






'''simple docstring'''





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							:							Optional[Any]  							=		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"xlm-roberta-xl"

    def __init__(					self     :     List[Any]				,     __lowerCamelCase     :     Union[str, Any]=250880				,     __lowerCamelCase     :     Dict=2560				,     __lowerCamelCase     :     Union[str, Any]=36				,     __lowerCamelCase     :     Optional[int]=32				,     __lowerCamelCase     :     str=10240				,     __lowerCamelCase     :     Any="gelu"				,     __lowerCamelCase     :     Optional[Any]=0.1				,     __lowerCamelCase     :     Optional[int]=0.1				,     __lowerCamelCase     :     Tuple=514				,     __lowerCamelCase     :     Optional[Any]=1				,     __lowerCamelCase     :     Dict=0.0_2				,     __lowerCamelCase     :     Tuple=1E-05				,     __lowerCamelCase     :     Dict=1				,     __lowerCamelCase     :     int=0				,     __lowerCamelCase     :     Dict=2				,     __lowerCamelCase     :     List[Any]="absolute"				,     __lowerCamelCase     :     str=True				,     __lowerCamelCase     :     str=None				,     **__lowerCamelCase     :     List[str]				,     )							->							List[Any]:

     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase				,     bos_token_id=__lowerCamelCase				,     eos_token_id=__lowerCamelCase				,     **__lowerCamelCase     )
     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     type_vocab_size
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     position_embedding_type
     lowerCamelCase__  =     use_cache
     lowerCamelCase__  =     classifier_dropout
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




     if self.task == "multiple-choice":
      lowerCamelCase__  =     {0: "batch", 1: "choice", 2: "sequence"}
     else:
      lowerCamelCase__  =     {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    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		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							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

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

