
"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_    							=					logging.get_logger(__name__)




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Tuple          =    ['pixel_values']




				def __init__(				self							,						UpperCAmelCase = True							,						UpperCAmelCase = 32							,						UpperCAmelCase=PILImageResampling.BILINEAR							,						UpperCAmelCase = True							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  do_resize
							lowerCamelCase_					=  do_rescale
							lowerCamelCase_					=  size_divisor
							lowerCamelCase_					=  resample
							super().__init__(**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase   ):
							lowerCamelCase_		, lowerCamelCase_					=  get_image_size(UpperCAmelCase   )
							# Rounds the height and width down to the closest multiple of size_divisor
							lowerCamelCase_					=  height // size_divisor * size_divisor
							lowerCamelCase_					=  width // size_divisor * size_divisor
							lowerCamelCase_					=  resize(UpperCAmelCase							,						(new_h, new_w)							,						resample=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )
							return image




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase   ):
							return rescale(image=UpperCAmelCase							,						scale=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase=None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = ChannelDimension.FIRST							,						**UpperCAmelCase							,						):
							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_					=  size_divisor if size_divisor is not None else self.size_divisor
							lowerCamelCase_					=  resample if resample is not None else self.resample

							if do_resize and size_divisor is None:
										raise ValueError('''size_divisor is required for resizing'''   )

							lowerCamelCase_					=  make_list_of_images(UpperCAmelCase   )

							if not valid_images(UpperCAmelCase   ):
										raise ValueError('''Invalid image(s)'''   )

							# All transformations expect numpy arrays.
							lowerCamelCase_					=  [to_numpy_array(UpperCAmelCase   ) for img in images]

							if do_resize:
										lowerCamelCase_					=  [self.resize(UpperCAmelCase							,						size_divisor=UpperCAmelCase							,						resample=UpperCAmelCase   ) for image in images]

							if do_rescale:
										lowerCamelCase_					=  [self.rescale(UpperCAmelCase							,						scale=1 / 255   ) 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   )







'''simple docstring'''





from typing import TYPE_CHECKING

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


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

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''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

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



