



"""simple docstring"""







from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


A		=						logging.get_logger(__name__)

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     ["""pixel_values"""]



			def __init__(			self							:				int   ,	_lowercase							:				bool = True   ,	_lowercase							:				Dict[str, int] = None   ,	_lowercase							:				PILImageResampling = PILImageResampling.BILINEAR   ,	_lowercase							:				bool = True   ,	_lowercase							:				Union[int, float] = 1 / 255   ,	_lowercase							:				bool = True   ,	_lowercase							:				Dict[str, int] = None   ,	_lowercase							:				bool = True   ,	**_lowercase							:				Dict   ,	)							->					None:
									super().__init__(**_lowercase		)
									snake_case   :			int						    = size if size is not None else {"shortest_edge": 224}
									snake_case   :			Tuple						    = get_size_dict(_lowercase   ,	default_to_square=_lowercase		)
									snake_case   :			Tuple						    = crop_size if crop_size is not None else {"height": 256, "width": 256}
									snake_case   :			Any						    = get_size_dict(_lowercase   ,	param_name="crop_size"		)

									snake_case   :			Union[str, Any]						    = do_resize
									snake_case   :			List[Any]						    = size
									snake_case   :			List[str]						    = resample
									snake_case   :			List[str]						    = do_rescale
									snake_case   :			Tuple						    = rescale_factor
									snake_case   :			int						    = do_center_crop
									snake_case   :			Union[str, Any]						    = crop_size
									snake_case   :			Union[str, Any]						    = do_flip_channel_order



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				Dict[str, int]   ,	_lowercase							:				PILImageResampling = PIL.Image.BILINEAR   ,	_lowercase							:				Optional[Union[str, ChannelDimension]] = None   ,	**_lowercase							:				List[Any]   ,	)							->					np.ndarray:
									snake_case   :			int						    = get_size_dict(_lowercase   ,	default_to_square=_lowercase		)
									if "shortest_edge" not in size:
															raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''		)
									snake_case   :			List[Any]						    = get_resize_output_image_size(_lowercase   ,	size=size["shortest_edge"]   ,	default_to_square=_lowercase		)
									return resize(_lowercase   ,	size=_lowercase   ,	resample=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				Dict[str, int]   ,	_lowercase							:				Optional[Union[str, ChannelDimension]] = None   ,	**_lowercase							:				str   ,	)							->					np.ndarray:
									snake_case   :			str						    = get_size_dict(_lowercase		)
									if "height" not in size or "width" not in size:
															raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''		)
									return center_crop(_lowercase   ,	size=(size["height"], size["width"])   ,	data_format=_lowercase   ,	**_lowercase		)



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



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				Optional[Union[str, ChannelDimension]] = None		)							->					np.ndarray:
									return flip_channel_order(_lowercase   ,	data_format=_lowercase		)



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				ImageInput   ,	_lowercase							:				bool = None   ,	_lowercase							:				Dict[str, int] = None   ,	_lowercase							:				PILImageResampling = None   ,	_lowercase							:				bool = None   ,	_lowercase							:				float = None   ,	_lowercase							:				bool = None   ,	_lowercase							:				Dict[str, int] = None   ,	_lowercase							:				bool = None   ,	_lowercase							:				Optional[Union[str, TensorType]] = None   ,	_lowercase							:				ChannelDimension = ChannelDimension.FIRST   ,	**_lowercase							:				List[Any]   ,	)							->					PIL.Image.Image:
									snake_case   :			Tuple						    = do_resize if do_resize is not None else self.do_resize
									snake_case   :			Union[str, Any]						    = resample if resample is not None else self.resample
									snake_case   :			List[str]						    = do_rescale if do_rescale is not None else self.do_rescale
									snake_case   :			Any						    = rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case   :			List[str]						    = do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case   :			Optional[Any]						    = (
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									snake_case   :			int						    = size if size is not None else self.size
									snake_case   :			Optional[Any]						    = get_size_dict(_lowercase   ,	default_to_square=_lowercase		)
									snake_case   :			Optional[Any]						    = crop_size if crop_size is not None else self.crop_size
									snake_case   :			Dict						    = get_size_dict(_lowercase   ,	param_name="crop_size"		)

									snake_case   :			Optional[Any]						    = make_list_of_images(_lowercase		)

									if not valid_images(_lowercase		):
															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_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."		)

									if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."		)

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

									if do_resize:
															snake_case   :			Optional[Any]						    = [self.resize(image=_lowercase   ,	size=_lowercase   ,	resample=_lowercase		) for image in images]

									if do_center_crop:
															snake_case   :			Tuple						    = [self.center_crop(image=_lowercase   ,	size=_lowercase		) for image in images]

									if do_rescale:
															snake_case   :			Union[str, Any]						    = [self.rescale(image=_lowercase   ,	scale=_lowercase		) for image in images]

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
															snake_case   :			str						    = [self.flip_channel_order(image=_lowercase		) for image in images]

									snake_case   :			List[str]						    = [to_channel_dimension_format(_lowercase   ,	_lowercase		) for image in images]

									snake_case   :			int						    = {"pixel_values": images}
									return BatchFeature(data=_lowercase   ,	tensor_type=_lowercase		)



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				int   ,	_lowercase							:				List[Tuple] = None		)							->					str:
									snake_case   :			int						    = outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
															if len(_lowercase		) != len(_lowercase		):
																					raise ValueError(
																					    "Make sure that you pass in as many target sizes as the batch dimension of the logits"		)

															if is_torch_tensor(_lowercase		):
																					snake_case   :			int						    = target_sizes.numpy()

															snake_case   :			Optional[Any]						    = []

															for idx in range(len(_lowercase		)		):
																					snake_case   :			Any						    = torch.nn.functional.interpolate(
																					    logits[idx].unsqueeze(dim=0		)   ,	size=target_sizes[idx]   ,	mode="bilinear"   ,	align_corners=_lowercase		)
																					snake_case   :			Optional[Any]						    = resized_logits[0].argmax(dim=0		)
																					semantic_segmentation.append(_lowercase		)
									else:
															snake_case   :			int						    = logits.argmax(dim=1		)
															snake_case   :			str						    = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

									return semantic_segmentation








"""simple docstring"""







from ..utils import DummyObject, requires_backends

class       _a  ( metaclass=SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     ["""keras_nlp"""]



			def __init__(			self							:				int   ,	*_lowercase							:				Optional[int]   ,	**_lowercase							:				Dict		)							->					List[Any]:
									requires_backends(self   ,	["keras_nlp"]		)



