






"""simple docstring"""



from ... import PretrainedConfig


_UpperCamelCase		:  List[str]						=		{
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}

class        a     (   a_	):
    UpperCAmelCase_ :     int	     =NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase_ :     List[str]	     ="nezha"







    def __init__(			self							,		_lowerCamelCase=2_1_1_2_8							,		_lowerCamelCase=7_6_8							,		_lowerCamelCase=1_2							,		_lowerCamelCase=1_2							,		_lowerCamelCase=3_0_7_2							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.1							,		_lowerCamelCase=5_1_2							,		_lowerCamelCase=6_4							,		_lowerCamelCase=2							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=1e-12							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0							,		_lowerCamelCase=2							,		_lowerCamelCase=3							,		_lowerCamelCase=True							,		**_lowerCamelCase							,		):
      super().__init__(pad_token_id=_lowerCamelCase							,		bos_token_id=_lowerCamelCase							,		eos_token_id=_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       vocab_size
      lowercase							    =       hidden_size
      lowercase							    =       num_hidden_layers
      lowercase							    =       num_attention_heads
      lowercase							    =       hidden_act
      lowercase							    =       intermediate_size
      lowercase							    =       hidden_dropout_prob
      lowercase							    =       attention_probs_dropout_prob
      lowercase							    =       max_position_embeddings
      lowercase							    =       max_relative_position
      lowercase							    =       type_vocab_size
      lowercase							    =       initializer_range
      lowercase							    =       layer_norm_eps
      lowercase							    =       classifier_dropout
      lowercase							    =       use_cache










"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

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

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

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

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

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


