Aluode/PerceptionLabPortable
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e�Z8G dd� de�Z9G dd� de�Z:e;e<e8e=e<e6e; f f Z>dd� Z?G dd� de�Z@dd� ZAdd� ZBde6fdd �ZCd d � ZDd d � ZEd!d"� ZFd#e j5d$eGfd%d&�ZHdnd(e=d$e6e7 fd)d*�ZI 'dnde9e6e7 e7f d(e=d$e7fd+d,�ZJ 'dnde10e6e7 e7f d(e=d$e6e7 fd-d.�ZKd$e j5fd/d0�ZL dod#e j5d1e e11e=eMe=d2f f d$e8fd3d4�ZN dod#e j5d5e e12e8e<f d$e=fd6d7�ZOdod#e j5d8e e8 d$eMe=e=f fd9d:�ZPd;eMe=e=f d<e=d=e=d$eMe=e=f fd>d?�ZQd@e;e<e13e6eMf f d$eGfdAdB�ZRd@e;e<e14e6eMf f d$eGfdCdD�ZSdEee;e<e15e6eMf f d$eGfdFdG�ZTdEee;e<e16e6eMf f d$eGfdHdI�ZUdod#e17e<d18f dJe eV d$d19fdKdL�ZW dode20e6eMe<d21f dJe eV d$e22d23e6d24 e6e6d25 f fdMdN�ZX dpdOe eG dPe eV dQe eG dRe e26eVe6eV f dSe e27eVe6eV f dTe eG dUe e28e;e<e=f e=f dVe eG dWe e;e<e=f dXe eG dYe e;e<e=f dZe d[ d\e d] fd^d_�ZYG d`da� da�ZZdbe9dceMe9d2f dEe6e; d$dfddde�Z[dfe6e< dge6e< fdhdi�Z\edjdk�G dldm� dm��Z]dS )q� N)�Iterable)� dataclass)�BytesIO)�Optional�Union� ) � ExplicitEnum�
is_jax_tensor�is_numpy_array� is_tf_tensor�is_torch_available�is_torch_tensor�is_torchvision_available�is_vision_available�logging�requires_backends�to_numpy)�IMAGENET_DEFAULT_MEAN�IMAGENET_DEFAULT_STD�IMAGENET_STANDARD_MEAN�IMAGENET_STANDARD_STD�OPENAI_CLIP_MEAN�OPENAI_CLIP_STD)�InterpolationModezPIL.Image.Imagez torch.Tensorc @ � e Zd ZdZdZdS )�ChannelDimensionZchannels_firstZ
channels_lastN)�__name__�29__module__� __qualname__�FIRST�LAST� r! r! ��E:\DocsHouse\542 percep lab latest\PerceptionLab\PerceptionLab_Portable\python_embed\Lib\site-packages\transformers/image_utils.pyr Q � r c @ r )�AnnotationFormatZcoco_detectionZ
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COCO_PANOPTICr! r! r! r" r$ V r# r$ c @ s e Zd ZejjZejjZdS )�AnnotionFormatN)r r r r$ r% �valuer& r! r! r! r" r' [ s r' c C s t � o t| tjj�S �N)r �30isinstance�PIL�Image��imgr! r! r" � is_pil_imagec s r/ c @ s e Zd ZdZdZdZdZdZdS )� ImageTypeZpillow�torch�numpyZ31tensorflowZjaxN)r r r r+ �TORCH�NUMPY�32TENSORFLOW�JAXr! r! r! r" r0 g s r0 c C sX t | �rtjS t| �rtjS t| �rtjS t| �r tjS t | �r#tj33S t dt | �� ���)NzUnrecognized image type )
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r3 r34 r4 r r5 r r6 �35ValueError�type��imager! r! r" �get_image_typeo s r; c C s( t | �pt| �pt| �pt| �pt| �S r) )r/ r36 r
r r r- r! r! r" �is_valid_image} s (r< �imagesc C s | o37t dd� | D ��S )Nc s � � | ]}t |�V qd S r) )r< ��.0r: r! r! r" � <genexpr>� � � z*is_valid_list_of_images.<locals>.<genexpr>��all)r= r! r! r" �is_valid_list_of_images� s rE c C s\ t | d t�rdd� | D �S t | d tj�r tj| dd�S t | d tj�r,tj| dd�S d S )Nr c S � g | ] }|D ]}|�qqS r! r! )r@ Zsublist�itemr! r! r" �38<listcomp>� � z$concatenate_list.<locals>.<listcomp>�Zaxis)�dim)r* �list�np�ndarrayZ concatenater1 �Tensor�cat)Z39input_listr! r! r" �concatenate_list� s �rQ c C s: t | ttf�r| D ] }t|�s dS q dS t| �sdS dS )NFT)r* rL �tuple� valid_imagesr< )Zimgsr. r! r! r" rS � s ��rS c C s t | ttf�r
t| d �S dS )Nr F)r* rL rR r< r- r! r! r" �40is_batched� s rT r: �returnc C s, | j tjkrdS t�| �dkot�| �dkS )zV41 Checks to see whether the pixel values have already been rescaled to [0, 1].42 Fr r )�dtyperM �uint8�min�maxr9 r! r! r" �is_scaled_image� s rZ � �expected_ndimsc C s� t | �r| S t| �r
| gS t| �r9| j|d kr t| �} | S | j|kr(| g} | S td|d � d|� d| j� d���tdt| �� d���)a 43 Ensure that the output is a list of images. If the input is a single image, it is converted to a list of length 1.44 If the input is a batch of images, it is converted to a list of images.45 46 Args:47 images (`ImageInput`):48 Image of images to turn into a list of images.49 expected_ndims (`int`, *optional*, defaults to 3):50 Expected number of dimensions for a single input image. If the input image has a different number of51 dimensions, an error is raised.52 r z%Invalid image shape. Expected either z or z dimensions, but got z dimensions.ztInvalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray, but got �.)rT r/ r< �ndimrL r7 r8 �r= r\ r! r! r" �make_list_of_images� s* 53������r` c C s� t | ttf�r tdd� | D ��r tdd� | D ��r dd� | D �S t | ttf�rJt| �rJt| d �s8| d j|kr:| S | d j|d krJdd� | D �S t| �ret| �sW| j|krZ| gS | j|d kret| �S td | � ���)54a� 55 Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1.56 If the input is a nested list of images, it is converted to a flat list of images.57 Args:58 images (`Union[list[ImageInput], ImageInput]`):59 The input image.60 expected_ndims (`int`, *optional*, defaults to 3):61 The expected number of dimensions for a single input image.62 Returns:63 list: A list of images or a 4d array of images.64 c s � � | ]65}t |ttf�V qd S r) �r* rL rR �r@ Zimages_ir! r! r" rA � � � z+make_flat_list_of_images.<locals>.<genexpr>c s � � | ]66}t |�p67| V qd S r) �rE rc r! r! r" rA � rd c S rF r! r! �r@ Zimg_listr. r! r! r" rH � rI z,make_flat_list_of_images.<locals>.<listcomp>r r c S rF r! r! rg r! r! r" rH � rI z*Could not make a flat list of images from � r* rL rR rD rE r/ r^ r< r7 r_ r! r! r" �make_flat_list_of_images� s$ ���ri c C s� t | ttf�rtdd� | D ��rtdd� | D ��r| S t | ttf�rFt| �rFt| d �s3| d j|kr6| gS | d j|d krFdd� | D �S t| �rct| �sS| j|krW| ggS | j|d krct| �gS td��) as 68 Ensure that the output is a nested list of images.69 Args:70 images (`Union[list[ImageInput], ImageInput]`):71 The input image.72 expected_ndims (`int`, *optional*, defaults to 3):73 The expected number of dimensions for a single input image.74 Returns:75 list: A list of list of images or a list of 4d array of images.76 c s ra r) rb rc r! r! r" rA rd z-make_nested_list_of_images.<locals>.<genexpr>c s re r) rf rc r! r! r" rA 77 rd r r c S s g | ]}t |��qS r! )rL r? r! r! r" rH s z.make_nested_list_of_images.<locals>.<listcomp>z]Invalid input type. Must be a single image, a list of images, or a list of batches of images.rh r_ r! r! r" �make_nested_list_of_images� s$ ���78rj c C s@ t | �s
tdt| �� ���t� r t| tjj�r t�| �S t | �S )NzInvalid image type: )79r< r7 r8 r r* r+ r, rM �arrayr r- r! r! r" �to_numpy_array s80 81rl � num_channels.c C s� |dur|nd}t |t�r|fn|}| jdkr d\}}n | jdkr&d\}}n| jdkr0d\}}ntd| j� ���| j| |v rS| j| |v rSt�d | j� d82�� tjS | j| |v r]tjS | j| |v rgtj S td ��) a[ 83 Infers the channel dimension format of `image`.84 85 Args:86 image (`np.ndarray`):87 The image to infer the channel dimension of.88 num_channels (`int` or `tuple[int, ...]`, *optional*, defaults to `(1, 3)`):89 The number of channels of the image.90 91 Returns:92 The channel dimension of the image.93 N�r r[ r[ )r � � � )ro rp z(Unsupported number of image dimensions: z4The channel dimension is ambiguous. Got image shape z�. Assuming channels are the first dimension. Use the [input_data_format](https://huggingface.co/docs/transformers/main/internal/image_processing_utils#transformers.image_transforms.rescale.input_data_format) parameter to assign the channel dimension.z(Unable to infer channel dimension format)94r* �intr^ r7 �shape�logger�warningr r r )r: rm Z first_dimZlast_dimr! r! r" � infer_channel_dimension_format( s& 9596979899100 �rv �input_data_formatc C sF |du rt | �}|tjkr| jd S |tjkr | jd S td|� ���)a� 101 Returns the channel dimension axis of the image.102 103 Args:104 image (`np.ndarray`):105 The image to get the channel dimension axis of.106 input_data_format (`ChannelDimension` or `str`, *optional*):107 The channel dimension format of the image. If `None`, will infer the channel dimension from the image.108 109 Returns:110 The channel dimension axis of the image.111 Nr[ r �Unsupported data format: )rv r r r^ r r7 )r: rw r! r! r" �get_channel_dimension_axisO s 112113114115ry � channel_dimc C sZ |du rt | �}|tjkr| jd | jd fS |tjkr&| jd | jd fS td|� ���)a� 116 Returns the (height, width) dimensions of the image.117 118 Args:119 image (`np.ndarray`):120 The image to get the dimensions of.121 channel_dim (`ChannelDimension`, *optional*):122 Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image.123 124 Returns:125 A tuple of the image's height and width.126 N���������������rx )rv r r rs r r7 )r: rz r! r! r" �get_image_sizeg s
127128r~ �129image_size�130max_height� max_widthc 131 C sB | \}}|| }|| }t ||�}t|| �}t|| �} || fS )a� 132 Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio.133 Important, even if image_height < max_height and image_width < max_width, the image will be resized134 to at least one of the edges be equal to max_height or max_width.135 136 For example:137 - input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50)138 - input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400)139 140 Args:141 image_size (`tuple[int, int]`):142 The image to resize.143 max_height (`int`):144 The maximum allowed height.145 max_width (`int`):146 The maximum allowed width.147 )rX rr )148r r� r� �height�widthZ height_scaleZ width_scaleZ min_scaleZ149new_heightZ new_widthr! r! r" �#get_image_size_for_max_height_width s 150 r� �151annotationc C sV t | t�r)d| v r)d| v r)t | d ttf�r)t| d �dks't | d d t�r)dS dS )N�image_id� annotationsr TF�r* �dictrL rR �len�r� r! r! r" �"is_valid_annotation_coco_detection� s ��"r� c C s^ t | t�r-d| v r-d| v r-d| v r-t | d ttf�r-t| d �dks+t | d d t�r-dS dS )Nr� Z
segments_info� file_namer TFr� r� r! r! r" �!is_valid_annotation_coco_panoptic� s ��"r� r� c C � t dd� | D ��S )Nc s r> r) )r� �r@ �annr! r! r" rA � rB z3valid_coco_detection_annotations.<locals>.<genexpr>rC �r� r! r! r" � valid_coco_detection_annotations� � r� c C r� )Nc s r> r) )r� r� r! r! r" rA � rB z2valid_coco_panoptic_annotations.<locals>.<genexpr>rC r� r! r! r" � valid_coco_panoptic_annotations� r� r� �timeoutc 152 C s� t tdg� t| t�re| �d�s| �d�r$tj�tt j153| |d�j ��} nLt j
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a3 156 Loads `image` to a PIL Image.157 158 Args:159 image (`str` or `PIL.Image.Image`):160 The image to convert to the PIL Image format.161 timeout (`float`, *optional*):162 The timeout value in seconds for the URL request.163 164 Returns:165 `PIL.Image.Image`: A PIL Image.166 Zvisionzhttp://zhttps://�r� z data:image/�,r z�Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got z. Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.�RGB)r �167load_imager* �str�168startswithr+ r, �openr �requests�get�content�os�path�isfile�split�base64� decodebytes�encode� Exceptionr7 � TypeErrorZImageOpsZexif_transpose�convert)r: r� Zb64�er! r! r" r� � s0
169 170���� 171r� c sX t | ttf�r&t| �r t | d ttf�r � fdd�| D �S � fdd�| D �S t| � d�S )a Loads images, handling different levels of nesting.172 173 Args:174 images: A single image, a list of images, or a list of lists of images to load.175 timeout: Timeout for loading images.176 177 Returns:178 A single image, a list of images, a list of lists of images.179 r c s g | ] }� fd d�|D ��qS )c � g | ]}t |� d ��qS �r� �r� r? r� r! r" rH � � z*load_images.<locals>.<listcomp>.<listcomp>r! )r@ Z image_groupr� r! r" rH � s z load_images.<locals>.<listcomp>c r� r� r� r? r� r! r" rH � r� r� )r* rL rR r� r� )r= r� r! r� r" � load_images� s180 r� �181do_rescale�rescale_factor� do_normalize�182image_mean� image_std�do_pad�pad_size�do_center_crop� crop_size� do_resize�size�resample�PILImageResampling�
interpolationr c
C s� | r183|du r184t d��|r|du rt d��|r"|du s |du r"t d��|r,|du r,t d��| dur8| dur8t d��| rJ|185durF| dusL| dusNt d��dS dS dS )a� 186 Checks validity of typically used arguments in an `ImageProcessor` `preprocess` method.187 Raises `ValueError` if arguments incompatibility is caught.188 Many incompatibilities are model-specific. `do_pad` sometimes needs `size_divisor`,189 sometimes `size_divisibility`, and sometimes `size`. New models and processors added should follow190 existing arguments when possible.191 192 Nz=`rescale_factor` must be specified if `do_rescale` is `True`.zgDepending on the model, `size_divisor` or `pad_size` or `size` must be specified if `do_pad` is `True`.zP`image_mean` and `image_std` must both be specified if `do_normalize` is `True`.z<`crop_size` must be specified if `do_center_crop` is `True`.zbOnly one of `interpolation` and `resample` should be specified, depending on image processor type.zO`size` and `resample/interpolation` must be specified if `do_resize` is `True`.)r7 )
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dd� Zd!dd�Zd"dd�Zdd� Zdd� Zd#d d �ZdS )$�ImageFeatureExtractionMixinzD195 Mixin that contain utilities for preparing image features.196 c C s8 t |tjjtjf�st|�stdt|�� d���d S d S )Nz Got type zU which is not supported, only `PIL.Image.Image`, `np.ndarray` and `torch.Tensor` are.)r* r+ r, rM rN r
r7 r8 ��selfr: r! r! r" �_ensure_format_supported> s197 ��z4ImageFeatureExtractionMixin._ensure_format_supportedNc C s� | � |� t|�r
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j�|�S |S )a" 199 Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if200 needed.201 202 Args:203 image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`):204 The image to convert to the PIL Image format.205 rescale (`bool`, *optional*):206 Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will207 default to `True` if the image type is a floating type, `False` otherwise.208 Nr r[ rn r ro �� )r� r
r2 r* rM rN �flatZfloatingr^ rs � transpose�astyperW r+ r, Z fromarray)r� r: �rescaler! r! r" � to_pil_imageE s 209 z(ImageFeatureExtractionMixin.to_pil_imagec C s& | � |� t|tjj�s|S |�d�S )z�210 Converts `PIL.Image.Image` to RGB format.211 212 Args:213 image (`PIL.Image.Image`):214 The image to convert.215 r� )r� r* r+ r, r� r� r! r! r" � convert_rgbc s 216217z'ImageFeatureExtractionMixin.convert_rgbr: �scalerU c C s | � |� || S )z7218 Rescale a numpy image by scale amount219 )r� )r� r: r� r! r! r" r� q s 220z#ImageFeatureExtractionMixin.rescaleTc C s� | � |� t|tjj�rt�|�}t|�r|�� }|du r&t|jd tj �n|}|r4| �221|� tj �d�}|rB|j
dkrB|�ddd�}|S )a� 222 Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first223 dimension.224 225 Args:226 image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):227 The image to convert to a NumPy array.228 rescale (`bool`, *optional*):229 Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will230 default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise.231 channel_first (`bool`, *optional*, defaults to `True`):232 Whether or not to permute the dimensions of the image to put the channel dimension first.233 Nr �p?r[ ro r )r� r* r+ r, rM rk r
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channel_firstr! r! r" rl x s 234235 z*ImageFeatureExtractionMixin.to_numpy_arrayc C sD | � |� t|tjj�r|S t|�r|�d�}|S tj|dd�}|S )z�236 Expands 2-dimensional `image` to 3 dimensions.237 238 Args:239 image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):240 The image to expand.241 r rJ )r� r* r+ r, r
Z unsqueezerM � expand_dimsr� r! r! r" r� � s 242243�z'ImageFeatureExtractionMixin.expand_dimsFc C sh | � |� t|tjj�r| j|dd�}n |r3t|tj�r'| �|�tj �d�}n t244|�r3| �|� � d�}t|tj�rXt|tj�sHt� |��|j
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246 Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array247 if it's a PIL Image.248 249 Args:250 image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):251 The image to normalize.252 mean (`list[float]` or `np.ndarray` or `torch.Tensor`):253 The mean (per channel) to use for normalization.254 std (`list[float]` or `np.ndarray` or `torch.Tensor`):255 The standard deviation (per channel) to use for normalization.256 rescale (`bool`, *optional*, defaults to `False`):257 Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will258 happen automatically.259 T)r� r� r Nr[ rn )r� r* r+ r, rl rM rN r� r� r� r
�floatrk rV r1 rO Z260from_numpyZtensorr^ rs )r� r: �meanZstdr� r1 r! r! r" � normalize� s6 261 � 262 263( z%ImageFeatureExtractionMixin.normalizec
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||d�S )a� 270 Resizes `image`. Enforces conversion of input to PIL.Image.271 272 Args:273 image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):274 The image to resize.275 size (`int` or `tuple[int, int]`):276 The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be277 matched to this.278 279 If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If280 `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to281 this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).282 resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):283 The filter to user for resampling.284 default_to_square (`bool`, *optional*, defaults to `True`):285 How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a286 square (`size`,`size`). If set to `False`, will replicate287 [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize)288 with support for resizing only the smallest edge and providing an optional `max_size`.289 max_size (`int`, *optional*, defaults to `None`):290 The maximum allowed for the longer edge of the resized image: if the longer edge of the image is291 greater than `max_size` after being resized according to `size`, then the image is resized again so292 that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller293 edge may be shorter than `size`. Only used if `default_to_square` is `False`.294 295 Returns:296 image: A resized `PIL.Image.Image`.297 Nr r z max_size = zN must be strictly greater than the requested size for the smaller edge size = )r� )r� �BILINEARr� r* r+ r, r� rL rR rr r� r� r7 �resize)
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rM rN r^ r� rs r� r+ r, �cropr� ZpermuterY Z326zeros_likeZ new_zerosrX )r� r: r� Z image_shape�top�bottom�left�rightr� Z new_shapeZ new_imageZtop_padZ327bottom_padZleft_padZ right_padr! r! r" � center_crop# sP 328329330331, (2 332 4�z'ImageFeatureExtractionMixin.center_cropc C s> | � |� t|tjj�r| �|�}|ddd�dd�dd�f S )a� 333 Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of334 `image` to a NumPy array if it's a PIL Image.335 336 Args:337 image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):338 The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should339 be first.340 Nr| )r� r* r+ r, rl r� r! r! r" �flip_channel_ordern s 341 342343 z.ImageFeatureExtractionMixin.flip_channel_orderr c C sL |dur|nt jj}| �|� t|t jj�s| �|�}|j||||||d�S )a� 344 Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees345 counter clockwise around its centre.346 347 Args:348 image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):349 The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before350 rotating.351 352 Returns:353 image: A rotated `PIL.Image.Image`.354 N)r� �expand�center� translate� fillcolor)r+ r, �NEARESTr� r* r� �rotate)r� r: Zangler� r� r� r� r� r! r! r" r� s
355356 �z"ImageFeatureExtractionMixin.rotater) )NT)F)NTN)Nr NNN)r r r �__doc__r� r� r� rM rN r r� rr r� rl r� r� r� r� r� r� r! r! r! r" r� 9 s 357 "358 3593604CKr� �annotation_format� supported_annotation_formatsc C sX | |vrt dt� d|� ���| tju rt|�st d��| tju r(t|�s*t d��d S d S )Nz Unsupported annotation format: z must be one of z�Invalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.z�Invalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.)r7 �formatr$ r% r� r& r� )r� r� r� r! r! r" �validate_annotations� s 361�362��r� �valid_processor_keys�captured_kwargsc C s: t |��t | ��}|rd�|�}t�d|� d�� d S d S )Nz, z Unused or unrecognized kwargs: r] )�set�363difference�joinrt ru )r� r� Z unused_keysZunused_key_strr! r! r" �validate_kwargs� s364 365�r� T)�frozenc @ sz e Zd ZU dZdZee ed< dZee ed< dZ ee ed< dZ366ee ed< dZ ee ed< dZ ee ed< d d367� Z
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