



'''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										=     {
    '''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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				'''xlm-roberta'''




  def __init__(   self  ,__UpperCAmelCase=3_0522  ,__UpperCAmelCase=768  ,__UpperCAmelCase=12  ,__UpperCAmelCase=12  ,__UpperCAmelCase=3072  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=512  ,__UpperCAmelCase=2  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-12  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		int:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase				)

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







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




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







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


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


