


'''simple docstring'''





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase												=						imread(R'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase												=						cvtColor(img, COLOR_BGR2GRAY)


def        a__     (					):
   UpperCAmelCase__	:List[Any]      =      cn.convert_to_negative(UpperCamelCase_						)
   # assert negative_img array for at least one True
   assert negative_img.any()


def        a__     (					):
   with Image.open('''digital_image_processing/image_data/lena_small.jpg'''						) as img:
      # Work around assertion for response
      assert str(cc.change_contrast(UpperCamelCase_,   110						)						).startswith(
          '''<PIL.Image.Image image mode=RGB size=100x100 at'''						)


def        a__     (					):
   UpperCAmelCase__	:List[Any]      =      canny.gen_gaussian_kernel(9,   sigma=1.4						)
   # Assert ambiguous array
   assert resp.all()


def        a__     (					):
   UpperCAmelCase__	:List[str]      =      imread('''digital_image_processing/image_data/lena_small.jpg''',   0						)
   # assert ambiguous array for all == True
   assert canny_img.all()
   UpperCAmelCase__	:int      =      canny.canny(UpperCamelCase_						)
   # assert canny array for at least one True
   assert canny_array.any()


def        a__     (					):
   assert gg.gaussian_filter(UpperCamelCase_,   5,   sigma=0.9						).all()


def        a__     (					):
   # laplace diagonals
   UpperCAmelCase__	:Union[str, Any]      =      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]						)
   UpperCAmelCase__	:Optional[Any]      =      conv.img_convolve(UpperCamelCase_,   UpperCamelCase_						).astype(UpperCamelCase_						)
   assert res.any()


def        a__     (					):
   assert med.median_filter(UpperCamelCase_,   3						).any()


def        a__     (					):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      sob.sobel_filter(UpperCamelCase_						)
   assert grad.any() and theta.any()


def        a__     (					):
   UpperCAmelCase__	:Dict      =      sp.make_sepia(UpperCamelCase_,   20						)
   assert sepia.all()


def        a__     (					UpperCamelCase_						:      str = "digital_image_processing/image_data/lena_small.jpg"						):
   UpperCAmelCase__	:Union[str, Any]      =      bs.Burkes(imread(UpperCamelCase_,   1						),   120						)
   burkes.process()
   assert burkes.output_img.any()


def        a__     (					UpperCamelCase_						:      str = "digital_image_processing/image_data/lena_small.jpg",   ):
   UpperCAmelCase__	:Tuple      =      rs.NearestNeighbour(imread(UpperCamelCase_,   1						),   400,   200						)
   nn.process()
   assert nn.output.any()


def        a__     (					):
   UpperCAmelCase__	:List[str]      =      '''digital_image_processing/image_data/lena.jpg'''

   # Reading the image and converting it to grayscale.
   UpperCAmelCase__	:int      =      imread(UpperCamelCase_,   0						)

   # Test for get_neighbors_pixel function() return not None
   UpperCAmelCase__	:Optional[Any]      =      0
   UpperCAmelCase__	:Dict      =      0
   UpperCAmelCase__	:Optional[int]      =      image[x_coordinate][y_coordinate]

   UpperCAmelCase__	:Optional[int]      =      lbp.get_neighbors_pixel(
       UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)

   assert neighbors_pixels is not None

   # Test for local_binary_pattern function()
   # Create a numpy array as the same height and width of read image
   UpperCAmelCase__	:Optional[int]      =      np.zeros((image.shape[0], image.shape[1])						)

   # Iterating through the image and calculating the local binary pattern value
   # for each pixel.
   for i in range(0,   image.shape[0]						):
      for j in range(0,   image.shape[1]						):
         UpperCAmelCase__	:Dict      =      lbp.local_binary_value(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)

   assert lbp_image.any()







'''simple docstring'''





from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

    # Transformer architecture configuration
    UpperCAmelCase__	:Optional[Any]      =      input_size * len(self.lags_sequence ) + self._number_of_features
    UpperCAmelCase__	:Optional[Any]      =      d_model
    UpperCAmelCase__	:Any      =      encoder_attention_heads
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:int      =      encoder_ffn_dim
    UpperCAmelCase__	:Union[str, Any]      =      decoder_ffn_dim
    UpperCAmelCase__	:Optional[Any]      =      encoder_layers
    UpperCAmelCase__	:str      =      decoder_layers

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

    super().__init__(is_encoder_decoder=__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return (
        sum(self.embedding_dimension )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )



