

'''simple docstring'''

from typing import TYPE_CHECKING

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


A_       						=							{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_       						=							[
        "RagModel",
        "RagPreTrainedModel",
        "RagSequenceForGeneration",
        "RagTokenForGeneration",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_       						=							[
        "TFRagModel",
        "TFRagPreTrainedModel",
        "TFRagSequenceForGeneration",
        "TFRagTokenForGeneration",
    ]


if TYPE_CHECKING:
    from .configuration_rag import RagConfig
    from .retrieval_rag import RagRetriever
    from .tokenization_rag import RagTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rag import (
            TFRagModel,
            TFRagPreTrainedModel,
            TFRagSequenceForGeneration,
            TFRagTokenForGeneration,
        )

else:
    import sys

    A_       						=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''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"):
    A_       						=							{
        "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:
    A_       						=							{
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					(images / 2 + 0.5).clamp(0      ,       1 )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					images.cpu().permute(0      ,       2      ,       3      ,       1 ).float().numpy()
  SCREAMING_SNAKE_CASE:str						      =					numpy_to_pil(snake_case )
  return images





def    A_  ( snake_case ):
  if images.ndim == 3:
    SCREAMING_SNAKE_CASE:List[str]						      =					images[None, ...]
  SCREAMING_SNAKE_CASE:List[Any]						      =					(images * 255).round().astype("uint8" )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE:Any						      =					[Image.fromarray(image.squeeze()      ,       mode="L" ) for image in images]
  else:
    SCREAMING_SNAKE_CASE:int						      =					[Image.fromarray(snake_case ) for image in images]

  return pil_images






