

from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class _UpperCamelCase							(							a__      ):
			'''simple docstring'''







			def __init__(	self							:       Dict					,					a							:       Dict					,					a							:       Optional[Any]		)							->				Tuple:

				"""simple docstring"""

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






			@torch.no_grad()
			def __call__(	self							:       List[Any]					,					a							:       Dict = 1					,					a							:       List[str] = None					,					a							:       Dict = 50					,					a							:       List[Any] = "pil"					,					a							:       Union[str, Any] = True					,					**a							:       Any					,					)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,					generator=_UpperCAmelCase					,					)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				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
					SCREAMING_SNAKE_CASE      :					str    =				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
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self.scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase		).prev_sample

				SCREAMING_SNAKE_CASE      :					Dict    =				(image / 2 + 0.5).clamp(0					,					1		)
				SCREAMING_SNAKE_CASE      :					Any    =				image.cpu().permute(0					,					2					,					3					,					1		).numpy()
				if output_type == "pil":
					SCREAMING_SNAKE_CASE      :					int    =				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_torch_available


lowercase__    :      List[str]						=    {
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Any						=    [
      '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GitForCausalLM''',
      '''GitModel''',
      '''GitPreTrainedModel''',
      '''GitVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

  lowercase__    :      List[str]						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)