




from typing import TYPE_CHECKING

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


_lowerCamelCase    				=		{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase    				=		[
     'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'OpenAIGPTDoubleHeadsModel',
     'OpenAIGPTForSequenceClassification',
     'OpenAIGPTLMHeadModel',
     'OpenAIGPTModel',
     'OpenAIGPTPreTrainedModel',
     'load_tf_weights_in_openai_gpt',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase    				=		[
     'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFOpenAIGPTDoubleHeadsModel',
     'TFOpenAIGPTForSequenceClassification',
     'TFOpenAIGPTLMHeadModel',
     'TFOpenAIGPTMainLayer',
     'TFOpenAIGPTModel',
     'TFOpenAIGPTPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .tokenization_openai import OpenAIGPTTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_openai_fast import OpenAIGPTTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_openai import (
      OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OpenAIGPTDoubleHeadsModel,
      OpenAIGPTForSequenceClassification,
      OpenAIGPTLMHeadModel,
      OpenAIGPTModel,
      OpenAIGPTPreTrainedModel,
      load_tf_weights_in_openai_gpt,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTMainLayer,
      TFOpenAIGPTModel,
      TFOpenAIGPTPreTrainedModel,
  )

else:
 import sys

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











import itertools




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple   )						->							List[Any]:
    return (
        int(''''''.join(combination[0:2]   )   ) * int(''''''.join(combination[2:5]   )   )
        == int(''''''.join(combination[5:9]   )   )
    ) or (
        int(''''''.join(combination[0]   )   ) * int(''''''.join(combination[1:5]   )   )
        == int(''''''.join(combination[5:9]   )   )
    )




def       SCREAMING_SNAKE_CASE			( )						->							Optional[Any]:
    return sum(
        {
            int(''''''.join(pandigital[5:9]   )   )
            for pandigital in itertools.permutations('''123456789'''   )
            if is_combination_valid(__UpperCamelCase   )
        }   )


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





