





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 			__A(    nn.Module			):

     def __init__(   self,							A,							A=1.0   ):





            """simple docstring"""

            super().__init__()

            _UpperCamelCase														=    cut_size
            _UpperCamelCase														=    cut_power



     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

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






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    F.normalize(__UpperCamelCase     ,							dim=-1       )
       _UpperCamelCase														=    F.normalize(__UpperCamelCase     ,							dim=-1       )
       return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):





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

class 			__A(    lowerCamelCase_			):

     def __init__(   self,							A,							A,							A,							A,							A,							A,							A,							):





            """simple docstring"""

            super().__init__()
            self.register_modules(
                vae=_lowercase,							text_encoder=_lowercase,							clip_model=_lowercase,							tokenizer=_lowercase,							unet=_lowercase,							scheduler=_lowercase,							feature_extractor=_lowercase,							)

            _UpperCamelCase														=    transforms.Normalize(mean=feature_extractor.image_mean,							std=feature_extractor.image_std   )
            _UpperCamelCase														=    (
                feature_extractor.size
                if isinstance(feature_extractor.size,							_lowercase   )
                else feature_extractor.size['''shortest_edge''']
            )
            _UpperCamelCase														=    MakeCutouts(self.cut_out_size   )

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

     def 						_UpperCamelCase  (   self,							A = "auto"   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self.enable_attention_slicing(_lowercase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            set_requires_grad(self.vae,							_lowercase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            set_requires_grad(self.vae,							_lowercase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            set_requires_grad(self.unet,							_lowercase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            set_requires_grad(self.unet,							_lowercase   )

     @torch.enable_grad()
     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							A,							A,							A,							A,							A=True,							):





            """simple docstring"""

            _UpperCamelCase														=    latents.detach().requires_grad_()

            _UpperCamelCase														=    self.scheduler.scale_model_input(_lowercase,							_lowercase   )

            # predict the noise residual
            _UpperCamelCase														=    self.unet(_lowercase,							_lowercase,							encoder_hidden_states=_lowercase   ).sample

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

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

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

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

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

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

            _UpperCamelCase														=    -torch.autograd.grad(_lowercase,							_lowercase   )[0]

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



     @torch.no_grad()
     def __call__(   self,							A,							A = 512,							A = 512,							A = 50,							A = 7.5,							A = 1,							A = 0.0,							A = 100,							A = None,							A = 4,							A = True,							A = None,							A = None,							A = "pil",							A = True,							):





            """simple docstring"""

            if isinstance(_lowercase,							_lowercase   ):
                   _UpperCamelCase														=    1
            elif isinstance(_lowercase,							_lowercase   ):
                   _UpperCamelCase														=    len(_lowercase   )
            else:
                   raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_lowercase   )}'''   )

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

            if clip_guidance_scale > 0:
                   if clip_prompt is not None:
                          _UpperCamelCase														=    self.tokenizer(
                              _lowercase,							padding='''max_length''',							max_length=self.tokenizer.model_max_length,							truncation=_lowercase,							return_tensors='''pt''',							).input_ids.to(self.device   )
                   else:
                          _UpperCamelCase														=    text_input.input_ids.to(self.device   )
                   _UpperCamelCase														=    self.clip_model.get_text_features(_lowercase   )
                   _UpperCamelCase														=    text_embeddings_clip / text_embeddings_clip.norm(p=2,							dim=-1,							keepdim=_lowercase   )
                   # duplicate text embeddings clip for each generation per prompt
                   _UpperCamelCase														=    text_embeddings_clip.repeat_interleave(_lowercase,							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														=    guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                   _UpperCamelCase														=    text_input.input_ids.shape[-1]
                   _UpperCamelCase														=    self.tokenizer([''''''],							padding='''max_length''',							max_length=_lowercase,							return_tensors='''pt'''   )
                   _UpperCamelCase														=    self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
                   # duplicate unconditional embeddings for each generation per prompt
                   _UpperCamelCase														=    uncond_embeddings.repeat_interleave(_lowercase,							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														=    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														=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            _UpperCamelCase														=    text_embeddings.dtype
            if latents is None:
                   if self.device.type == "mps":
                          # randn does not work reproducibly on mps
                          _UpperCamelCase														=    torch.randn(_lowercase,							generator=_lowercase,							device='''cpu''',							dtype=_lowercase   ).to(
                              self.device   )
                   else:
                          _UpperCamelCase														=    torch.randn(_lowercase,							generator=_lowercase,							device=self.device,							dtype=_lowercase   )
            else:
                   if latents.shape != latents_shape:
                          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
                   _UpperCamelCase														=    latents.to(self.device   )

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

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

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

            # scale the initial noise by the standard deviation required by the scheduler
            _UpperCamelCase														=    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														=    '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
            _UpperCamelCase														=    {}
            if accepts_eta:
                   _UpperCamelCase														=    eta

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

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

                   # predict the noise residual
                   _UpperCamelCase														=    self.unet(_lowercase,							_lowercase,							encoder_hidden_states=_lowercase   ).sample

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

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

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

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

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

            if output_type == "pil":
                   _UpperCamelCase														=    self.numpy_to_pil(_lowercase   )

            if not return_dict:
                   return (image, None)

            return StableDiffusionPipelineOutput(images=_lowercase,							nsfw_content_detected=_lowercase   )







# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
