




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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Optional[int]    		=   ['pixel_values']
      def __init__(				self: List[Any]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[Dict[str, int]] = None    ,       UpperCamelCase_: PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       **UpperCamelCase_: List[str]    ,       ):
             super().__init__(**UpperCamelCase_   )
             __lowerCamelCase   =						size if size is not None else {"""height""": 2_24, """width""": 2_24}
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_   )
             __lowerCamelCase   =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       default_to_square=UpperCamelCase_    ,       param_name="""crop_size"""   )

             __lowerCamelCase   =						do_resize
             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						do_center_crop
             __lowerCamelCase   =						crop_size
             __lowerCamelCase   =						size
             __lowerCamelCase   =						resample
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
             __lowerCamelCase   =						image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Dict[str, int]    ,       UpperCamelCase_: PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Optional[int]    ,       ):
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_   )

             if "shortest_edge" in size:
                    __lowerCamelCase   =						get_resize_output_image_size(UpperCamelCase_    ,       size=size["""shortest_edge"""]    ,       default_to_square=UpperCamelCase_   )
                    # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
             elif "height" in size and "width" in size:
                    __lowerCamelCase   =						(size["""height"""], size["""width"""])
             else:
                    raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
             return resize(UpperCamelCase_    ,       size=UpperCamelCase_    ,       resample=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Dict[str, int]    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Dict    ,       ):
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_   )
             if "height" not in size or "width" not in size:
                    raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
             return center_crop(UpperCamelCase_    ,       size=(size["""height"""], size["""width"""])    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: List[Any]   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Union[float, List[float]]    ,       UpperCamelCase_: Union[float, List[float]]    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: List[Any]    ,       ):
             return normalize(UpperCamelCase_    ,       mean=UpperCamelCase_    ,       std=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Dict[str, int] = None    ,       UpperCamelCase_: PILImageResampling = None    ,       UpperCamelCase_: bool = None    ,       UpperCamelCase_: int = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Tuple    ,       ):
             __lowerCamelCase   =						do_resize if do_resize is not None else self.do_resize
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						do_normalize if do_normalize is not None else self.do_normalize
             __lowerCamelCase   =						do_center_crop if do_center_crop is not None else self.do_center_crop
             __lowerCamelCase   =						crop_size if crop_size is not None else self.crop_size
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_    ,       param_name="""crop_size"""    ,       default_to_square=UpperCamelCase_   )
             __lowerCamelCase   =						resample if resample is not None else self.resample
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						image_mean if image_mean is not None else self.image_mean
             __lowerCamelCase   =						image_std if image_std is not None else self.image_std

             __lowerCamelCase   =						size if size is not None else self.size
             __lowerCamelCase   =						get_size_dict(UpperCamelCase_   )

             if not is_batched(UpperCamelCase_   ):
                    __lowerCamelCase   =						[images]

             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:
                    raise ValueError("""Size must be specified if do_resize is True."""   )

             if do_center_crop and crop_size is None:
                    raise ValueError("""Crop size 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."""   )

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

             if do_resize:
                    __lowerCamelCase   =						[self.resize(image=UpperCamelCase_    ,       size=UpperCamelCase_    ,       resample=UpperCamelCase_   ) for image in images]

             if do_center_crop:
                    __lowerCamelCase   =						[self.center_crop(image=UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

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

             if do_normalize:
                    __lowerCamelCase   =						[self.normalize(image=UpperCamelCase_    ,       mean=UpperCamelCase_    ,       std=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_									=	{
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['VisionEncoderDecoderModel']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['TFVisionEncoderDecoderModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
       from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

