





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def        __lowerCamelCase (							__a       :Any   ,					__a       :Dict   ,					__a       :Optional[Any]   ,					__a       :Dict   ,					__a       :str   ):






		"""simple docstring"""

		A__  				=       cva.getAffineTransform(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   )
		return cva.warpAffine(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					(rows, cols)   )


if __name__ == "__main__":
					# read original image
					A   : Optional[int]			=				cva.imread(
					    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
					)
					# turn image in gray scale value
					A   : int			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
					# get image shape
					A,     A   : Tuple			=				gray_img.shape

					# set different points to rotate image
					A   : Optional[Any]			=				np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
					A   : Dict			=				np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
					A   : str			=				np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
					A   : List[Any]			=				np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)

					# add all rotated images in a list
					A   : Optional[Any]			=				[
					    gray_img,
					    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
					    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
					    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
					]

					# plot different image rotations
					A   : Any			=				plt.figure(1)
					A   : Optional[int]			=				['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
					for i, image in enumerate(images):
										plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
										plt.title(titles[i])
										plt.axis('''off''')
										plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
					plt.show()










from typing import List, Optional, Union

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


A   : Optional[Any]			=				logging.get_logger(__name__)

A   : List[str]			=				{
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''time_series_transformer'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(      self       :   Union[str, Any]							,     __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       :   Optional[Union[str, bool]] = "mean"							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   str = "gelu"							,     __lowerCAmelCase       :   int = 64							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   int = 1_00							,     __lowerCAmelCase       :   float = 0.0_2							,     __lowerCAmelCase       :   Optional[Any]=True							,     **__lowerCAmelCase       :   Union[str, Any]							,     )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       prediction_length
					A__  				=       context_length or prediction_length
					A__  				=       distribution_output
					A__  				=       loss
					A__  				=       input_size
					A__  				=       num_time_features
					A__  				=       lags_sequence
					A__  				=       scaling
					A__  				=       num_dynamic_real_features
					A__  				=       num_static_real_features
					A__  				=       num_static_categorical_features
					if cardinality 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`"""       )
							A__  				=       cardinality
					else:
							A__  				=       [0]
					if embedding_dimension 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`"""       )
							A__  				=       embedding_dimension
					else:
							A__  				=       [min(50							,     (cat + 1) // 2       ) for cat in self.cardinality]
					A__  				=       num_parallel_samples

					# Transformer architecture configuration
					A__  				=       input_size * len(__lowerCAmelCase       ) + self._number_of_features
					A__  				=       d_model
					A__  				=       encoder_attention_heads
					A__  				=       decoder_attention_heads
					A__  				=       encoder_ffn_dim
					A__  				=       decoder_ffn_dim
					A__  				=       encoder_layers
					A__  				=       decoder_layers

					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop

					A__  				=       activation_function
					A__  				=       init_std

					A__  				=       use_cache

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







			@property
			def        a_	(      self       :   List[Any]       )     ->     int:







					"""simple docstring"""






					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
					)



