


UpperCAmelCase_				:			str   =	{
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '\"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_				:			List[Any]   =	{value: key for key, value in MORSE_CODE_DICT.items()}







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple    )     ->		str:

       """simple docstring"""

       return " ".join(MORSE_CODE_DICT[char] for char in message.upper()    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		str:

       """simple docstring"""

       return "".join(REVERSE_DICT[char] for char in message.split()    )







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Any							=  '''Morse code here!'''
       print(__UpperCAmelCase    )
       a_ :		List[Any]							=  encrypt(__UpperCAmelCase    )
       print(__UpperCAmelCase    )
       a_ :		Union[str, Any]							=  decrypt(__UpperCAmelCase    )
       print(__UpperCAmelCase    )


if __name__ == "__main__":
   main()






import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  list(filter(lambda __A    : len(__A    ) != 0  ,   [line.strip() for line in raw_story.split('\n'    )]    )    )

       # for some unknown reason some lines miss a period, add it
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  lines.popleft()
                     if element.startswith('@highlight'    ):
                            break
                     story_lines.append(__A    )
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		Any							=  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
       if line.startswith('@highlight'    ):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

       if len(__A    ) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(__A    ))    )
              return sequence







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2    )
              batch_embeddings.append(__A    )
       return torch.tensor(__A    )


