase_      )	,   total=len(lowercase_      )      ):
          __UpperCAmelCase							:			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      ):
            __UpperCAmelCase							:			Tuple     =						j ^ 1

            if self.leapfrog_steps:
              if i % 2 == 0:
                __UpperCAmelCase   ,				__UpperCAmelCase							:			List[Any]     =						j, k

            __UpperCAmelCase							:			Union[str, Any]     =						coupled_latents[j]
            __UpperCAmelCase							:			Optional[int]     =						coupled_latents[k]

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

            __UpperCAmelCase							:			str     =						self.unet(lowercase_	,   lowercase_	,   encoder_hidden_states=lowercase_      ).sample

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

            __UpperCAmelCase   ,				__UpperCAmelCase							:			List[Any]     =						self.noise_step(
                base=lowercase_	,   model_input=lowercase_	,   model_output=lowercase_	,   timestep=lowercase_	,   )

            __UpperCAmelCase							:			Tuple     =						model_input

        return coupled_latents




      @torch.no_grad()
      def __call__(   self      :    List[str]	,   __lowercase      :    Dict	,   __lowercase      :    Optional[Any]	,   __lowercase      :    List[str]	,   __lowercase      :    int = 3.0	,   __lowercase      :    Tuple = 50	,   __lowercase      :    Optional[Any] = 0.8	,   __lowercase      :    str = None	,   __lowercase      :    Optional[int] = None	,   __lowercase      :    List[Any] = "pil"	,   )							->   Tuple:
        __UpperCAmelCase							:			List[str]     =						guidance_scale > 1.0

        __UpperCAmelCase							:			int     =						self.image_processor.preprocess(lowercase_      )

        __UpperCAmelCase							:			Any     =						self._encode_prompt(lowercase_	,   lowercase_	,   lowercase_      )
        __UpperCAmelCase							:			Dict     =						self._encode_prompt(lowercase_	,   lowercase_	,   lowercase_      )

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

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

        __UpperCAmelCase							:			List[str]     =						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      ):
            __UpperCAmelCase							:			int     =						k ^ 1

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

            __UpperCAmelCase							:			List[str]     =						coupled_latents[j]
            __UpperCAmelCase							:			Optional[Any]     =						coupled_latents[k]

            __UpperCAmelCase							:			Dict     =						torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

            __UpperCAmelCase							:			str     =						self.unet(lowercase_	,   lowercase_	,   encoder_hidden_states=lowercase_      ).sample

            if do_classifier_free_guidance:
              __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						noise_pred.chunk(2      )
              __UpperCAmelCase							:			Union[str, Any]     =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            __UpperCAmelCase   ,				__UpperCAmelCase							:			List[str]     =						self.denoise_step(
                base=lowercase_	,   model_input=lowercase_	,   model_output=lowercase_	,   timestep=lowercase_	,   )

            __UpperCAmelCase							:			List[Any]     =						model_input

          __UpperCAmelCase							:			str     =						self.denoise_mixing_layer(x=coupled_latents[0]	,   y=coupled_latents[1]      )

        # either one is fine
        __UpperCAmelCase							:			Any     =						coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
          __UpperCAmelCase							:			Union[str, 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"""	,   lowercase_	,   standard_warn=lowercase_      )
          __UpperCAmelCase							:			Tuple     =						"""np"""

        if output_type == "latent":
          __UpperCAmelCase							:			Tuple     =						final_latent
        else:
          __UpperCAmelCase							:			Union[str, Any]     =						self.decode_latents(lowercase_      )
          __UpperCAmelCase							:			str     =						self.image_processor.postprocess(lowercase_	,   output_type=lowercase_      )

        return image










from typing import TYPE_CHECKING

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


a	: Union[str, Any]      =				{
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				["MobileNetV1FeatureExtractor"]
       a	: int      =				["MobileNetV1ImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				[
           "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileNetV1ForImageClassification",
           "MobileNetV1Model",
           "MobileNetV1PreTrainedModel",
           "load_tf_weights_in_mobilenet_v1",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

       a	: str      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


