"""simple docstring"""



from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest

@custom_tokenizers
class SCREAMING_SNAKE_CASE     (    a_	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Union[str, Any]					,   _snake_case		:      str					,   _snake_case		:      Any					,   _snake_case		:      str					,   _snake_case		:      str					,   _snake_case		:      List[Any]				)				->			Tuple:




    '''simple docstring'''






    return True



 def        _lowerCAmelCase				(   self		:      Dict				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'				)
    a__     				=		'Hugging Face大法好，谁用谁知道。'
    a__     				=		'Hugging Face大法好,谁用谁知道。<unk>'
    a__     				=		'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

    a__     				=		tokenizer.tokenize(_snake_case				)
    self.assertListEqual(_snake_case					,   _snake_case				)

    a__     				=		tokens + [tokenizer.unk_token]

    a__     				=		[1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case				)					,   _snake_case				)

    a__     				=		tokenizer.decode(_snake_case				)
    self.assertEqual(_snake_case					,   _snake_case				)





"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def __init__(   self		:      Dict					,   _snake_case		:      List[str]					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






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



 @torch.no_grad()
 def __call__(   self		:      int					,   _snake_case		:      int = 1					,   _snake_case		:      Optional[torch.Generator] = None					,   _snake_case		:      int = 50					,   _snake_case		:      Optional[str] = "pil"					,   _snake_case		:      bool = True					,   **_snake_case		:      str					,   )				->			Union[ImagePipelineOutput, Tuple]:




    '''simple docstring'''






    a__     				=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,   generator=_snake_case					,   )
    a__     				=		image.to(self.device				)

    # set step values
    self.scheduler.set_timesteps(_snake_case				)

    for t in self.progress_bar(self.scheduler.timesteps				):
       # 1. predict noise model_output
       a__     				=		self.unet(_snake_case					,   _snake_case				).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
       a__     				=		self.scheduler.step(_snake_case					,   _snake_case					,   _snake_case				).prev_sample

    a__     				=		(image / 2 + 0.5).clamp(0					,   1				)
    a__     				=		image.cpu().permute(0					,   2					,   3					,   1				).numpy()
    if output_type == "pil":
       a__     				=		self.numpy_to_pil(_snake_case				)

    if not return_dict:
       return (image,), "This is a local test"

    return ImagePipelineOutput(images=_snake_case				), "This is a local test"


