from typing import TYPE_CHECKING

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


lowerCamelCase_				:		Optional[int]       						=       {
    """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_				:		Tuple       						=       ["""OpenAIGPTTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Tuple       						=       [
	    """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_				:		Any       						=       [
	    """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_				:		Optional[int]       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








UpperCAmelCase			=    10**8






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  bool:




      """simple docstring"""

      if n % 10 == 0:
            return False
      snake_case_       =      str(SCREAMING_SNAKE_CASE					)
      return s == s[::-1]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      set()
      snake_case_       =      1
      snake_case_       =      5
      while sum_squares < LIMIT:
            snake_case_       =      first_square + 1
            while sum_squares < LIMIT:
                  if is_palindrome(SCREAMING_SNAKE_CASE					):
                        answer.add(SCREAMING_SNAKE_CASE					)
                  last_square += 1
                  sum_squares += last_square**2
            first_square += 1
            snake_case_       =      first_square**2 + (first_square + 1) ** 2

      return sum(SCREAMING_SNAKE_CASE					)


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