






'''simple docstring'''



from functools import lru_cache



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			set:

   """simple docstring"""
   __a				     =    2
   __a				     =    set()
   while i * i <= n:
      if n % i:
         i += 1
      else:
         n //= i
         factors.add(SCREAMING_SNAKE_CASE__    )
   if n > 1:
      factors.add(SCREAMING_SNAKE_CASE__    )
   return factors



@lru_cache
def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   return len(unique_prime_factors(SCREAMING_SNAKE_CASE__    )    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list    )  ->			bool:

   """simple docstring"""
   return len(set(SCREAMING_SNAKE_CASE__    )    ) in (0, 1)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			list:

   """simple docstring"""
   __a				     =    2

   while True:
      # Increment each value of a generated range
      __a				     =    [base + i for i in range(SCREAMING_SNAKE_CASE__    )]

      # Run elements through out unique_prime_factors function
      # Append our target number to the end.
      __a				     =    [upf_len(SCREAMING_SNAKE_CASE__    ) for x in group]
      checker.append(SCREAMING_SNAKE_CASE__    )

      # If all numbers in the list are equal, return the group variable.
      if equality(SCREAMING_SNAKE_CASE__    ):
         return group

      # Increment our base variable by 1
      base += 1



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int = 4    )  ->			int:

   """simple docstring"""
   __a				     =    run(SCREAMING_SNAKE_CASE__    )
   return results[0] if len(SCREAMING_SNAKE_CASE__    ) else None


if __name__ == "__main__":
 print(solution())







'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Union[str, Any]      			=      ["""Speech2TextTokenizer"""]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      ["""Speech2TextFeatureExtractor"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       List[Any]      			=      [
     """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFSpeech2TextForConditionalGeneration""",
     """TFSpeech2TextModel""",
     """TFSpeech2TextPreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Speech2TextForConditionalGeneration""",
     """Speech2TextModel""",
     """Speech2TextPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 __UpperCamelCase      :       List[str]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)