





from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(					self					:    List[str]					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





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







  @torch.no_grad()
  def __call__(					self					:    Tuple					, UpperCamelCase					:    str = 1					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    List[str] = 50					, UpperCamelCase					:    Optional[int] = "pil"					, UpperCamelCase					:    Optional[Any] = True					, **UpperCamelCase					:    Dict					, ):


    '''simple docstring'''





    _snake_case      : str														=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					, generator=__UpperCAmelCase					, )
    _snake_case      : List[Any]														=		image.to(self.device							)

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

    for t in self.progress_bar(self.scheduler.timesteps							):
      # 1. predict noise model_output
      _snake_case      : Optional[Any]														=		self.unet(__UpperCAmelCase					, __UpperCAmelCase							).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      : Tuple														=		self.scheduler.step(__UpperCAmelCase					, __UpperCAmelCase					, __UpperCAmelCase							).prev_sample

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

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

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






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

			__A       						=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



