



'''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			,       A			,       A			,       A			,       A			,       A			,       A = 0.93			,       A = True			,       )   ->					Any:


      """simple docstring"""



      _a     =  mixing_coeff
      _a     =  leapfrog_steps

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

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



 def  a__  (self			,       A			,       A = None			,       A = False	)   ->					int:


      """simple docstring"""



      _a     =  self.tokenizer(
          lowercase_			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=lowercase_			,       return_tensors='''pt'''			,       )

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

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

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

           _a     =  self.tokenizer(
               lowercase_			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=lowercase_			,       return_tensors='''pt'''			,       )

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

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

      return prompt_embeds



 def  a__  (self			,       A			,       A	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  self.mixing_coeff * x + (1 - self.mixing_coeff) * y
      _a     =  self.mixing_coeff * y + (1 - self.mixing_coeff) * x

      return [x, y]



 def  a__  (self			,       A			,       A	)   ->					Dict:


      """simple docstring"""



      _a     =  (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
      _a     =  (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

      return [x, y]



 def  a__  (self			,       A	)   ->					str:


      """simple docstring"""



      _a     =  int(lowercase_	)

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

      return alpha_prod, 1 - alpha_prod



 def  a__  (self			,       A			,       A			,       A			,       A			,       )   ->					Any:


      """simple docstring"""



      _a     =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      _a  ,   _a     =  self._get_alpha_and_beta(lowercase_	)
      _a  ,   _a     =  self._get_alpha_and_beta(lowercase_	)

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

      _a     =  (base - b_t * model_output) / a_t

      return model_input, next_model_input.to(base.dtype	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       )   ->					Any:


      """simple docstring"""



      _a     =  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      _a  ,   _a     =  self._get_alpha_and_beta(lowercase_	)
      _a  ,   _a     =  self._get_alpha_and_beta(lowercase_	)

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

      return model_input, next_model_input.to(base.dtype	)



 @torch.no_grad()
 def  a__  (self			,       A	)   ->					int:


      """simple docstring"""



      _a     =  1 / self.vae.config.scaling_factor * latents
      _a     =  self.vae.decode(lowercase_	).sample
      _a     =  (image / 2 + 0.5).clamp(0			,       1	)
      return image



 @torch.no_grad()
 def  a__  (self			,       A			,       A			,       A			,       A			,       A = None			,       )   ->					str:


      """simple docstring"""



      _a     =  guidance_scale > 1.0

      _a     =  image.to(device=self.device			,       dtype=text_embeds.dtype	)
      _a     =  self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

      _a     =  self.vae.config.scaling_factor * latent

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

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

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

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

                _a     =  coupled_latents[j]
                _a     =  coupled_latents[k]

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

                _a     =  self.unet(lowercase_			,       lowercase_			,       encoder_hidden_states=lowercase_	).sample

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

                _a  ,   _a     =  self.noise_step(
                    base=lowercase_			,       model_input=lowercase_			,       model_output=lowercase_			,       timestep=lowercase_			,       )

                _a     =  model_input

      return coupled_latents



 @torch.no_grad()
 def __call__(self			,       A			,       A			,       A			,       A = 3.0			,       A = 50			,       A = 0.8			,       A = None			,       A = None			,       A = "pil"			,       )   ->					List[Any]:


      """simple docstring"""



      _a     =  guidance_scale > 1.0

      _a     =  self.image_processor.preprocess(lowercase_	)

      _a     =  self._encode_prompt(lowercase_			,       lowercase_			,       lowercase_	)
      _a     =  self._encode_prompt(lowercase_			,       lowercase_			,       lowercase_	)

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

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

      _a     =  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	):
                _a     =  k ^ 1

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

                _a     =  coupled_latents[j]
                _a     =  coupled_latents[k]

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

                _a     =  self.unet(lowercase_			,       lowercase_			,       encoder_hidden_states=lowercase_	).sample

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

                _a  ,   _a     =  self.denoise_step(
                    base=lowercase_			,       model_input=lowercase_			,       model_output=lowercase_			,       timestep=lowercase_			,       )

                _a     =  model_input

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

      # either one is fine
      _a     =  coupled_latents[0]

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

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

      return image








'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

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



