



"""simple docstring"""







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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
							import torch

if is_vision_available():
							import PIL


lowerCAmelCase_   						=   logging.get_logger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Tuple[int, int]:
		def constraint_to_multiple_of(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=0						,     __lowerCamelCase=None	):
				lowercase__   :    Dict				=			round(val / multiple	) * multiple

				if max_val is not None and x > max_val:
						lowercase__   :    Optional[int]				=			math.floor(val / multiple	) * multiple

				if x < min_val:
						lowercase__   :    int				=			math.ceil(val / multiple	) * multiple

				return x

		lowercase__   :    Dict				=			(output_size, output_size) if isinstance(__lowerCamelCase						,     __lowerCamelCase	) else output_size

		lowercase__		,			lowercase__   :    str				=			get_image_size(__lowerCamelCase	)
		lowercase__		,			lowercase__   :    str				=			output_size

		# determine new height and width
		lowercase__   :    str				=			output_height / input_height
		lowercase__   :    Any				=			output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width	) < abs(1 - scale_height	):
						# fit width
						lowercase__   :    List[Any]				=			scale_width
				else:
						# fit height
						lowercase__   :    int				=			scale_height

		lowercase__   :    List[Any]				=			constraint_to_multiple_of(scale_height * input_height						,     multiple=__lowerCamelCase	)
		lowercase__   :    Dict				=			constraint_to_multiple_of(scale_width * input_width						,     multiple=__lowerCamelCase	)

		return (new_height, new_width)





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				["pixel_values"]







		def __init__(	self						:  List[str]			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = PILImageResampling.BILINEAR			,_snake_case						:  bool = False			,_snake_case						:  int = 1			,_snake_case						:  bool = True			,_snake_case						:  Union[int, float] = 1 / 255			,_snake_case						:  bool = True			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,**_snake_case						:  Tuple			,)					->		None:






				"""simple docstring"""



				super().__init__(**_snake_case						)
				lowercase__   :    List[str]				=			size if size is not None else {'''height''': 384, '''width''': 384}
				lowercase__   :    Dict				=			get_size_dict(_snake_case						)
				lowercase__   :    str				=			do_resize
				lowercase__   :    List[Any]				=			size
				lowercase__   :    Optional[Any]				=			keep_aspect_ratio
				lowercase__   :    Optional[int]				=			ensure_multiple_of
				lowercase__   :    Dict				=			resample
				lowercase__   :    Union[str, Any]				=			do_rescale
				lowercase__   :    Optional[int]				=			rescale_factor
				lowercase__   :    Any				=			do_normalize
				lowercase__   :    List[Any]				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				lowercase__   :    str				=			image_std if image_std is not None else IMAGENET_STANDARD_STD







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  bool = False			,_snake_case						:  int = 1			,_snake_case						:  PILImageResampling = PILImageResampling.BICUBIC			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Optional[int]			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    str				=			get_size_dict(_snake_case						)
				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()}"""						)
				lowercase__   :    List[str]				=			get_resize_output_image_size(
				    _snake_case			,output_size=(size['''height'''], size['''width'''])			,keep_aspect_ratio=_snake_case			,multiple=_snake_case			,)
				return resize(_snake_case			,size=_snake_case			,resample=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[int, float]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  int			,)					->		Tuple:






				"""simple docstring"""



				return rescale(_snake_case			,scale=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  List[Any]			,)					->		np.ndarray:






				"""simple docstring"""



				return normalize(_snake_case			,mean=_snake_case			,std=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  ImageInput			,_snake_case						:  bool = None			,_snake_case						:  int = None			,_snake_case						:  bool = None			,_snake_case						:  int = None			,_snake_case						:  PILImageResampling = None			,_snake_case						:  bool = None			,_snake_case						:  float = None			,_snake_case						:  bool = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[str, TensorType]] = None			,_snake_case						:  ChannelDimension = ChannelDimension.FIRST			,**_snake_case						:  int			,)					->		PIL.Image.Image:






				"""simple docstring"""



				lowercase__   :    Tuple				=			do_resize if do_resize is not None else self.do_resize
				lowercase__   :    Dict				=			size if size is not None else self.size
				lowercase__   :    Dict				=			get_size_dict(_snake_case						)
				lowercase__   :    Union[str, Any]				=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
				lowercase__   :    Optional[Any]				=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
				lowercase__   :    List[Any]				=			resample if resample is not None else self.resample
				lowercase__   :    List[str]				=			do_rescale if do_rescale is not None else self.do_rescale
				lowercase__   :    Optional[Any]				=			rescale_factor if rescale_factor is not None else self.rescale_factor
				lowercase__   :    List[str]				=			do_normalize if do_normalize is not None else self.do_normalize
				lowercase__   :    Union[str, Any]				=			image_mean if image_mean is not None else self.image_mean
				lowercase__   :    Tuple				=			image_std if image_std is not None else self.image_std

				lowercase__   :    int				=			make_list_of_images(_snake_case						)

				if not valid_images(_snake_case						):
						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 or resample is None:
						raise ValueError('''Size and resample 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_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.'''						)

				# All transformations expect numpy arrays.
				lowercase__   :    Tuple				=			[to_numpy_array(_snake_case						) for image in images]

				if do_resize:
						lowercase__   :    Dict				=			[self.resize(image=_snake_case			,size=_snake_case			,resample=_snake_case						) for image in images]

				if do_rescale:
						lowercase__   :    str				=			[self.rescale(image=_snake_case			,scale=_snake_case						) for image in images]

				if do_normalize:
						lowercase__   :    str				=			[self.normalize(image=_snake_case			,mean=_snake_case			,std=_snake_case						) for image in images]

				lowercase__   :    Union[str, Any]				=			[to_channel_dimension_format(_snake_case			,_snake_case						) for image in images]

				lowercase__   :    Any				=			{'''pixel_values''': images}
				return BatchFeature(data=_snake_case			,tensor_type=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  str			,_snake_case						:  List[Tuple] = None						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[str]				=			outputs.logits

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

						if is_torch_tensor(_snake_case						):
								lowercase__   :    Optional[int]				=			target_sizes.numpy()

						lowercase__   :    Tuple				=			[]

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

				return semantic_segmentation





"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  Dict			,*_snake_case						:  Any			,**_snake_case						:  str						)					->		None:






				"""simple docstring"""



				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''			,_snake_case			,)
				super().__init__(*_snake_case			,**_snake_case						)
