


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE						:str			=  transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]				)->Optional[Any]:
   '''simple docstring'''






   if isinstance(lowerCAmelCase_		,    torch.Tensor				):
      return image
   elif isinstance(lowerCAmelCase_		,    PIL.Image.Image				):
      snake_case_            =       [image]

   snake_case_            =       [trans(img.convert("RGB"				)				) for img in image]
   snake_case_            =       torch.stack(lowerCAmelCase_				)
   return image



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__()

     # make sure scheduler can always be converted to DDIM
     snake_case_            =       DDIMScheduler.from_config(scheduler.config	)

     self.register_modules(unet=_lowerCAmelCase     ,					scheduler=_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[Any]:




     """simple docstring"""
     if strength < 0 or strength > 1:
        raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     # get the original timestep using init_timestep
     snake_case_            =       min(int(num_inference_steps * strength	)     ,					_lowerCAmelCase	)

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

     return timesteps, num_inference_steps - t_start



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Any=None	) ->			List[Any]:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase     ,					(torch.Tensor, PIL.Image.Image, list)	):
        raise ValueError(
            F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase	)}'''	)

     snake_case_            =       image.to(device=_lowerCAmelCase     ,					dtype=_lowerCAmelCase	)

     if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) and len(_lowerCAmelCase	) != batch_size:
        raise ValueError(
            F'''You have passed a list of generators of length {len(_lowerCAmelCase	)}, but requested an effective batch'''
            F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

     snake_case_            =       init_latents.shape
     snake_case_            =       randn_tensor(_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					device=_lowerCAmelCase     ,					dtype=_lowerCAmelCase	)

     # get latents
     print("add noise to latents at timestep"     ,					_lowerCAmelCase	)
     snake_case_            =       self.scheduler.add_noise(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     snake_case_            =       init_latents

     return latents



  @torch.no_grad()
  def __call__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							Union[torch.FloatTensor, PIL.Image.Image] = None     ,					_lowerCAmelCase							:							float = 0.8     ,					_lowerCAmelCase							:							int = 1     ,					_lowerCAmelCase							:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,					_lowerCAmelCase							:							float = 0.0     ,					_lowerCAmelCase							:							int = 5_0     ,					_lowerCAmelCase							:							Optional[bool] = None     ,					_lowerCAmelCase							:							Optional[str] = "pil"     ,					_lowerCAmelCase							:							bool = True     ,					) ->			Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     self.check_inputs(_lowerCAmelCase	)

     # 2. Preprocess image
     snake_case_            =       preprocess(_lowerCAmelCase	)

     # 3. set timesteps
     self.scheduler.set_timesteps(_lowerCAmelCase     ,					device=self.device	)
     snake_case_     ,     snake_case_            =       self.get_timesteps(_lowerCAmelCase     ,					_lowerCAmelCase     ,					self.device	)
     snake_case_            =       timesteps[:1].repeat(_lowerCAmelCase	)

     # 4. Prepare latent variables
     snake_case_            =       self.prepare_latents(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					self.unet.dtype     ,					self.device     ,					_lowerCAmelCase	)
     snake_case_            =       latents

     # 5. Denoising loop
     for t in self.progress_bar(_lowerCAmelCase	):
        # 1. predict noise model_output
        snake_case_            =       self.unet(_lowerCAmelCase     ,					_lowerCAmelCase	).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        snake_case_            =       self.scheduler.step(
            _lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					eta=_lowerCAmelCase     ,					use_clipped_model_output=_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					).prev_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, latent_timestep.item())

     return ImagePipelineOutput(images=_lowerCAmelCase	)








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					*_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[Any]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							str	) ->			Dict:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       eval_examples
     snake_case_            =       post_process_function



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Dataset] = None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[List[str]] = None     ,					_lowerCAmelCase							:							str = "eval"     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Dict[str, float]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()
     snake_case_            =       (
         gen_kwargs["max_length"] if gen_kwargs.get("max_length"	) is not None else self.args.generation_max_length
     )
     snake_case_            =       (
         gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"	) is not None else self.args.generation_num_beams
     )
     snake_case_            =       gen_kwargs

     snake_case_            =       self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_            =       self.get_eval_dataloader(_lowerCAmelCase	)
     snake_case_            =       self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Evaluation"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics
     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
        snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()	):
           if not key.startswith(F'''{metric_key_prefix}_'''	):
              snake_case_            =       metrics.pop(_lowerCAmelCase	)

        metrics.update(output.metrics	)
     else:
        snake_case_            =       output.metrics

     if self.args.should_log:
        # Only the main node log the results by default
        self.log(_lowerCAmelCase	)

     if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()	)

     snake_case_            =       self.callback_handler.on_evaluate(self.args     ,					self.state     ,					self.control     ,					_lowerCAmelCase	)
     return metrics



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str = "test"     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()

     snake_case_            =       self.get_test_dataloader(_lowerCAmelCase	)

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Prediction"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics

     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)
     if self.post_process_function is None or self.compute_metrics is None:
        return output

     snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					"predict"	)
     snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()	):
        if not key.startswith(F'''{metric_key_prefix}_'''	):
           snake_case_            =       metrics.pop(_lowerCAmelCase	)
     metrics.update(output.metrics	)
     return PredictionOutput(predictions=predictions.predictions     ,					label_ids=predictions.label_ids     ,					metrics=_lowerCAmelCase	)




