


from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


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






from collections import OrderedDict
from typing import Mapping

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


A      :    Union[str, Any]		=					logging.get_logger(__name__)

A      :    Optional[Any]		=					{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"xlm-roberta"





       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[Any]=30522					,      __lowerCamelCase							:			List[Any]=768					,      __lowerCamelCase							:			Union[str, Any]=12					,      __lowerCamelCase							:			int=12					,      __lowerCamelCase							:			Dict=3072					,      __lowerCamelCase							:			Optional[int]="gelu"					,      __lowerCamelCase							:			Optional[Any]=0.1					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			str=512					,      __lowerCamelCase							:			Optional[Any]=2					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			List[str]=1E-1_2					,      __lowerCamelCase							:			Optional[int]=1					,      __lowerCamelCase							:			Union[str, Any]=0					,      __lowerCamelCase							:			Optional[int]=2					,      __lowerCamelCase							:			Dict="absolute"					,      __lowerCamelCase							:			Optional[Any]=True					,      __lowerCamelCase							:			Tuple=None					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



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

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







class 			_lowercase (							lowercase__):


       """simple docstring"""




       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



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


