


from typing import TYPE_CHECKING

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


__lowerCamelCase   	=  {
    '''configuration_open_llama''': ['''OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenLlamaConfig'''],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''LlamaTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''LlamaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''OpenLlamaForCausalLM''',
           '''OpenLlamaModel''',
           '''OpenLlamaPreTrainedModel''',
           '''OpenLlamaForSequenceClassification''',
       ]


if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from transformers import LlamaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from transformers import LlamaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_open_llama import (
                  OpenLlamaForCausalLM,
                  OpenLlamaForSequenceClassification,
                  OpenLlamaModel,
                  OpenLlamaPreTrainedModel,
              )


else:
       import sys

       __lowerCamelCase   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


if __name__ == "__main__":
      print(F"{solution() = }")