

import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput







class  __lowerCamelCase				(	nn.Module	):







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=1.0   )						->       List[str]:
        super().__init__()

        snake_case_       						=      cut_size
        snake_case_       						=      cut_power







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_						,				snake_case_       						=      pixel_values.shape[2:4]
        snake_case_       						=      min(lowerCamelCase							,  lowerCamelCase   )
        snake_case_       						=      min(lowerCamelCase							,  lowerCamelCase							,  self.cut_size   )
        snake_case_       						=      []
        for _ in range(lowerCamelCase   ):
          snake_case_       						=      int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
          snake_case_       						=      torch.randint(0							,  sideX - size + 1							,  ()   )
          snake_case_       						=      torch.randint(0							,  sideY - size + 1							,  ()   )
          snake_case_       						=      pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
          cutouts.append(F.adaptive_avg_poolad(lowerCamelCase							,  self.cut_size   )   )
        return torch.cat(lowerCamelCase   )



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      F.normalize(lowercase_			,					dim=-1							)
  snake_case_       						=      F.normalize(lowercase_			,					dim=-1							)
  return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  for param in model.parameters():
    snake_case_       						=      value







class  __lowerCamelCase				(	__snake_case	):







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  )						->       Any:
        super().__init__()
        self.register_modules(
            vae=lowerCamelCase							,  text_encoder=lowerCamelCase							,  clip_model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  unet=lowerCamelCase							,  scheduler=lowerCamelCase							,  feature_extractor=lowerCamelCase							,  )

        snake_case_       						=      transforms.Normalize(mean=feature_extractor.image_mean							,  std=feature_extractor.image_std   )
        snake_case_       						=      (
            feature_extractor.size
            if isinstance(feature_extractor.size							,  lowerCamelCase   )
            else feature_extractor.size["""shortest_edge"""]
        )
        snake_case_       						=      MakeCutouts(self.cut_out_size   )

        set_requires_grad(self.text_encoder							,  lowerCamelCase   )
        set_requires_grad(self.clip_model							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase = "auto"   )						->       Any:
        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          snake_case_       						=      self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        self.enable_attention_slicing(lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        set_requires_grad(self.vae							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        set_requires_grad(self.vae							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        set_requires_grad(self.unet							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       str:
        set_requires_grad(self.unet							,  lowerCamelCase   )







      @torch.enable_grad()
      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase=True							,  )						->       int:
        snake_case_       						=      latents.detach().requires_grad_()

        snake_case_       						=      self.scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

        # predict the noise residual
        snake_case_       						=      self.unet(lowerCamelCase							,  lowerCamelCase							,  encoder_hidden_states=lowerCamelCase   ).sample

        if isinstance(self.scheduler							,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
          snake_case_       						=      self.scheduler.alphas_cumprod[timestep]
          snake_case_       						=      1 - alpha_prod_t
          # compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          snake_case_       						=      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

          snake_case_       						=      torch.sqrt(lowerCamelCase   )
          snake_case_       						=      pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler							,  lowerCamelCase   ):
          snake_case_       						=      self.scheduler.sigmas[index]
          snake_case_       						=      latents - sigma * noise_pred
        else:
          raise ValueError(f'''scheduler type {type(self.scheduler   )} not supported'''   )

        snake_case_       						=      1 / self.vae.config.scaling_factor * sample
        snake_case_       						=      self.vae.decode(lowerCamelCase   ).sample
        snake_case_       						=      (image / 2 + 0.5).clamp(0							,  1   )

        if use_cutouts:
          snake_case_       						=      self.make_cutouts(lowerCamelCase							,  lowerCamelCase   )
        else:
          snake_case_       						=      transforms.Resize(self.cut_out_size   )(lowerCamelCase   )
        snake_case_       						=      self.normalize(lowerCamelCase   ).to(latents.dtype   )

        snake_case_       						=      self.clip_model.get_image_features(lowerCamelCase   )
        snake_case_       						=      image_embeddings_clip / image_embeddings_clip.norm(p=2							,  dim=-1							,  keepdim=lowerCamelCase   )

        if use_cutouts:
          snake_case_       						=      spherical_dist_loss(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      dists.view([num_cutouts, sample.shape[0], -1]   )
          snake_case_       						=      dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
        else:
          snake_case_       						=      spherical_dist_loss(lowerCamelCase							,  lowerCamelCase   ).mean() * clip_guidance_scale

        snake_case_       						=      -torch.autograd.grad(lowerCamelCase							,  lowerCamelCase   )[0]

        if isinstance(self.scheduler							,  lowerCamelCase   ):
          snake_case_       						=      latents.detach() + grads * (sigma**2)
          snake_case_       						=      noise_pred_original
        else:
          snake_case_       						=      noise_pred_original - torch.sqrt(lowerCamelCase   ) * grads
        return noise_pred, latents







      @torch.no_grad()
      def __call__(     self							,  lowerCamelCase							,  lowerCamelCase = 512							,  lowerCamelCase = 512							,  lowerCamelCase = 50							,  lowerCamelCase = 7.5							,  lowerCamelCase = 1							,  lowerCamelCase = 0.0							,  lowerCamelCase = 100							,  lowerCamelCase = None							,  lowerCamelCase = 4							,  lowerCamelCase = True							,  lowerCamelCase = None							,  lowerCamelCase = None							,  lowerCamelCase = "pil"							,  lowerCamelCase = True							,  )						->       List[Any]:
        if isinstance(lowerCamelCase							,  lowerCamelCase   ):
          snake_case_       						=      1
        elif isinstance(lowerCamelCase							,  lowerCamelCase   ):
          snake_case_       						=      len(lowerCamelCase   )
        else:
          raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase   )}'''   )

        if height % 8 != 0 or width % 8 != 0:
          raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

        # get prompt text embeddings
        snake_case_       						=      self.tokenizer(
            lowerCamelCase							,  padding="""max_length"""							,  max_length=self.tokenizer.model_max_length							,  truncation=lowerCamelCase							,  return_tensors="""pt"""							,  )
        snake_case_       						=      self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
        # duplicate text embeddings for each generation per prompt
        snake_case_       						=      text_embeddings.repeat_interleave(lowerCamelCase							,  dim=0   )

        if clip_guidance_scale > 0:
          if clip_prompt is not None:
            snake_case_       						=      self.tokenizer(
                lowerCamelCase							,  padding="""max_length"""							,  max_length=self.tokenizer.model_max_length							,  truncation=lowerCamelCase							,  return_tensors="""pt"""							,  ).input_ids.to(self.device   )
          else:
            snake_case_       						=      text_input.input_ids.to(self.device   )
          snake_case_       						=      self.clip_model.get_text_features(lowerCamelCase   )
          snake_case_       						=      text_embeddings_clip / text_embeddings_clip.norm(p=2							,  dim=-1							,  keepdim=lowerCamelCase   )
          # duplicate text embeddings clip for each generation per prompt
          snake_case_       						=      text_embeddings_clip.repeat_interleave(lowerCamelCase							,  dim=0   )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        snake_case_       						=      guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
          snake_case_       						=      text_input.input_ids.shape[-1]
          snake_case_       						=      self.tokenizer([""""""]							,  padding="""max_length"""							,  max_length=lowerCamelCase							,  return_tensors="""pt"""   )
          snake_case_       						=      self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
          # duplicate unconditional embeddings for each generation per prompt
          snake_case_       						=      uncond_embeddings.repeat_interleave(lowerCamelCase							,  dim=0   )

          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          snake_case_       						=      torch.cat([uncond_embeddings, text_embeddings]   )

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        snake_case_       						=      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        snake_case_       						=      text_embeddings.dtype
        if latents is None:
          if self.device.type == "mps":
            # randn does not work reproducibly on mps
            snake_case_       						=      torch.randn(lowerCamelCase							,  generator=lowerCamelCase							,  device="""cpu"""							,  dtype=lowerCamelCase   ).to(
                self.device   )
          else:
            snake_case_       						=      torch.randn(lowerCamelCase							,  generator=lowerCamelCase							,  device=self.device							,  dtype=lowerCamelCase   )
        else:
          if latents.shape != latents_shape:
            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
          snake_case_       						=      latents.to(self.device   )

        # set timesteps
        snake_case_       						=      """offset""" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
        snake_case_       						=      {}
        if accepts_offset:
          snake_case_       						=      1

        self.scheduler.set_timesteps(lowerCamelCase							,  **lowerCamelCase   )

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        snake_case_       						=      self.scheduler.timesteps.to(self.device   )

        # scale the initial noise by the standard deviation required by the scheduler
        snake_case_       						=      latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        snake_case_       						=      """eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
        snake_case_       						=      {}
        if accepts_eta:
          snake_case_       						=      eta

        # check if the scheduler accepts generator
        snake_case_       						=      """generator""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
        if accepts_generator:
          snake_case_       						=      generator

        for i, t in enumerate(self.progress_bar(lowerCamelCase   )   ):
          # expand the latents if we are doing classifier free guidance
          snake_case_       						=      torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
          snake_case_       						=      self.scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          # predict the noise residual
          snake_case_       						=      self.unet(lowerCamelCase							,  lowerCamelCase							,  encoder_hidden_states=lowerCamelCase   ).sample

          # perform classifier free guidance
          if do_classifier_free_guidance:
            snake_case_						,				snake_case_       						=      noise_pred.chunk(2   )
            snake_case_       						=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # perform clip guidance
          if clip_guidance_scale > 0:
            snake_case_       						=      (
                text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
            )
            snake_case_						,				snake_case_       						=      self.cond_fn(
                lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  )

          # compute the previous noisy sample x_t -> x_t-1
          snake_case_       						=      self.scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  **lowerCamelCase   ).prev_sample

        # scale and decode the image latents with vae
        snake_case_       						=      1 / self.vae.config.scaling_factor * latents
        snake_case_       						=      self.vae.decode(lowerCamelCase   ).sample

        snake_case_       						=      (image / 2 + 0.5).clamp(0							,  1   )
        snake_case_       						=      image.cpu().permute(0							,  2							,  3							,  1   ).numpy()

        if output_type == "pil":
          snake_case_       						=      self.numpy_to_pil(lowerCamelCase   )

        if not return_dict:
          return (image, None)

        return StableDiffusionPipelineOutput(images=lowerCamelCase							,  nsfw_content_detected=lowerCamelCase   )


from typing import TYPE_CHECKING

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


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)