


import os
import sys
import time

from . import transposition_cipher as trans_cipher




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          ='Prehistoric Men.txt'
       _lowerCamelCase	:		Union[str, Any]          ='Output.txt'
       _lowerCamelCase	:		str          =int(input('Enter key: '					)					)
       _lowerCamelCase	:		Any          =input('Encrypt/Decrypt [e/d]: '					)

       if not os.path.exists(SCREAMING_SNAKE_CASE__					):
              print(F'''File {input_file} does not exist. Quitting...'''					)
              sys.exit()
       if os.path.exists(SCREAMING_SNAKE_CASE__					):
              print(F'''Overwrite {output_file}? [y/n]'''					)
              _lowerCamelCase	:		Union[str, Any]          =input('> '					)
              if not response.lower().startswith('y'					):
                     sys.exit()

       _lowerCamelCase	:		Optional[int]          =time.time()
       if mode.lower().startswith('e'					):
              with open(SCREAMING_SNAKE_CASE__					) as f:
                     _lowerCamelCase	:		Dict          =f.read()
              _lowerCamelCase	:		Any          =trans_cipher.encrypt_message(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       elif mode.lower().startswith('d'					):
              with open(SCREAMING_SNAKE_CASE__					) as f:
                     _lowerCamelCase	:		List[Any]          =f.read()
              _lowerCamelCase	:		Dict          =trans_cipher.decrypt_message(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

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

       _lowerCamelCase	:		Union[str, Any]          =round(time.time() - start_time		,      2					)
       print(('Done (', total_time, 'seconds )')					)


if __name__ == "__main__":
 main()






import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =np.arange(start=3		,      stop=SCREAMING_SNAKE_CASE__		,      step=4					).tolist() if k > 0 else [10]
       _lowerCamelCase	:		List[Any]          =_quantize(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       # count number of elements per group
       _lowerCamelCase	:		Union[str, Any]          =np.unique(SCREAMING_SNAKE_CASE__		,      return_counts=SCREAMING_SNAKE_CASE__					)[1]
       _lowerCamelCase	:		int          =[0] + bins + [np.inf]
       logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE__					)					)
       logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE__					)					)
       return groups




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =self.group_ids[idx]
                    buffer_per_group[group_id].append(lowercase_			)
                    samples_per_group[group_id].append(lowercase_			)
                    if len(buffer_per_group[group_id]			) == self.batch_size:
                           yield buffer_per_group[group_id]  # TODO
                           num_batches += 1
                           del buffer_per_group[group_id]
                    assert len(buffer_per_group[group_id]			) < self.batch_size

             # now we have run out of elements that satisfy
             # the group criteria, let's return the remaining
             # elements so that the size of the sampler is
             # deterministic
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =batch_idx[self.batch_size :]
                                  num_remaining -= 1
                    if len(lowercase_			) > 0:
                           yield batch_idx
                           num_remaining -= 1
             assert num_remaining == 0






      def __len__( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




             return (len(self.sampler			) + self.batch_size - 1) // self.batch_size


