import os
import sys
import time

from . import transposition_cipher as trans_cipher


def SCREAMING_SNAKE_CASE					(  ):
       _UpperCamelCase  :							Dict	    =	'Prehistoric Men.txt'
       _UpperCamelCase  :							Tuple	    =	'Output.txt'
       _UpperCamelCase  :							Optional[int]	    =	int(input('Enter key: '					)					)
       _UpperCamelCase  :							str	    =	input('Encrypt/Decrypt [e/d]: '					)

       if not os.path.exists(lowercase_					):
              print(F"File {input_file} does not exist. Quitting..."					)
              sys.exit()
       if os.path.exists(lowercase_					):
              print(F"Overwrite {output_file}? [y/n]"					)
              _UpperCamelCase  :							Tuple	    =	input('> '					)
              if not response.lower().startswith('y'					):
                     sys.exit()

       _UpperCamelCase  :							Any	    =	time.time()
       if mode.lower().startswith('e'					):
              with open(lowercase_					) as f:
                     _UpperCamelCase  :							List[str]	    =	f.read()
              _UpperCamelCase  :							List[str]	    =	trans_cipher.encrypt_message(lowercase_		,    lowercase_					)
       elif mode.lower().startswith('d'					):
              with open(lowercase_					) as f:
                     _UpperCamelCase  :							List[Any]	    =	f.read()
              _UpperCamelCase  :							str	    =	trans_cipher.decrypt_message(lowercase_		,    lowercase_					)

       with open(lowercase_		,    'w'					) as output_obj:
              output_obj.write(lowercase_					)

       _UpperCamelCase  :							str	    =	round(time.time() - start_time		,    2					)
       print(('Done (', total_time, 'seconds )')					)


if __name__ == "__main__":
 main()








from collections import OrderedDict
from typing import Mapping

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


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__(pad_token_id=lowerCamelCase_						,			**lowerCamelCase_						)

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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