


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  UpperCamelCase__ ( nn.Module			):






      def __init__(     self					:   List[str],		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Union[str, Any]=1.0   )  -> List[Any]:
           super().__init__()

           UpperCamelCase__							:  Optional[Any]								=     cut_size
           UpperCamelCase__							:  Tuple								=     cut_power






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   str,		__lowerCamelCase					:   str   )  -> Any:
           UpperCamelCase__	,UpperCamelCase__							:  str								=     pixel_values.shape[2:4]
           UpperCamelCase__							:  List[str]								=     min(__lowerCamelCase,		__lowerCamelCase   )
           UpperCamelCase__							:  List[Any]								=     min(__lowerCamelCase,		__lowerCamelCase,		self.cut_size   )
           UpperCamelCase__							:  Dict								=     []
           for _ in range(__lowerCamelCase   ):
                UpperCamelCase__							:  List[Any]								=     int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
                UpperCamelCase__							:  Tuple								=     torch.randint(0,		sideX - size + 1,		()   )
                UpperCamelCase__							:  List[Any]								=     torch.randint(0,		sideY - size + 1,		()   )
                UpperCamelCase__							:  Union[str, Any]								=     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                cutouts.append(F.adaptive_avg_poolad(__lowerCamelCase,		self.cut_size   )   )
           return torch.cat(__lowerCamelCase   )







def 						_lowercase       (     __lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					List[str]			)  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     F.normalize(__snake_case		,dim=-1			)
     UpperCamelCase__							:  Dict								=     F.normalize(__snake_case		,dim=-1			)
     return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)







def 						_lowercase       (     __lowerCamelCase			:					str		,__lowerCamelCase			:					Dict			)  ->   Union[str, Any]:





     '''simple docstring'''





     for param in model.parameters():
          UpperCamelCase__							:  Dict								=     value







class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		__lowerCamelCase					:   int,		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Any,		__lowerCamelCase					:   List[str],		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   List[str],		__lowerCamelCase					:   str,		)  -> Tuple:
           super().__init__()
           self.register_modules(
               vae=__lowerCamelCase,		text_encoder=__lowerCamelCase,		clip_model=__lowerCamelCase,		tokenizer=__lowerCamelCase,		unet=__lowerCamelCase,		scheduler=__lowerCamelCase,		feature_extractor=__lowerCamelCase,		)

           UpperCamelCase__							:  Dict								=     transforms.Normalize(mean=feature_extractor.image_mean,		std=feature_extractor.image_std   )
           UpperCamelCase__							:  Optional[Any]								=     (
               feature_extractor.size
               if isinstance(feature_extractor.size,		__lowerCamelCase   )
               else feature_extractor.size['''shortest_edge''']
           )
           UpperCamelCase__							:  List[Any]								=     MakeCutouts(self.cut_out_size   )

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






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   Tuple = "auto"   )  -> Union[str, Any]:







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






      def 	__lowercase(     self					:   List[Any]   )  -> Optional[int]:
           self.enable_attention_slicing(__lowerCamelCase   )






      def 	__lowercase(     self					:   str   )  -> Optional[Any]:
           set_requires_grad(self.vae,		__lowerCamelCase   )






      def 	__lowercase(     self					:   Tuple   )  -> Optional[Any]:
           set_requires_grad(self.vae,		__lowerCamelCase   )






      def 	__lowercase(     self					:   int   )  -> Tuple:
           set_requires_grad(self.unet,		__lowerCamelCase   )






      def 	__lowercase(     self					:   Dict   )  -> int:
           set_requires_grad(self.unet,		__lowerCamelCase   )






      @torch.enable_grad()
      def 	__lowercase(     self					:   Optional[Any],		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Dict,		__lowerCamelCase					:   Dict=True,		)  -> int:
           UpperCamelCase__							:  Union[str, Any]								=     latents.detach().requires_grad_()

           UpperCamelCase__							:  Union[str, Any]								=     self.scheduler.scale_model_input(__lowerCamelCase,		__lowerCamelCase   )

           # predict the noise residual
           UpperCamelCase__							:  Dict								=     self.unet(__lowerCamelCase,		__lowerCamelCase,		encoder_hidden_states=__lowerCamelCase   ).sample

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

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

           UpperCamelCase__							:  Optional[int]								=     1 / self.vae.config.scaling_factor * sample
           UpperCamelCase__							:  Optional[int]								=     self.vae.decode(__lowerCamelCase   ).sample
           UpperCamelCase__							:  Dict								=     (image / 2 + 0.5).clamp(0,		1   )

           if use_cutouts:
                UpperCamelCase__							:  Optional[int]								=     self.make_cutouts(__lowerCamelCase,		__lowerCamelCase   )
           else:
                UpperCamelCase__							:  int								=     transforms.Resize(self.cut_out_size   )(__lowerCamelCase   )
           UpperCamelCase__							:  Tuple								=     self.normalize(__lowerCamelCase   ).to(latents.dtype   )

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

           if use_cutouts:
                UpperCamelCase__							:  Optional[Any]								=     spherical_dist_loss(__lowerCamelCase,		__lowerCamelCase   )
                UpperCamelCase__							:  int								=     dists.view([num_cutouts, sample.shape[0], -1]   )
                UpperCamelCase__							:  Tuple								=     dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
           else:
                UpperCamelCase__							:  Any								=     spherical_dist_loss(__lowerCamelCase,		__lowerCamelCase   ).mean() * clip_guidance_scale

           UpperCamelCase__							:  Any								=     -torch.autograd.grad(__lowerCamelCase,		__lowerCamelCase   )[0]

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






      @torch.no_grad()
      def __call__(     self					:   int,		__lowerCamelCase					:   Any,		__lowerCamelCase					:   Union[str, Any] = 5_12,		__lowerCamelCase					:   Tuple = 5_12,		__lowerCamelCase					:   Optional[Any] = 50,		__lowerCamelCase					:   Optional[Any] = 7.5,		__lowerCamelCase					:   Any = 1,		__lowerCamelCase					:   int = 0.0,		__lowerCamelCase					:   Any = 1_00,		__lowerCamelCase					:   Dict = None,		__lowerCamelCase					:   Optional[int] = 4,		__lowerCamelCase					:   Dict = True,		__lowerCamelCase					:   Union[str, Any] = None,		__lowerCamelCase					:   Optional[Any] = None,		__lowerCamelCase					:   Optional[int] = "pil",		__lowerCamelCase					:   Union[str, Any] = True,		)  -> Optional[Any]:







           if isinstance(__lowerCamelCase,		__lowerCamelCase   ):
                UpperCamelCase__							:  str								=     1
           elif isinstance(__lowerCamelCase,		__lowerCamelCase   ):
                UpperCamelCase__							:  Tuple								=     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
           UpperCamelCase__							:  Union[str, Any]								=     self.tokenizer(
               __lowerCamelCase,		padding='''max_length''',		max_length=self.tokenizer.model_max_length,		truncation=__lowerCamelCase,		return_tensors='''pt''',		)
           UpperCamelCase__							:  str								=     self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
           # duplicate text embeddings for each generation per prompt
           UpperCamelCase__							:  Optional[Any]								=     text_embeddings.repeat_interleave(__lowerCamelCase,		dim=0   )

           if clip_guidance_scale > 0:
                if clip_prompt is not None:
                     UpperCamelCase__							:  Optional[Any]								=     self.tokenizer(
                         __lowerCamelCase,		padding='''max_length''',		max_length=self.tokenizer.model_max_length,		truncation=__lowerCamelCase,		return_tensors='''pt''',		).input_ids.to(self.device   )
                else:
                     UpperCamelCase__							:  List[str]								=     text_input.input_ids.to(self.device   )
                UpperCamelCase__							:  str								=     self.clip_model.get_text_features(__lowerCamelCase   )
                UpperCamelCase__							:  Union[str, Any]								=     text_embeddings_clip / text_embeddings_clip.norm(p=2,		dim=-1,		keepdim=__lowerCamelCase   )
                # duplicate text embeddings clip for each generation per prompt
                UpperCamelCase__							:  Dict								=     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.
           UpperCamelCase__							:  List[Any]								=     guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                UpperCamelCase__							:  Dict								=     text_input.input_ids.shape[-1]
                UpperCamelCase__							:  Optional[int]								=     self.tokenizer([''''''],		padding='''max_length''',		max_length=__lowerCamelCase,		return_tensors='''pt'''   )
                UpperCamelCase__							:  Union[str, Any]								=     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
                # duplicate unconditional embeddings for each generation per prompt
                UpperCamelCase__							:  List[Any]								=     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
                UpperCamelCase__							:  str								=     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`.
           UpperCamelCase__							:  Dict								=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           UpperCamelCase__							:  Any								=     text_embeddings.dtype
           if latents is None:
                if self.device.type == "mps":
                     # randn does not work reproducibly on mps
                     UpperCamelCase__							:  Any								=     torch.randn(__lowerCamelCase,		generator=__lowerCamelCase,		device='''cpu''',		dtype=__lowerCamelCase   ).to(
                         self.device   )
                else:
                     UpperCamelCase__							:  Dict								=     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}'   )
                UpperCamelCase__							:  Any								=     latents.to(self.device   )

           # set timesteps
           UpperCamelCase__							:  Tuple								=     '''offset''' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
           UpperCamelCase__							:  Optional[int]								=     {}
           if accepts_offset:
                UpperCamelCase__							:  Optional[Any]								=     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
           UpperCamelCase__							:  Any								=     self.scheduler.timesteps.to(self.device   )

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

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

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

                # predict the noise residual
                UpperCamelCase__							:  Optional[int]								=     self.unet(__lowerCamelCase,		__lowerCamelCase,		encoder_hidden_states=__lowerCamelCase   ).sample

                # perform classifier free guidance
                if do_classifier_free_guidance:
                     UpperCamelCase__	,UpperCamelCase__							:  int								=     noise_pred.chunk(2   )
                     UpperCamelCase__							:  Optional[int]								=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # perform clip guidance
                if clip_guidance_scale > 0:
                     UpperCamelCase__							:  Any								=     (
                         text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
                     )
                     UpperCamelCase__	,UpperCamelCase__							:  Union[str, Any]								=     self.cond_fn(
                         __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		)

                # compute the previous noisy sample x_t -> x_t-1
                UpperCamelCase__							:  Union[str, Any]								=     self.scheduler.step(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		**__lowerCamelCase   ).prev_sample

           # scale and decode the image latents with vae
           UpperCamelCase__							:  str								=     1 / self.vae.config.scaling_factor * latents
           UpperCamelCase__							:  Union[str, Any]								=     self.vae.decode(__lowerCamelCase   ).sample

           UpperCamelCase__							:  int								=     (image / 2 + 0.5).clamp(0,		1   )
           UpperCamelCase__							:  Any								=     image.cpu().permute(0,		2,		3,		1   ).numpy()

           if output_type == "pil":
                UpperCamelCase__							:  Optional[int]								=     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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE :					Tuple			=	{
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Any			=	["""MarianTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	[
       """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MarianForCausalLM""",
       """MarianModel""",
       """MarianMTModel""",
       """MarianPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Optional[Any]			=	["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

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




