


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


__lowercase    =				logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	["""pixel_values"""]






    def __init__( self    :   int    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   int = 0.9    ,			__UpperCAmelCase    :   PILImageResampling = PILImageResampling.BICUBIC    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   Union[int, float] = 1 / 255    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			**__UpperCAmelCase    :   Optional[int]    ,			):
      super().__init__(**__UpperCAmelCase)
      a						:		str											=      size if size is not None else {"shortest_edge": 224}
      a						:		str											=      get_size_dict(__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase)
      a						:		str											=      crop_size if crop_size is not None else {"height": 224, "width": 224}
      a						:		List[str]											=      get_size_dict(__UpperCAmelCase    ,			param_name="crop_size")

      a						:		List[str]											=      do_resize
      a						:		int											=      size
      a						:		List[Any]											=      crop_pct
      a						:		str											=      resample
      a						:		Any											=      do_center_crop
      a						:		Optional[Any]											=      crop_size
      a						:		Dict											=      do_rescale
      a						:		Union[str, Any]											=      rescale_factor
      a						:		List[Any]											=      do_normalize
      a						:		Tuple											=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      a						:		Optional[int]											=      image_std if image_std is not None else IMAGENET_DEFAULT_STD






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Dict[str, int]    ,			__UpperCAmelCase    :   Optional[float] = None    ,			__UpperCAmelCase    :   PILImageResampling = PILImageResampling.BICUBIC    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   Optional[int]    ,			):
      a						:		int											=      get_size_dict(__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase)
      if "shortest_edge" not in size and ("height" not in size or "width" not in size):
        raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
      if crop_pct is not None:
        if "shortest_edge" in size:
          a						:		int											=      int(size["shortest_edge"] / crop_pct)
        elif "height" in size and "width" in size:
          if size["height"] == size["width"]:
            a						:		Dict											=      int(size["height"] / crop_pct)
          else:
            a						:		int											=      (int(size["height"] / crop_pct), int(size["width"] / crop_pct))
        else:
          raise ValueError("Invalid size for resize: {}".format(__UpperCAmelCase))

        a						:		List[Any]											=      get_resize_output_image_size(__UpperCAmelCase    ,			size=__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase)
      else:
        if "shortest_edge" in size:
          a						:		List[str]											=      get_resize_output_image_size(__UpperCAmelCase    ,			size=size["shortest_edge"]    ,			default_to_square=__UpperCAmelCase)
        elif "height" in size and "width" in size:
          a						:		Optional[int]											=      (size["height"], size["width"])
        else:
          raise ValueError("Invalid size for resize: {}".format(__UpperCAmelCase))

      return resize(__UpperCAmelCase    ,			size=__UpperCAmelCase    ,			resample=__UpperCAmelCase    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Dict[str, int]    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   List[Any]    ,			):
      a						:		List[Any]											=      get_size_dict(__UpperCAmelCase)
      if "height" not in size or "width" not in size:
        raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}''')
      return center_crop(__UpperCAmelCase    ,			size=(size["height"], size["width"])    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Union[int, float]    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   Union[str, Any]    ,			):
      return rescale(__UpperCAmelCase    ,			scale=__UpperCAmelCase    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   np.ndarray    ,			__UpperCAmelCase    :   Union[float, List[float]]    ,			__UpperCAmelCase    :   Union[float, List[float]]    ,			__UpperCAmelCase    :   Optional[Union[str, ChannelDimension]] = None    ,			**__UpperCAmelCase    :   Union[str, Any]    ,			):
      return normalize(__UpperCAmelCase    ,			mean=__UpperCAmelCase    ,			std=__UpperCAmelCase    ,			data_format=__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   ImageInput    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   int = None    ,			__UpperCAmelCase    :   PILImageResampling = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   Dict[str, int] = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   float = None    ,			__UpperCAmelCase    :   bool = None    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   Optional[Union[float, List[float]]] = None    ,			__UpperCAmelCase    :   Optional[Union[str, TensorType]] = None    ,			__UpperCAmelCase    :   ChannelDimension = ChannelDimension.FIRST    ,			**__UpperCAmelCase    :   Dict    ,			):
      a						:		Optional[Any]											=      do_resize if do_resize is not None else self.do_resize
      a						:		List[Any]											=      crop_pct if crop_pct is not None else self.crop_pct
      a						:		Optional[Any]											=      resample if resample is not None else self.resample
      a						:		str											=      do_center_crop if do_center_crop is not None else self.do_center_crop
      a						:		Any											=      do_rescale if do_rescale is not None else self.do_rescale
      a						:		List[str]											=      rescale_factor if rescale_factor is not None else self.rescale_factor
      a						:		Optional[int]											=      do_normalize if do_normalize is not None else self.do_normalize
      a						:		int											=      image_mean if image_mean is not None else self.image_mean
      a						:		int											=      image_std if image_std is not None else self.image_std

      a						:		int											=      size if size is not None else self.size
      a						:		List[str]											=      get_size_dict(__UpperCAmelCase    ,			default_to_square=__UpperCAmelCase)
      a						:		Union[str, Any]											=      crop_size if crop_size is not None else self.crop_size
      a						:		Optional[Any]											=      get_size_dict(__UpperCAmelCase    ,			param_name="crop_size")

      a						:		int											=      make_list_of_images(__UpperCAmelCase)

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

      if do_resize and size is None or resample is None:
        raise ValueError("Size and resample must be specified if do_resize is True.")

      if do_center_crop and crop_pct is None:
        raise ValueError("Crop_pct must be specified if do_center_crop is True.")

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

      if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True.")

      # All transformations expect numpy arrays.
      a						:		Any											=      [to_numpy_array(__UpperCAmelCase) for image in images]

      if do_resize:
        a						:		List[str]											=      [self.resize(image=__UpperCAmelCase    ,			size=__UpperCAmelCase    ,			crop_pct=__UpperCAmelCase    ,			resample=__UpperCAmelCase) for image in images]

      if do_center_crop:
        a						:		Optional[int]											=      [self.center_crop(image=__UpperCAmelCase    ,			size=__UpperCAmelCase) for image in images]

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

      if do_normalize:
        a						:		List[str]											=      [self.normalize(image=__UpperCAmelCase    ,			mean=__UpperCAmelCase    ,			std=__UpperCAmelCase) for image in images]

      a						:		Dict											=      [to_channel_dimension_format(__UpperCAmelCase    ,			__UpperCAmelCase) for image in images]

      a						:		List[Any]											=      {"pixel_values": images}
      return BatchFeature(data=__UpperCAmelCase    ,			tensor_type=__UpperCAmelCase)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

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






