from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_ :				Any						 =    logging.get_logger(__name__)

A_ :				Union[str, Any]						 =    {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Tuple													= '''roberta-prelayernorm'''






					def __init__(				self					, A__=5_0265					, A__=768					, A__=12					, A__=12					, A__=3072					, A__="gelu"					, A__=0.1					, A__=0.1					, A__=512					, A__=2					, A__=0.0_2					, A__=1e-12					, A__=1					, A__=0					, A__=2					, A__="absolute"					, A__=True					, A__=None					, **A__					, ):
						super().__init__(pad_token_id=__lowerCAmelCase					, bos_token_id=__lowerCAmelCase					, eos_token_id=__lowerCAmelCase					, **__lowerCAmelCase    )

						A__							:					int       =	vocab_size
						A__							:					Any       =	hidden_size
						A__							:					int       =	num_hidden_layers
						A__							:					Dict       =	num_attention_heads
						A__							:					Optional[Any]       =	hidden_act
						A__							:					List[str]       =	intermediate_size
						A__							:					Tuple       =	hidden_dropout_prob
						A__							:					List[str]       =	attention_probs_dropout_prob
						A__							:					Union[str, Any]       =	max_position_embeddings
						A__							:					Optional[int]       =	type_vocab_size
						A__							:					Optional[Any]       =	initializer_range
						A__							:					Optional[Any]       =	layer_norm_eps
						A__							:					Optional[int]       =	position_embedding_type
						A__							:					Any       =	use_cache
						A__							:					int       =	classifier_dropout



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					@property
					def 						__A    (				self    ):







						if self.task == "multiple-choice":
							A__							:					List[str]       =	{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
							A__							:					str       =	{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ]    )






from pathlib import Path

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

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


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

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A_ :				Dict						 =    [
	    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_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['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()



