






"""simple docstring"""





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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



def     __lowercase					(    a :				Dict       ,		a :				Tuple       ,		a :				Optional[Any] )					->					Union[str, Any]:
      if isinstance(a       ,		torch.Tensor ):
            return image
      elif isinstance(a       ,		PIL.Image.Image ):
            __snake_case      :    Any  			=[image]

      if isinstance(image[0]       ,		PIL.Image.Image ):
            __snake_case      :    Optional[Any]  			=[np.array(i.resize((w, h)       ,		resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
            __snake_case      :    Union[str, Any]  			=np.concatenate(a       ,		axis=0 )
            __snake_case      :    List[Any]  			=np.array(a ).astype(np.floataa ) / 2_5_5.0
            __snake_case      :    Optional[int]  			=image.transpose(0       ,		3       ,		1       ,		2 )
            __snake_case      :    Union[str, Any]  			=2.0 * image - 1.0
            __snake_case      :    Dict  			=torch.from_numpy(a )
      elif isinstance(image[0]       ,		torch.Tensor ):
            __snake_case      :    str  			=torch.cat(a       ,		dim=0 )
      return image



def     __lowercase					(    a :				Any       ,		a :				List[Any]       ,		a :				Any       ,		a :				int=0.9_9_9_5 )					->					List[str]:
      if not isinstance(a       ,		np.ndarray ):
            __snake_case      :    List[Any]  			=True
            __snake_case      :    int  			=va.device
            __snake_case      :    Union[str, Any]  			=va.cpu().numpy()
            __snake_case      :    Optional[int]  			=va.cpu().numpy()

      __snake_case      :    Optional[Any]  			=np.sum(va * va / (np.linalg.norm(a ) * np.linalg.norm(a )) )
      if np.abs(a ) > DOT_THRESHOLD:
            __snake_case      :    Dict  			=(1 - t) * va + t * va
      else:
            __snake_case      :    str  			=np.arccos(a )
            __snake_case      :    int  			=np.sin(a )
            __snake_case      :    str  			=theta_a * t
            __snake_case      :    Optional[Any]  			=np.sin(a )
            __snake_case      :    str  			=np.sin(theta_a - theta_t ) / sin_theta_a
            __snake_case      :    Any  			=sin_theta_t / sin_theta_a
            __snake_case      :    Optional[int]  			=sa * va + sa * va

      if inputs_are_torch:
            __snake_case      :    Dict  			=torch.from_numpy(a ).to(a )

      return va



def     __lowercase					(    a :				int       ,		a :				int )					->					List[str]:
      __snake_case      :    str  			=F.normalize(a       ,		dim=-1 )
      __snake_case      :    List[Any]  			=F.normalize(a       ,		dim=-1 )
      return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )



def     __lowercase					(    a :				Dict       ,		a :				str )					->					List[Any]:
      for param in model.parameters():
            __snake_case      :    Dict  			=value


class      _lowercase (     lowerCAmelCase						):


      def __init__(    self				:	Tuple							,   a				:	AutoencoderKL							,   a				:	CLIPTextModel							,   a				:	CLIPModel							,   a				:	CLIPTokenizer							,   a				:	UNetaDConditionModel							,   a				:	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,   a				:	CLIPFeatureExtractor							,   a				:	List[Any]=None							,   a				:	List[str]=None							,   a				:	Optional[int]=None							,   ):





            """simple docstring"""







            super().__init__()
            self.register_modules(
                vae=a							,   text_encoder=a							,   clip_model=a							,   tokenizer=a							,   unet=a							,   scheduler=a							,   feature_extractor=a							,   coca_model=a							,   coca_tokenizer=a							,   coca_transform=a							,   )
            __snake_case      :    List[str]  			=(
                feature_extractor.size
                if isinstance(feature_extractor.size							,   a						)
                else feature_extractor.size['''shortest_edge''']
            )
            __snake_case      :    List[Any]  			=transforms.Normalize(mean=feature_extractor.image_mean							,   std=feature_extractor.image_std						)
            set_requires_grad(self.text_encoder							,   a						)
            set_requires_grad(self.clip_model							,   a						)


      def _UpperCamelCase						(    self				:	List[Any]							,   a				:	Optional[Union[str, int]] = "auto"						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            self.enable_attention_slicing(a						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            set_requires_grad(self.vae							,   a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            set_requires_grad(self.vae							,   a						)


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            set_requires_grad(self.unet							,   a						)


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            set_requires_grad(self.unet							,   a						)


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	Any							,   a				:	str							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    str  			=min(int(num_inference_steps * strength						)							,   a						)

            __snake_case      :    Any  			=max(num_inference_steps - init_timestep							,   0						)
            __snake_case      :    Optional[Any]  			=self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start


      def _UpperCamelCase						(    self				:	int							,   a				:	int							,   a				:	List[str]							,   a				:	List[str]							,   a				:	Optional[Any]							,   a				:	Optional[Any]							,   a				:	List[Any]=None						):





            """simple docstring"""







            if not isinstance(a							,   torch.Tensor						):
                  raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(a						)}'''						)

            __snake_case      :    Optional[int]  			=image.to(device=a							,   dtype=a						)

            if isinstance(a							,   a						):
                  __snake_case      :    Optional[Any]  			=[
                      self.vae.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(a						)
                  ]
                  __snake_case      :    Union[str, Any]  			=torch.cat(a							,   dim=0						)
            else:
                  __snake_case      :    Optional[Any]  			=self.vae.encode(a						).latent_dist.sample(a						)

            # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            __snake_case      :    Union[str, Any]  			=0.1_8_2_1_5 * init_latents
            __snake_case      :    Union[str, Any]  			=init_latents.repeat_interleave(a							,   dim=0						)

            __snake_case      :    List[str]  			=randn_tensor(init_latents.shape							,   generator=a							,   device=a							,   dtype=a						)

            # get latents
            __snake_case      :    Optional[Any]  			=self.scheduler.add_noise(a							,   a							,   a						)
            __snake_case      :    List[str]  			=init_latents

            return latents


      def _UpperCamelCase						(    self				:	str							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=self.coca_transform(a						).unsqueeze(0						)
            with torch.no_grad(), torch.cuda.amp.autocast():
                  __snake_case      :    List[str]  			=self.coca_model.generate(transformed_image.to(device=self.device							,   dtype=self.coca_model.dtype						)						)
            __snake_case      :    int  			=self.coca_tokenizer.decode(generated[0].cpu().numpy()						)
            return generated.split('''<end_of_text>'''						)[0].replace('''<start_of_text>'''							,   ''''''						).rstrip(''' .,'''						)


      def _UpperCamelCase						(    self				:	str							,   a				:	List[Any]							,   a				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.feature_extractor.preprocess(a						)
            __snake_case      :    Union[str, Any]  			=torch.from_numpy(clip_image_input['''pixel_values'''][0]						).unsqueeze(0						).to(self.device						).half()
            __snake_case      :    Tuple  			=self.clip_model.get_image_features(a						)
            __snake_case      :    Optional[int]  			=image_embeddings_clip / image_embeddings_clip.norm(p=2							,   dim=-1							,   keepdim=a						)
            __snake_case      :    Union[str, Any]  			=image_embeddings_clip.repeat_interleave(a							,   dim=0						)
            return image_embeddings_clip


      @torch.enable_grad()
      def _UpperCamelCase						(    self				:	Dict							,   a				:	str							,   a				:	Any							,   a				:	Dict							,   a				:	Optional[int]							,   a				:	Optional[int]							,   a				:	int							,   a				:	Any							,   ):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=latents.detach().requires_grad_()

            __snake_case      :    Any  			=self.scheduler.scale_model_input(a							,   a						)

            # predict the noise residual
            __snake_case      :    Optional[Any]  			=self.unet(a							,   a							,   encoder_hidden_states=a						).sample

            if isinstance(self.scheduler							,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
                  __snake_case      :    Optional[Any]  			=self.scheduler.alphas_cumprod[timestep]
                  __snake_case      :    Tuple  			=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      :    List[Any]  			=(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                  __snake_case      :    Optional[Any]  			=torch.sqrt(a						)
                  __snake_case      :    Union[str, Any]  			=pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler							,   a						):
                  __snake_case      :    int  			=self.scheduler.sigmas[index]
                  __snake_case      :    Optional[Any]  			=latents - sigma * noise_pred
            else:
                  raise ValueError(f'''scheduler type {type(self.scheduler						)} not supported'''						)

            # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            __snake_case      :    List[str]  			=1 / 0.1_8_2_1_5 * sample
            __snake_case      :    Optional[Any]  			=self.vae.decode(a						).sample
            __snake_case      :    List[str]  			=(image / 2 + 0.5).clamp(0							,   1						)

            __snake_case      :    Union[str, Any]  			=transforms.Resize(self.feature_extractor_size						)(a						)
            __snake_case      :    List[Any]  			=self.normalize(a						).to(latents.dtype						)

            __snake_case      :    Optional[int]  			=self.clip_model.get_image_features(a						)
            __snake_case      :    List[Any]  			=image_embeddings_clip / image_embeddings_clip.norm(p=2							,   dim=-1							,   keepdim=a						)

            __snake_case      :    Optional[Any]  			=spherical_dist_loss(a							,   a						).mean() * clip_guidance_scale

            __snake_case      :    Optional[int]  			=-torch.autograd.grad(a							,   a						)[0]

            if isinstance(self.scheduler							,   a						):
                  __snake_case      :    List[Any]  			=latents.detach() + grads * (sigma**2)
                  __snake_case      :    Optional[Any]  			=noise_pred_original
            else:
                  __snake_case      :    Optional[int]  			=noise_pred_original - torch.sqrt(a						) * grads
            return noise_pred, latents







      @torch.no_grad()
      def __call__(    self				:	str							,   a				:	Union[torch.FloatTensor, PIL.Image.Image]							,   a				:	Union[torch.FloatTensor, PIL.Image.Image]							,   a				:	Optional[str] = None							,   a				:	Optional[str] = None							,   a				:	Optional[int] = 5_1_2							,   a				:	Optional[int] = 5_1_2							,   a				:	float = 0.6							,   a				:	Optional[int] = 5_0							,   a				:	Optional[float] = 7.5							,   a				:	Optional[int] = 1							,   a				:	float = 0.0							,   a				:	Optional[float] = 1_0_0							,   a				:	Optional[torch.Generator] = None							,   a				:	Optional[str] = "pil"							,   a				:	bool = True							,   a				:	float = 0.8							,   a				:	float = 0.1							,   a				:	float = 0.1							,   ):





            """simple docstring"""







            if isinstance(a							,   a						) and len(a						) != batch_size:
                  raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(a						)} generators.'''						)

            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}.'''						)

            if isinstance(a							,   torch.Generator						) and batch_size > 1:
                  __snake_case      :    int  			=[generator] + [None] * (batch_size - 1)

            __snake_case      :    Dict  			=[
                ('''model''', self.coca_model is None),
                ('''tokenizer''', self.coca_tokenizer is None),
                ('''transform''', self.coca_transform is None),
            ]
            __snake_case      :    int  			=[x[0] for x in coca_is_none if x[1]]
            __snake_case      :    Any  			=''', '''.join(a						)
            # generate prompts with coca model if prompt is None
            if content_prompt is None:
                  if len(a						):
                        raise ValueError(
                            f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                            f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''						)
                  __snake_case      :    Optional[int]  			=self.get_image_description(a						)
            if style_prompt is None:
                  if len(a						):
                        raise ValueError(
                            f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                            f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''						)
                  __snake_case      :    List[str]  			=self.get_image_description(a						)

            # get prompt text embeddings for content and style
            __snake_case      :    List[Any]  			=self.tokenizer(
                a							,   padding='''max_length'''							,   max_length=self.tokenizer.model_max_length							,   truncation=a							,   return_tensors='''pt'''							,   )
            __snake_case      :    List[Any]  			=self.text_encoder(content_text_input.input_ids.to(self.device						)						)[0]

            __snake_case      :    int  			=self.tokenizer(
                a							,   padding='''max_length'''							,   max_length=self.tokenizer.model_max_length							,   truncation=a							,   return_tensors='''pt'''							,   )
            __snake_case      :    Tuple  			=self.text_encoder(style_text_input.input_ids.to(self.device						)						)[0]

            __snake_case      :    Any  			=slerp(a							,   a							,   a						)

            # duplicate text embeddings for each generation per prompt
            __snake_case      :    Optional[int]  			=text_embeddings.repeat_interleave(a							,   dim=0						)

            # set timesteps
            __snake_case      :    List[Any]  			='''offset''' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
            __snake_case      :    List[Any]  			={}
            if accepts_offset:
                  __snake_case      :    Dict  			=1

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

            __snake_case			,				__snake_case      :    Optional[Any]  			=self.get_timesteps(a							,   a							,   self.device						)
            __snake_case      :    Any  			=timesteps[:1].repeat(a						)

            # Preprocess image
            __snake_case      :    Optional[Any]  			=preprocess(a							,   a							,   a						)
            __snake_case      :    int  			=self.prepare_latents(
                a							,   a							,   a							,   text_embeddings.dtype							,   self.device							,   a						)

            __snake_case      :    Union[str, Any]  			=preprocess(a							,   a							,   a						)
            __snake_case      :    List[Any]  			=self.prepare_latents(
                a							,   a							,   a							,   text_embeddings.dtype							,   self.device							,   a						)

            __snake_case      :    Any  			=slerp(a							,   a							,   a						)

            if clip_guidance_scale > 0:
                  __snake_case      :    str  			=self.get_clip_image_embeddings(a							,   a						)
                  __snake_case      :    List[Any]  			=self.get_clip_image_embeddings(a							,   a						)
                  __snake_case      :    Tuple  			=slerp(
                      a							,   a							,   a						)

            # 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      :    List[Any]  			=guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                  __snake_case      :    Union[str, Any]  			=content_text_input.input_ids.shape[-1]
                  __snake_case      :    str  			=self.tokenizer(['''''']							,   padding='''max_length'''							,   max_length=a							,   return_tensors='''pt'''						)
                  __snake_case      :    List[str]  			=self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
                  # duplicate unconditional embeddings for each generation per prompt
                  __snake_case      :    str  			=uncond_embeddings.repeat_interleave(a							,   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      :    Union[str, Any]  			=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      :    int  			=(batch_size, self.unet.config.in_channels, height // 8, width // 8)
            __snake_case      :    int  			=text_embeddings.dtype
            if latents is None:
                  if self.device.type == "mps":
                        # randn does not work reproducibly on mps
                        __snake_case      :    Union[str, Any]  			=torch.randn(a							,   generator=a							,   device='''cpu'''							,   dtype=a						).to(
                            self.device						)
                  else:
                        __snake_case      :    Any  			=torch.randn(a							,   generator=a							,   device=self.device							,   dtype=a						)
            else:
                  if latents.shape != latents_shape:
                        raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''						)
                  __snake_case      :    Optional[Any]  			=latents.to(self.device						)

            # scale the initial noise by the standard deviation required by the scheduler
            __snake_case      :    Optional[int]  			=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      :    List[str]  			='''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
            __snake_case      :    Dict  			={}
            if accepts_eta:
                  __snake_case      :    Tuple  			=eta

            # check if the scheduler accepts generator
            __snake_case      :    List[str]  			='''generator''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
            if accepts_generator:
                  __snake_case      :    Optional[Any]  			=generator

            with self.progress_bar(total=a						):
                  for i, t in enumerate(a						):
                        # expand the latents if we are doing classifier free guidance
                        __snake_case      :    List[Any]  			=torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
                        __snake_case      :    int  			=self.scheduler.scale_model_input(a							,   a						)

                        # predict the noise residual
                        __snake_case      :    int  			=self.unet(a							,   a							,   encoder_hidden_states=a						).sample

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

                        # perform clip guidance
                        if clip_guidance_scale > 0:
                              __snake_case      :    Optional[Any]  			=(
                                  text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
                              )
                              __snake_case			,				__snake_case      :    Optional[int]  			=self.cond_fn(
                                  a							,   a							,   a							,   a							,   a							,   a							,   a							,   )

                        # compute the previous noisy sample x_t -> x_t-1
                        __snake_case      :    int  			=self.scheduler.step(a							,   a							,   a							,   **a						).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            __snake_case      :    List[Any]  			=1 / 0.1_8_2_1_5 * latents
            __snake_case      :    Optional[int]  			=self.vae.decode(a						).sample

            __snake_case      :    Optional[Any]  			=(image / 2 + 0.5).clamp(0							,   1						)
            __snake_case      :    Tuple  			=image.cpu().permute(0							,   2							,   3							,   1						).numpy()

            if output_type == "pil":
                  __snake_case      :    Dict  			=self.numpy_to_pil(a						)

            if not return_dict:
                  return (image, None)

            return StableDiffusionPipelineOutput(images=a							,   nsfw_content_detected=a						)










"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_	:				str										= {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Union[str, Any]										= ["""LayoutXLMTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


