






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







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







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

	return pil_images











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



