

'''simple docstring'''






import itertools
import string
from collections.abc import Generator, Iterable



def 			__a		(       lowerCAmelCase__ :    List[Any]     ,  lowerCAmelCase__ :    Union[str, Any]      ):
     a__   :      str     =						iter(lowerCamelCase__      )
     while True:
          a__   :      str     =						tuple(itertools.islice(lowerCamelCase__     ,  lowerCamelCase__      )      )
          if not chunk:
               return
          yield chunk



def 			__a		(       lowerCAmelCase__ :    Union[str, Any]      ):
     a__   :      List[str]     =						''''''.join([c.upper() for c in dirty if c in string.ascii_letters]      )
     a__   :      int     =						''''''

     if len(lowerCamelCase__      ) < 2:
          return dirty

     for i in range(len(lowerCamelCase__      ) - 1      ):
          clean += dirty[i]

          if dirty[i] == dirty[i + 1]:
               clean += "X"

     clean += dirty[-1]

     if len(lowerCamelCase__      ) & 1:
          clean += "X"

     return clean



def 			__a		(       lowerCAmelCase__ :    Dict      ):
     # I and J are used interchangeably to allow
     # us to use a 5x5 table (25 letters)
     a__   :      Optional[Any]     =						'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
     # we're using a list instead of a '2d' array because it makes the math
     # for setting up the table and doing the actual encoding/decoding simpler
     a__   :      List[Any]     =						[]

     # copy key chars into the table if they are in `alphabet` ignoring duplicates
     for char in key.upper():
          if char not in table and char in alphabet:
               table.append(lowerCamelCase__      )

    # fill the rest of the table in with the remaining alphabet chars
     for char in alphabet:
          if char not in table:
               table.append(lowerCamelCase__      )

     return table



def 			__a		(       lowerCAmelCase__ :    Tuple     ,  lowerCAmelCase__ :    Optional[int]      ):
     a__   :      str     =						generate_table(lowerCamelCase__      )
     a__   :      Tuple     =						prepare_input(lowerCamelCase__      )
     a__   :      int     =						''''''

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(lowerCamelCase__     ,  2      ):
          a__     ,    a__   :      int     =						divmod(table.index(lowerCamelCase__      )     ,  5      )
          a__     ,    a__   :      List[Any]     =						divmod(table.index(lowerCamelCase__      )     ,  5      )

          if rowa == rowa:
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
          elif cola == cola:
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
          else:  # rectangle
               ciphertext += table[rowa * 5 + cola]
               ciphertext += table[rowa * 5 + cola]

     return ciphertext



def 			__a		(       lowerCAmelCase__ :    List[Any]     ,  lowerCAmelCase__ :    Tuple      ):
     a__   :      List[Any]     =						generate_table(lowerCamelCase__      )
     a__   :      Dict     =						''''''

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(lowerCamelCase__     ,  2      ):
          a__     ,    a__   :      int     =						divmod(table.index(lowerCamelCase__      )     ,  5      )
          a__     ,    a__   :      Any     =						divmod(table.index(lowerCamelCase__      )     ,  5      )

          if rowa == rowa:
               plaintext += table[rowa * 5 + (cola - 1) % 5]
               plaintext += table[rowa * 5 + (cola - 1) % 5]
          elif cola == cola:
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
          else:  # rectangle
               plaintext += table[rowa * 5 + cola]
               plaintext += table[rowa * 5 + cola]

     return plaintext


import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'MCTCTFeatureExtractor'
	lowerCAmelCase__   =					'AutoTokenizer'



	def __init__(       self		,    lowercase		,    lowercase					)							->			str:
					super().__init__(lowercase		,    lowercase					)
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False



	def __call__(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase		,    **lowercase					)

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."					)
									lowerCamelCase_         =     kwargs.pop("raw_speech"					)
					else:
									lowerCamelCase_         =     kwargs.pop("audio"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("sampling_rate"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("text"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process."					)

					if audio is not None:
									lowerCamelCase_         =     self.feature_extractor(lowercase		,    *lowercase		,    sampling_rate=lowercase		,    **lowercase					)
					if text is not None:
									lowerCamelCase_         =     self.tokenizer(lowercase		,    **lowercase					)

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_         =     encodings["input_ids"]
									return inputs



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					return self.tokenizer.batch_decode(*lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase		,    **lowercase					)

					lowerCamelCase_         =     kwargs.pop("input_features"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("labels"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if input_features is not None:
									lowerCamelCase_         =     self.feature_extractor.pad(lowercase		,    *lowercase		,    **lowercase					)
					if labels is not None:
									lowerCamelCase_         =     self.tokenizer.pad(lowercase		,    **lowercase					)

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_         =     labels["input_ids"]
									return input_features



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					return self.tokenizer.decode(*lowercase		,    **lowercase					)



	@contextmanager
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call."					)
					lowerCamelCase_         =     True
					lowerCamelCase_         =     self.tokenizer
					yield
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False


