'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_:					Any  =logging.get_logger(__name__)


if is_vision_available():
    import PIL




class 	__A				(			UpperCamelCase__     ):
    a__	:						List[str]	       =  ["""pixel_values"""]



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

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else OPENAI_CLIP_STD
         UpperCAmelCase_	   =       do_convert_rgb



    def  _lowercase						(self   :   Dict       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Tuple       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a			)
         if "shortest_edge" not in size:
              raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""			)
         UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   Any       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)
         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(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



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



    def  _lowercase						(self   :   int       ,      __a   :   np.ndarray       ,      __a   :   Union[float, List[float]]       ,      __a   :   Union[float, List[float]]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Tuple       ,      ):
         return normalize(__a       ,      mean=__a       ,      std=__a       ,      data_format=__a       ,      **__a			)






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

         UpperCAmelCase_	   =       make_list_of_images(__a			)

         if not valid_images(__a			):
              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."			)

         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."			)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              UpperCAmelCase_	   =       [convert_to_rgb(__a			) for image in images]

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

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

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

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

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

         UpperCAmelCase_	   =       [to_channel_dimension_format(__a       ,      __a			) for image in images]

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




