



from collections import OrderedDict
from typing import Mapping

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "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   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'xlm-roberta-xl'







							def __init__(   self,			__a=25_0880,			__a=2560,			__a=36,			__a=32,			__a=1_0240,			__a="gelu",			__a=0.1,			__a=0.1,			__a=514,			__a=1,			__a=0.02,			__a=1E-05,			__a=1,			__a=0,			__a=2,			__a="absolute",			__a=True,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a,			**__a)
														_lowerCAmelCase     :							List[Any]    =    vocab_size
														_lowerCAmelCase     :							List[Any]    =    hidden_size
														_lowerCAmelCase     :							List[str]    =    num_hidden_layers
														_lowerCAmelCase     :							Dict    =    num_attention_heads
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							int    =    intermediate_size
														_lowerCAmelCase     :							List[Any]    =    hidden_dropout_prob
														_lowerCAmelCase     :							Tuple    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    max_position_embeddings
														_lowerCAmelCase     :							Optional[int]    =    type_vocab_size
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Union[str, Any]    =    layer_norm_eps
														_lowerCAmelCase     :							str    =    position_embedding_type
														_lowerCAmelCase     :							Optional[int]    =    use_cache
														_lowerCAmelCase     :							Optional[Any]    =    classifier_dropout



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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





from PIL import Image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    image.size
							_lowerCAmelCase     :							Any    =    0
							_lowerCAmelCase     :							Tuple    =    image.load()
							for i in range(_lowerCamelCase  ):
														for j in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Union[str, Any]    =    pixels[j, i]
																					mean += pixel
							mean //= width * height

							for j in range(_lowerCamelCase  ):
														for i in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Optional[Any]    =    255 if pixels[i, j] > mean else 0
							return image


if __name__ == "__main__":
						_snake_case		=  mean_threshold(Image.open("path_to_image").convert("L"))
						image.save("output_image_path")
