
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    '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       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"xlm-roberta-xl"

							def __init__(self  ,  UpperCAmelCase=250880  ,  UpperCAmelCase=2560  ,  UpperCAmelCase=36  ,  UpperCAmelCase=32  ,  UpperCAmelCase=10240  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=514  ,  UpperCAmelCase=1  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-0_5  ,  UpperCAmelCase=1  ,  UpperCAmelCase=0  ,  UpperCAmelCase=2  ,  UpperCAmelCase="absolute"  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       Any:
														super().__init__(pad_token_id=UpperCAmelCase  ,  bos_token_id=UpperCAmelCase  ,  eos_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  position_embedding_type
														_snake_case       =  use_cache
														_snake_case       =  classifier_dropout


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							@property
							def 	lowercase							(self  )					->       Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					_snake_case       =  {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					_snake_case       =  {0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""input_ids""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														    ]  )

'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														if dst_width < 0 or dst_height < 0:
																					raise ValueError("""Destination width/height should be > 0"""  )

														_snake_case       =  img
														_snake_case       =  img.shape[1]
														_snake_case       =  img.shape[0]
														_snake_case       =  dst_width
														_snake_case       =  dst_height

														_snake_case       =  self.src_w / self.dst_w
														_snake_case       =  self.src_h / self.dst_h

														_snake_case       =  _snake_case       =  (
														    np.ones((self.dst_h, self.dst_w, 3)  ,  np.uinta  ) * 255
														)

							def 	lowercase							(self  )					->       List[Any]:
														for i in range(self.dst_h  ):
																					for j in range(self.dst_w  ):
																												_snake_case       =  self.img[self.get_y(UpperCAmelCase  )][self.get_x(UpperCAmelCase  )]

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_x * x  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_y * y  )


if __name__ == "__main__":
				__lowerCAmelCase							,    __lowerCAmelCase												=	800, 600
				__lowerCAmelCase												=	imread('image_data/lena.jpg', 1)
				__lowerCAmelCase												=	NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()