



"""simple docstring"""






from __future__ import annotations






def 	snake_case__  (						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			if not list_of_ints:
						return []

			if min(_A      ) < 0:
						raise ValueError('''All numbers must be positive'''      )

			lowerCamelCase__						:						List[Any]						      =max(len(bin(_A      )[2:]      ) for x in list_of_ints      )
			return _msd_radix_sort(_A				,						_A      )






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			if bit_position == 0 or len(_A      ) in [0, 1]:
						return list_of_ints

			lowerCamelCase__						:						Optional[int]						      =[]
			lowerCamelCase__						:						List[str]						      =[]
			# Split numbers based on bit at bit_position from the right
			for number in list_of_ints:
						if (number >> (bit_position - 1)) & 1:
									# number has a one at bit bit_position
									ones.append(_A      )
						else:
									# number has a zero at bit bit_position
									zeros.append(_A      )

    # recursively split both lists further
			lowerCamelCase__						:						Union[str, Any]						      =_msd_radix_sort(_A				,						bit_position - 1      )
			lowerCamelCase__						:						List[str]						      =_msd_radix_sort(_A				,						bit_position - 1      )

			# recombine lists
			lowerCamelCase__						:						Dict						      =zeros
			res.extend(_A      )

			return res






def 	snake_case__  (						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						str						      =len(_A      )
			if not list_of_ints or length == 1:
						return

			if min(_A      ) < 0:
						raise ValueError('''All numbers must be positive'''      )

			lowerCamelCase__						:						str						      =max(len(bin(_A      )[2:]      ) for x in list_of_ints      )
			_msd_radix_sort_inplace(_A				,						_A				,						0				,						_A      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       List[Any]      ):
			"""simple docstring"""
			if bit_position == 0 or end_index - begin_index <= 1:
						return

			bit_position -= 1

			lowerCamelCase__						:						Optional[Any]						      =begin_index
			lowerCamelCase__						:						List[str]						      =end_index - 1
			while i <= j:
						lowerCamelCase__						:						List[Any]						      =False
						if not (list_of_ints[i] >> bit_position) & 1:
									# found zero at the beginning
									i += 1
									lowerCamelCase__						:						Any						      =True
						if (list_of_ints[j] >> bit_position) & 1:
									# found one at the end
									j -= 1
									lowerCamelCase__						:						Optional[Any]						      =True

						if changed:
									continue

						lowerCamelCase__						:						Any						      =list_of_ints[j], list_of_ints[i]
						j -= 1
						if j != i:
									i += 1

			_msd_radix_sort_inplace(_A				,						_A				,						_A				,						_A      )
			_msd_radix_sort_inplace(_A				,						_A				,						_A				,						_A      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__			:  Tuple					     =     {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Tuple					     =     [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Optional[int]					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




