

'''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"):
     lowerCamelCase			:				str 			=					{
         "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:
     lowerCamelCase			:				List[Any] 			=					{
         "linear": PIL.Image.LINEAR,
         "bilinear": PIL.Image.BILINEAR,
         "bicubic": PIL.Image.BICUBIC,
         "lanczos": PIL.Image.LANCZOS,
         "nearest": PIL.Image.NEAREST,
     }


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any    )				->      str:
     """simple docstring"""




     _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(_UpperCamelCase    )
     return images







def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]    )				->      List[Any]:
     """simple docstring"""




     if images.ndim == 3:
          _SCREAMING_SNAKE_CASE							   =images[None, ...]
     _SCREAMING_SNAKE_CASE							   =(images * 2_55).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(_UpperCamelCase    ) for image in images]

     return pil_images



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
