





'''simple docstring'''




import os
from collections.abc import Iterator






def 	_lowerCAmelCase      (  __snake_case  :			str = "."      )					->					Iterator[str]:
     for dir_path, dir_names, filenames in os.walk(UpperCAmelCase__      ):
          __A						:			Dict								=	[d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(UpperCAmelCase__      )[1] in (".py", ".ipynb"):
                    yield os.path.join(UpperCAmelCase__			,							UpperCAmelCase__      ).lstrip('./'      )






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					List[str]:
     return f'{i * "  "}*' if i else "\n##"






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					str:
     __A						:			List[Any]								=	old_path.split(os.sep      )
     for i, new_part in enumerate(new_path.split(os.sep      )      ):
          if (i + 1 > len(UpperCAmelCase__      ) or old_parts[i] != new_part) and new_part:
               print(f'{md_prefix(UpperCAmelCase__      )} {new_part.replace("_"			,							" "      ).title()}'      )
     return new_path






def 	_lowerCAmelCase      (  __snake_case  :			str = "."      )					->					None:
     __A						:			List[str]								=	""""""
     for filepath in sorted(good_file_paths(UpperCAmelCase__      )      ):
          __A						:			Optional[Any]								=	os.path.split(UpperCAmelCase__      )
          if filepath != old_path:
               __A						:			Tuple								=	print_path(UpperCAmelCase__			,							UpperCAmelCase__      )
          __A						:			List[Any]								=	(filepath.count(os.sep      ) + 1) if filepath else 0
          __A						:			int								=	f'{filepath}/{filename}'.replace(' '			,							'%20'      )
          __A						:			List[Any]								=	os.path.splitext(filename.replace('_'			,							' '      ).title()      )[0]
          print(f'{md_prefix(UpperCAmelCase__      )} [{filename}]({url})'      )


if __name__ == "__main__":
  print_directory_md('''.''')






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

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




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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