
"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase							 =							{
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							['''GPT2TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPT2DoubleHeadsModel''',
					    '''GPT2ForQuestionAnswering''',
					    '''GPT2ForSequenceClassification''',
					    '''GPT2ForTokenClassification''',
					    '''GPT2LMHeadModel''',
					    '''GPT2Model''',
					    '''GPT2PreTrainedModel''',
					    '''load_tf_weights_in_gpt2''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFGPT2DoubleHeadsModel''',
					    '''TFGPT2ForSequenceClassification''',
					    '''TFGPT2LMHeadModel''',
					    '''TFGPT2MainLayer''',
					    '''TFGPT2Model''',
					    '''TFGPT2PreTrainedModel''',
					]

try:
					if not is_keras_nlp_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							['''TFGPT2Tokenizer''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
					from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
					from .tokenization_gpta import GPTaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpta_fast import GPTaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpta import (
										    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTaDoubleHeadsModel,
										    GPTaForQuestionAnswering,
										    GPTaForSequenceClassification,
										    GPTaForTokenClassification,
										    GPTaLMHeadModel,
										    GPTaModel,
										    GPTaPreTrainedModel,
										    load_tf_weights_in_gpta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gpta import (
										    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFGPTaDoubleHeadsModel,
										    TFGPTaForSequenceClassification,
										    TFGPTaLMHeadModel,
										    TFGPTaMainLayer,
										    TFGPTaModel,
										    TFGPTaPreTrainedModel,
										)

					try:
										if not is_keras_nlp_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpta_tf import TFGPTaTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
					import sys

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







"""simple docstring"""






import os



def     lowerCAmelCase__ (		)	->Tuple:
			__lowercase						      =					0
			__lowercase						      =					0
			with open(os.path.dirname(__magic_name__			) + "/p022_names.txt"			) as file:
						__lowercase						      =					str(file.readlines()[0]			)
						__lowercase						      =					name.replace("\"" ,      ""			).split(","			)

			name.sort()
			for i in range(len(__magic_name__			)			):
						for j in name[i]:
									temp_sum += ord(__magic_name__			) - ord("A"			) + 1
						total_sum += (i + 1) * temp_sum
						__lowercase						      =					0
			return total_sum


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





