






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):
				_UpperCamelCase					:       Any							       = "MCTCTFeatureExtractor"
				_UpperCamelCase					:       List[str]							       = "AutoTokenizer"


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											super().__init__(_a ,						_a      )
											_lowercase							:   Optional[Any] 							=   self.feature_extractor
											_lowercase							:   Optional[int] 							=   False


				def __call__(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*_a ,						**_a      )

											if "raw_speech" in kwargs:
																		warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'      )
																		_lowercase							:   Optional[Any] 							=   kwargs.pop('raw_speech'      )
											else:
																		_lowercase							:   List[str] 							=   kwargs.pop('audio' ,						_a      )
											_lowercase							:   Optional[Any] 							=   kwargs.pop('sampling_rate' ,						_a      )
											_lowercase							:   int 							=   kwargs.pop('text' ,						_a      )
											if len(_a      ) > 0:
																		_lowercase							:   Union[str, Any] 							=   args[0]
																		_lowercase							:   List[Any] 							=   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:
																		_lowercase							:   Dict 							=   self.feature_extractor(_a ,						*_a ,						sampling_rate=_a ,						**_a      )
											if text is not None:
																		_lowercase							:   Optional[Any] 							=   self.tokenizer(_a ,						**_a      )

											if text is None:
																		return inputs
											elif audio is None:
																		return encodings
											else:
																		_lowercase							:   Dict 							=   encodings['input_ids']
																		return inputs


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											return self.tokenizer.batch_decode(*_a ,						**_a      )


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor.pad(*_a ,						**_a      )

											_lowercase							:   List[Any] 							=   kwargs.pop('input_features' ,						_a      )
											_lowercase							:   List[str] 							=   kwargs.pop('labels' ,						_a      )
											if len(_a      ) > 0:
																		_lowercase							:   List[str] 							=   args[0]
																		_lowercase							:   Tuple 							=   args[1:]

											if input_features is not None:
																		_lowercase							:   Optional[Any] 							=   self.feature_extractor.pad(_a ,						*_a ,						**_a      )
											if labels is not None:
																		_lowercase							:   List[Any] 							=   self.tokenizer.pad(_a ,						**_a      )

											if labels is None:
																		return input_features
											elif input_features is None:
																		return labels
											else:
																		_lowercase							:   Optional[int] 							=   labels['input_ids']
																		return input_features


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											return self.tokenizer.decode(*_a ,						**_a      )


				@contextmanager
				def 						__a					(					self      ):
											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.'      )
											_lowercase							:   Union[str, Any] 							=   True
											_lowercase							:   int 							=   self.tokenizer
											yield
											_lowercase							:   Union[str, Any] 							=   self.feature_extractor
											_lowercase							:   str 							=   False









'''simple docstring'''





from __future__ import annotations

_UpperCamelCase											=		10

def 			a_	(				_lowerCAmelCase			) -> list[int]:
  __lowerCamelCase			:			str       			=					1
  __lowerCamelCase			:			Union[str, Any]       			=					max(_lowerCAmelCase			)
  while placement <= max_digit:
    # declare and initialize empty buckets
    __lowerCamelCase			:			list[list]       			=					[[] for _ in range(_lowerCAmelCase			)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __lowerCamelCase			:			List[str]       			=					int((i / placement) % RADIX			)
      buckets[tmp].append(_lowerCAmelCase			)
    # put each buckets' contents into list_of_ints
    __lowerCamelCase			:			Dict       			=					0
    for b in range(_lowerCAmelCase			):
      for i in buckets[b]:
        __lowerCamelCase			:			List[str]       			=					i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






