

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  """ibert"""

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    Dict=3_0_5_2_2					,  UpperCamelCase__							:    Any=7_6_8					,  UpperCamelCase__							:    Optional[int]=1_2					,  UpperCamelCase__							:    Union[str, Any]=1_2					,  UpperCamelCase__							:    Optional[Any]=3_0_7_2					,  UpperCamelCase__							:    int="gelu"					,  UpperCamelCase__							:    int=0.1					,  UpperCamelCase__							:    Tuple=0.1					,  UpperCamelCase__							:    Any=5_1_2					,  UpperCamelCase__							:    Optional[int]=2					,  UpperCamelCase__							:    List[str]=0.02					,  UpperCamelCase__							:    Union[str, Any]=1e-12					,  UpperCamelCase__							:    Tuple=1					,  UpperCamelCase__							:    Dict=0					,  UpperCamelCase__							:    Optional[int]=2					,  UpperCamelCase__							:    Optional[int]="absolute"					,  UpperCamelCase__							:    Any=False					,  UpperCamelCase__							:    List[str]="none"					,  **UpperCamelCase__							:    List[Any]					,  )->       Union[str, Any]:



						'''simple docstring'''


						super().__init__(pad_token_id=UpperCamelCase__					,  bos_token_id=UpperCamelCase__					,  eos_token_id=UpperCamelCase__					,  **UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      vocab_size
						__lowerCAmelCase:	Union[str, Any]   				=      hidden_size
						__lowerCAmelCase:	List[Any]   				=      num_hidden_layers
						__lowerCAmelCase:	int   				=      num_attention_heads
						__lowerCAmelCase:	Tuple   				=      hidden_act
						__lowerCAmelCase:	List[str]   				=      intermediate_size
						__lowerCAmelCase:	List[str]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Any   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	List[Any]   				=      max_position_embeddings
						__lowerCAmelCase:	Optional[int]   				=      type_vocab_size
						__lowerCAmelCase:	Union[str, Any]   				=      initializer_range
						__lowerCAmelCase:	List[str]   				=      layer_norm_eps
						__lowerCAmelCase:	Any   				=      position_embedding_type
						__lowerCAmelCase:	List[str]   				=      quant_mode
						__lowerCAmelCase:	int   				=      force_dequant





class    snake_case     (    __snake_case  ):

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


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





"""simple docstring"""


from pathlib import Path

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      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						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

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

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


