





'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
      lowercase_					= {
          """linear""": PIL.Image.Resampling.BILINEAR,
          """bilinear""": PIL.Image.Resampling.BILINEAR,
          """bicubic""": PIL.Image.Resampling.BICUBIC,
          """lanczos""": PIL.Image.Resampling.LANCZOS,
          """nearest""": PIL.Image.Resampling.NEAREST,
      }
else:
      lowercase_					= {
          """linear""": PIL.Image.LINEAR,
          """bilinear""": PIL.Image.BILINEAR,
          """bicubic""": PIL.Image.BICUBIC,
          """lanczos""": PIL.Image.LANCZOS,
          """nearest""": PIL.Image.NEAREST,
      }





def lowerCamelCase						(     __lowerCamelCase :    Tuple    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			(images / 2 + 0.5).clamp(0					,       1    )
    _SCREAMING_SNAKE_CASE									=			images.cpu().permute(0					,       2					,       3					,       1    ).float().numpy()
    _SCREAMING_SNAKE_CASE									=			numpy_to_pil(__lowerCamelCase    )
    return images






def lowerCamelCase						(     __lowerCamelCase :    Any    )						->Dict:
    if images.ndim == 3:
        _SCREAMING_SNAKE_CASE									=			images[None, ...]
    _SCREAMING_SNAKE_CASE									=			(images * 255).round().astype("""uint8"""    )
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        _SCREAMING_SNAKE_CASE									=			[Image.fromarray(image.squeeze()					,       mode="""L"""    ) for image in images]
    else:
        _SCREAMING_SNAKE_CASE									=			[Image.fromarray(__lowerCamelCase    ) for image in images]

    return pil_images










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



