


'''simple docstring'''







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCAmelCase     :  List[str]											=    logging.get_logger(__name__)
class 						UpperCamelCase__       (			_a						):



     """simple docstring"""






     __magic_name__           =     ["pixel_values"]







     def __init__(			self   ,						snake_case__ = True   ,						snake_case__ = 1 / 255   ,						snake_case__ = True   ,						snake_case__ = 8   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(**__lowerCamelCase )

            _lowerCAmelCase     :				Union[str, Any]       =							do_rescale
            _lowerCAmelCase     :				List[Any]       =							rescale_factor
            _lowerCAmelCase     :				Optional[Any]       =							do_pad
            _lowerCAmelCase     :				str       =							pad_size







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = None   ,						**snake_case__ ):

            '''simple docstring'''

            return rescale(__lowerCamelCase   ,						scale=__lowerCamelCase   ,						data_format=__lowerCamelCase   ,						**__lowerCamelCase )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							get_image_size(__lowerCamelCase )
            _lowerCAmelCase     :				str       =							(old_height // size + 1) * size - old_height
            _lowerCAmelCase     :				Tuple       =							(old_width // size + 1) * size - old_width

            return pad(__lowerCamelCase   ,						((0, pad_height), (0, pad_width))   ,						mode='symmetric'   ,						data_format=__lowerCamelCase )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = ChannelDimension.FIRST   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase     :				Union[str, Any]       =							rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase     :				Tuple       =							do_pad if do_pad is not None else self.do_pad
            _lowerCAmelCase     :				Optional[int]       =							pad_size if pad_size is not None else self.pad_size

            _lowerCAmelCase     :				Tuple       =							make_list_of_images(__lowerCamelCase )

            if not valid_images(__lowerCamelCase ):
                   raise ValueError(
                       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                       'torch.Tensor, tf.Tensor or jax.ndarray.' )

            if do_rescale and rescale_factor is None:
                   raise ValueError('Rescale factor must be specified if do_rescale is True.' )

            # All transformations expect numpy arrays.
            _lowerCAmelCase     :				Dict       =							[to_numpy_array(__lowerCamelCase ) for image in images]

            if do_rescale:
                   _lowerCAmelCase     :				str       =							[self.rescale(image=__lowerCamelCase   ,						scale=__lowerCamelCase ) for image in images]

            if do_pad:
                   _lowerCAmelCase     :				Dict       =							[self.pad(__lowerCamelCase   ,						size=__lowerCamelCase ) for image in images]

            _lowerCAmelCase     :				List[Any]       =							[to_channel_dimension_format(__lowerCamelCase   ,						__lowerCamelCase ) for image in images]

            _lowerCAmelCase     :				int       =							{'pixel_values': images}
            return BatchFeature(data=__lowerCamelCase   ,						tensor_type=__lowerCamelCase )







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Any					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

