

"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 						__UpperCamelCase     (     __snake_case    ):
  _UpperCAmelCase        =		42
  _UpperCAmelCase        =		42

  def __init__(		self     ,_A     ,_A   ):
      '''simple docstring'''

      super().__init__()
      self.register_modules(unet=__lowerCamelCase     ,scheduler=__lowerCamelCase   )







  @torch.no_grad()
  def __call__(		self     ,_A = 1     ,_A = 50     ,_A = None     ,_A = "pil"     ,_A = True     ,**_A     ,):
      '''simple docstring'''

      _lowerCAmelCase							: Dict	=     self.unet.config.sample_size
      _lowerCAmelCase							: str	=     (batch_size, 3, img_size, img_size)

      _lowerCAmelCase							: Union[str, Any]	=     self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      _lowerCAmelCase							: str	=     randn_tensor(__lowerCamelCase     ,generator=__lowerCamelCase     ,device=self.device   ) * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(__lowerCamelCase   )

      for t in self.progress_bar(self.scheduler.timesteps   ):
          # here sigma_t == t_i from the paper
          _lowerCAmelCase							: Optional[Any]	=     self.scheduler.schedule[t]
          _lowerCAmelCase							: Any	=     self.scheduler.schedule[t - 1] if t > 0 else 0

          # 1. Select temporarily increased noise level sigma_hat
          # 2. Add new noise to move from sample_i to sample_hat
          _lowerCAmelCase,       _lowerCAmelCase							: Tuple	=     self.scheduler.add_noise_to_input(__lowerCamelCase     ,__lowerCamelCase     ,generator=__lowerCamelCase   )

          # 3. Predict the noise residual given the noise magnitude `sigma_hat`
          # The model inputs and output are adjusted by following eq. (213) in [1].
          _lowerCAmelCase							: List[str]	=     (sigma_hat / 2) * model((sample_hat + 1) / 2     ,sigma_hat / 2   ).sample

          # 4. Evaluate dx/dt at sigma_hat
          # 5. Take Euler step from sigma to sigma_prev
          _lowerCAmelCase							: List[Any]	=     self.scheduler.step(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase   )

          if sigma_prev != 0:
              # 6. Apply 2nd order correction
              # The model inputs and output are adjusted by following eq. (213) in [1].
              _lowerCAmelCase							: Optional[int]	=     (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2     ,sigma_prev / 2   ).sample
              _lowerCAmelCase							: Optional[int]	=     self.scheduler.step_correct(
                  __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,step_output.prev_sample     ,step_output['derivative']     ,)
          _lowerCAmelCase							: Tuple	=     step_output.prev_sample

      _lowerCAmelCase							: List[Any]	=     (sample / 2 + 0.5).clamp(0     ,1   )
      _lowerCAmelCase							: Dict	=     sample.cpu().permute(0     ,2     ,3     ,1   ).numpy()
      if output_type == "pil":
          _lowerCAmelCase							: List[str]	=     self.numpy_to_pil(__lowerCamelCase   )

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=__lowerCamelCase   )








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)