





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


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

lowercase      :       Tuple									=  {
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  '''biogpt'''

   def __init__(					self					,  lowercase=4_2384					,  lowercase=1024					,  lowercase=24					,  lowercase=16					,  lowercase=4096					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=1024					,  lowercase=0.02					,  lowercase=1e-12					,  lowercase=True					,  lowercase=True					,  lowercase=0.0					,  lowercase=0.0					,  lowercase=1					,  lowercase=0					,  lowercase=2					,  **lowercase					,  )			->						Optional[Any]:







          '''simple docstring'''

          a__						:	List[Any] =				vocab_size
          a__						:	Tuple =				max_position_embeddings
          a__						:	Union[str, Any] =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Union[str, Any] =				num_attention_heads
          a__						:	str =				intermediate_size
          a__						:	int =				hidden_act
          a__						:	Dict =				hidden_dropout_prob
          a__						:	Dict =				attention_probs_dropout_prob
          a__						:	Any =				initializer_range
          a__						:	str =				layer_norm_eps
          a__						:	Tuple =				scale_embedding
          a__						:	str =				use_cache
          a__						:	Optional[int] =				layerdrop
          a__						:	int =				activation_dropout
          super().__init__(pad_token_id=lowercase					,  bos_token_id=lowercase					,  eos_token_id=lowercase					,  **lowercase)







'''simple docstring'''

from pathlib import Path

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


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

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    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
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''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()




