





'''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


snake_case_			:     int  	=  logging.get_logger(__name__)


if is_vision_available():
					import PIL






class 							lowercase__     (							lowerCamelCase__       ):
	lowercase__      =      ['pixel_values']





	def __init__(			self :    int			,lowerCamelCase__ :    int = True			,lowerCamelCase__ :    str = None			,lowerCamelCase__ :    Dict = PILImageResampling.BICUBIC			,lowerCamelCase__ :    List[Any] = True			,lowerCamelCase__ :    List[str] = None			,lowerCamelCase__ :    List[Any] = True			,lowerCamelCase__ :    Tuple = 1 / 255			,lowerCamelCase__ :    Optional[int] = True			,lowerCamelCase__ :    Union[str, Any] = None			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Optional[Any] = True			,**lowerCamelCase__ :    str			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       size if size is not None else {'shortest_edge': 224}
							_UpperCamelCase :		Tuple			    =       get_size_dict(lowerCamelCase__			,default_to_square=lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase :		Tuple			    =       get_size_dict(lowerCamelCase__			,default_to_square=lowerCamelCase__			,param_name='crop_size'			)

							_UpperCamelCase :		Optional[Any]			    =       do_resize
							_UpperCamelCase :		int			    =       size
							_UpperCamelCase :		List[str]			    =       resample
							_UpperCamelCase :		Optional[Any]			    =       do_center_crop
							_UpperCamelCase :		Dict			    =       crop_size
							_UpperCamelCase :		Any			    =       do_rescale
							_UpperCamelCase :		int			    =       rescale_factor
							_UpperCamelCase :		str			    =       do_normalize
							_UpperCamelCase :		List[str]			    =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							_UpperCamelCase :		List[str]			    =       image_std if image_std is not None else OPENAI_CLIP_STD
							_UpperCamelCase :		Dict			    =       do_convert_rgb





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    List[str] = PILImageResampling.BICUBIC			,lowerCamelCase__ :    Tuple = None			,**lowerCamelCase__ :    Tuple			,):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       get_size_dict(lowerCamelCase__			,default_to_square=lowerCamelCase__			)
							if "shortest_edge" not in size:
													raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'			)
							_UpperCamelCase :		List[Any]			    =       get_resize_output_image_size(lowerCamelCase__			,size=size['shortest_edge']			,default_to_square=lowerCamelCase__			)
							return resize(lowerCamelCase__			,size=lowerCamelCase__			,resample=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    Union[str, Any] = None			,**lowerCamelCase__ :    Union[str, Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       get_size_dict(lowerCamelCase__			)
							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(lowerCamelCase__			,size=(size['height'], size['width'])			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    List[str] = None			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							return rescale(lowerCamelCase__			,scale=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str] = None			,**lowerCamelCase__ :    str			,):







							'''simple docstring'''
							return normalize(lowerCamelCase__			,mean=lowerCamelCase__			,std=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    int			,lowerCamelCase__ :    Dict = None			,lowerCamelCase__ :    Dict = None			,lowerCamelCase__ :    Optional[Any] = None			,lowerCamelCase__ :    Dict = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Union[str, Any] = None			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    int = None			,lowerCamelCase__ :    List[Any] = None			,lowerCamelCase__ :    List[Any] = None			,lowerCamelCase__ :    Tuple = ChannelDimension.FIRST			,**lowerCamelCase__ :    Dict			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase :		int			    =       size if size is not None else self.size
							_UpperCamelCase :		Optional[int]			    =       get_size_dict(lowerCamelCase__			,param_name='size'			,default_to_square=lowerCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       resample if resample is not None else self.resample
							_UpperCamelCase :		int			    =       do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase :		Optional[Any]			    =       crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase :		Tuple			    =       get_size_dict(lowerCamelCase__			,param_name='crop_size'			,default_to_square=lowerCamelCase__			)
							_UpperCamelCase :		int			    =       do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase :		Union[str, Any]			    =       rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase :		Optional[int]			    =       do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase :		str			    =       image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase :		Optional[Any]			    =       image_std if image_std is not None else self.image_std
							_UpperCamelCase :		Optional[int]			    =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							_UpperCamelCase :		Union[str, Any]			    =       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_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 :		List[str]			    =       [convert_to_rgb(lowerCamelCase__			) for image in images]

							# All transformations expect numpy arrays.
							_UpperCamelCase :		List[Any]			    =       [to_numpy_array(lowerCamelCase__			) for image in images]

							if do_resize:
													_UpperCamelCase :		List[Any]			    =       [self.resize(image=lowerCamelCase__			,size=lowerCamelCase__			,resample=lowerCamelCase__			) for image in images]

							if do_center_crop:
													_UpperCamelCase :		Dict			    =       [self.center_crop(image=lowerCamelCase__			,size=lowerCamelCase__			) for image in images]

							if do_rescale:
													_UpperCamelCase :		Optional[int]			    =       [self.rescale(image=lowerCamelCase__			,scale=lowerCamelCase__			) for image in images]

							if do_normalize:
													_UpperCamelCase :		Optional[Any]			    =       [self.normalize(image=lowerCamelCase__			,mean=lowerCamelCase__			,std=lowerCamelCase__			) for image in images]

							_UpperCamelCase :		Any			    =       [to_channel_dimension_format(lowerCamelCase__			,lowerCamelCase__			) for image in images]

							_UpperCamelCase :		str			    =       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__			,tensor_type=lowerCamelCase__			)







'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_			:     Optional[int]  	=  {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  ['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

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


