





import bisect
import copy
from collections import defaultdict

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

from utils import logger



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		copy.deepcopy(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		sorted(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		[bisect.bisect_right(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ) for y in x]
      return quantized



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=0      )					->  int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		np.arange(start=3					,		stop=_SCREAMING_SNAKE_CASE					,		step=4      ).tolist() if k > 0 else [10]
      SCREAMING_SNAKE_CASE						=		_quantize(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )
      # count number of elements per group
      SCREAMING_SNAKE_CASE						=		np.unique(_SCREAMING_SNAKE_CASE					,		return_counts=_SCREAMING_SNAKE_CASE      )[1]
      SCREAMING_SNAKE_CASE						=		[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 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     def __init__(				self		:       Optional[Any]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       Any			)	->						Optional[int]:




           '''simple docstring'''


           if not isinstance(lowerCamelCase__		,lowerCamelCase__			):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__			)			)
           SCREAMING_SNAKE_CASE						=		sampler
           SCREAMING_SNAKE_CASE						=		group_ids
           SCREAMING_SNAKE_CASE						=		batch_size






     def __iter__(				self		:       Union[str, Any]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		defaultdict(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		defaultdict(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		0
           for idx in self.sampler:
                 SCREAMING_SNAKE_CASE						=		self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase__			)
                 samples_per_group[group_id].append(lowerCamelCase__			)
                 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
           SCREAMING_SNAKE_CASE						=		len(self			)
           SCREAMING_SNAKE_CASE						=		expected_num_batches - num_batches
           if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 SCREAMING_SNAKE_CASE						=		[]
                 for group_id, idxs in sorted(buffer_per_group.items()		,key=lambda lowerCamelCase__			: x[0]			):
                       batch_idx.extend(lowerCamelCase__			)
                       if len(lowerCamelCase__			) >= self.batch_size:
                             yield batch_idx[: self.batch_size]
                             SCREAMING_SNAKE_CASE						=		batch_idx[self.batch_size :]
                             num_remaining -= 1
                 if len(lowerCamelCase__			) > 0:
                       yield batch_idx
                       num_remaining -= 1
           assert num_remaining == 0






     def __len__(				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


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









import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


