





"""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 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :UNetaDModel
       __magic_name__     :KarrasVeScheduler






       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       @torch.no_grad()
       def __call__(    self    , __UpperCAmelCase = 1    , __UpperCAmelCase = 5_0    , __UpperCAmelCase = None    , __UpperCAmelCase = "pil"    , __UpperCAmelCase = True    , **__UpperCAmelCase    , ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							self.unet.config.sample_size
         lowerCAmelCase__			:Union[str, Any]											=							(batch_size, 3, img_size, img_size)

         lowerCAmelCase__			:Tuple											=							self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         lowerCAmelCase__			:Optional[int]											=							randn_tensor(__UpperCAmelCase    , generator=__UpperCAmelCase    , device=self.device      ) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__UpperCAmelCase      )

         for t in self.progress_bar(self.scheduler.timesteps      ):
           # here sigma_t == t_i from the paper
           lowerCAmelCase__			:List[str]											=							self.scheduler.schedule[t]
           lowerCAmelCase__			:int											=							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__			:int											=							self.scheduler.add_noise_to_input(__UpperCAmelCase    , __UpperCAmelCase    , generator=__UpperCAmelCase      )

           # 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__			:Union[str, Any]											=							(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(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )

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

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

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=__UpperCAmelCase      )







"""simple docstring"""





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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

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

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							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(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

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

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

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

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
