





def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					int										=len(SCREAMING_SNAKE_CASE						)
  a__	:					int										=len(SCREAMING_SNAKE_CASE						)
  a__	:					int										=(
      first_str_length if first_str_length > second_str_length else second_str_length
  )
  a__	:					list										=[]
  for char_count in range(SCREAMING_SNAKE_CASE						):
    if char_count < first_str_length:
      output_list.append(first_str[char_count]						)
    if char_count < second_str_length:
      output_list.append(second_str[char_count]						)
  return "".join(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A        =    {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


