
"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase      :							str          =					{
    "auto_factory": ["get_values"],
    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
    "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
    "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							Optional[Any]          =					[
          "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
          "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
          "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
          "MODEL_FOR_BACKBONE_MAPPING",
          "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
          "MODEL_FOR_CAUSAL_LM_MAPPING",
          "MODEL_FOR_CTC_MAPPING",
          "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
          "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
          "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
          "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
          "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
          "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
          "MODEL_FOR_MASKED_LM_MAPPING",
          "MODEL_FOR_MASK_GENERATION_MAPPING",
          "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
          "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
          "MODEL_FOR_OBJECT_DETECTION_MAPPING",
          "MODEL_FOR_PRETRAINING_MAPPING",
          "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
          "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
          "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
          "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
          "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
          "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
          "MODEL_FOR_TEXT_ENCODING_MAPPING",
          "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
          "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
          "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
          "MODEL_FOR_VISION_2_SEQ_MAPPING",
          "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
          "MODEL_MAPPING",
          "MODEL_WITH_LM_HEAD_MAPPING",
          "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
          "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
          "AutoModel",
          "AutoBackbone",
          "AutoModelForAudioClassification",
          "AutoModelForAudioFrameClassification",
          "AutoModelForAudioXVector",
          "AutoModelForCausalLM",
          "AutoModelForCTC",
          "AutoModelForDepthEstimation",
          "AutoModelForImageClassification",
          "AutoModelForImageSegmentation",
          "AutoModelForInstanceSegmentation",
          "AutoModelForMaskGeneration",
          "AutoModelForTextEncoding",
          "AutoModelForMaskedImageModeling",
          "AutoModelForMaskedLM",
          "AutoModelForMultipleChoice",
          "AutoModelForNextSentencePrediction",
          "AutoModelForObjectDetection",
          "AutoModelForPreTraining",
          "AutoModelForQuestionAnswering",
          "AutoModelForSemanticSegmentation",
          "AutoModelForSeq2SeqLM",
          "AutoModelForSequenceClassification",
          "AutoModelForSpeechSeq2Seq",
          "AutoModelForTableQuestionAnswering",
          "AutoModelForTokenClassification",
          "AutoModelForUniversalSegmentation",
          "AutoModelForVideoClassification",
          "AutoModelForVision2Seq",
          "AutoModelForVisualQuestionAnswering",
          "AutoModelForDocumentQuestionAnswering",
          "AutoModelWithLMHead",
          "AutoModelForZeroShotImageClassification",
          "AutoModelForZeroShotObjectDetection",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							List[str]          =					[
          "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
          "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
          "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
          "TF_MODEL_FOR_MASK_GENERATION_MAPPING",
          "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
          "TF_MODEL_FOR_MASKED_LM_MAPPING",
          "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
          "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
          "TF_MODEL_FOR_PRETRAINING_MAPPING",
          "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
          "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
          "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
          "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
          "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
          "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
          "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
          "TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
          "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
          "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
          "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
          "TF_MODEL_MAPPING",
          "TF_MODEL_WITH_LM_HEAD_MAPPING",
          "TFAutoModel",
          "TFAutoModelForAudioClassification",
          "TFAutoModelForCausalLM",
          "TFAutoModelForImageClassification",
          "TFAutoModelForMaskedImageModeling",
          "TFAutoModelForMaskedLM",
          "TFAutoModelForMaskGeneration",
          "TFAutoModelForMultipleChoice",
          "TFAutoModelForNextSentencePrediction",
          "TFAutoModelForPreTraining",
          "TFAutoModelForDocumentQuestionAnswering",
          "TFAutoModelForQuestionAnswering",
          "TFAutoModelForSemanticSegmentation",
          "TFAutoModelForSeq2SeqLM",
          "TFAutoModelForSequenceClassification",
          "TFAutoModelForSpeechSeq2Seq",
          "TFAutoModelForTableQuestionAnswering",
          "TFAutoModelForTextEncoding",
          "TFAutoModelForTokenClassification",
          "TFAutoModelForVision2Seq",
          "TFAutoModelForZeroShotImageClassification",
          "TFAutoModelWithLMHead",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							Any          =					[
          "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
          "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
          "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
          "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
          "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
          "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
          "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
          "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
          "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
          "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
          "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
          "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
          "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
          "FLAX_MODEL_MAPPING",
          "FlaxAutoModel",
          "FlaxAutoModelForCausalLM",
          "FlaxAutoModelForImageClassification",
          "FlaxAutoModelForMaskedLM",
          "FlaxAutoModelForMultipleChoice",
          "FlaxAutoModelForNextSentencePrediction",
          "FlaxAutoModelForPreTraining",
          "FlaxAutoModelForQuestionAnswering",
          "FlaxAutoModelForSeq2SeqLM",
          "FlaxAutoModelForSequenceClassification",
          "FlaxAutoModelForSpeechSeq2Seq",
          "FlaxAutoModelForTokenClassification",
          "FlaxAutoModelForVision2Seq",
      ]


if TYPE_CHECKING:
      from .auto_factory import get_values
      from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
      from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
      from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
      from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
      from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_auto import (
                MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
                MODEL_FOR_AUDIO_XVECTOR_MAPPING,
                MODEL_FOR_BACKBONE_MAPPING,
                MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
                MODEL_FOR_CAUSAL_LM_MAPPING,
                MODEL_FOR_CTC_MAPPING,
                MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
                MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
                MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
                MODEL_FOR_MASK_GENERATION_MAPPING,
                MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                MODEL_FOR_MASKED_LM_MAPPING,
                MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                MODEL_FOR_OBJECT_DETECTION_MAPPING,
                MODEL_FOR_PRETRAINING_MAPPING,
                MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_TEXT_ENCODING_MAPPING,
                MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
                MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
                MODEL_FOR_VISION_2_SEQ_MAPPING,
                MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
                MODEL_MAPPING,
                MODEL_WITH_LM_HEAD_MAPPING,
                AutoBackbone,
                AutoModel,
                AutoModelForAudioClassification,
                AutoModelForAudioFrameClassification,
                AutoModelForAudioXVector,
                AutoModelForCausalLM,
                AutoModelForCTC,
                AutoModelForDepthEstimation,
                AutoModelForDocumentQuestionAnswering,
                AutoModelForImageClassification,
                AutoModelForImageSegmentation,
                AutoModelForInstanceSegmentation,
                AutoModelForMaskedImageModeling,
                AutoModelForMaskedLM,
                AutoModelForMaskGeneration,
                AutoModelForMultipleChoice,
                AutoModelForNextSentencePrediction,
                AutoModelForObjectDetection,
                AutoModelForPreTraining,
                AutoModelForQuestionAnswering,
                AutoModelForSemanticSegmentation,
                AutoModelForSeqaSeqLM,
                AutoModelForSequenceClassification,
                AutoModelForSpeechSeqaSeq,
                AutoModelForTableQuestionAnswering,
                AutoModelForTextEncoding,
                AutoModelForTokenClassification,
                AutoModelForUniversalSegmentation,
                AutoModelForVideoClassification,
                AutoModelForVisionaSeq,
                AutoModelForVisualQuestionAnswering,
                AutoModelForZeroShotImageClassification,
                AutoModelForZeroShotObjectDetection,
                AutoModelWithLMHead,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_auto import (
                TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_MASK_GENERATION_MAPPING,
                TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                TF_MODEL_FOR_MASKED_LM_MAPPING,
                TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                TF_MODEL_FOR_PRETRAINING_MAPPING,
                TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
                TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                TF_MODEL_MAPPING,
                TF_MODEL_WITH_LM_HEAD_MAPPING,
                TFAutoModel,
                TFAutoModelForAudioClassification,
                TFAutoModelForCausalLM,
                TFAutoModelForDocumentQuestionAnswering,
                TFAutoModelForImageClassification,
                TFAutoModelForMaskedImageModeling,
                TFAutoModelForMaskedLM,
                TFAutoModelForMaskGeneration,
                TFAutoModelForMultipleChoice,
                TFAutoModelForNextSentencePrediction,
                TFAutoModelForPreTraining,
                TFAutoModelForQuestionAnswering,
                TFAutoModelForSemanticSegmentation,
                TFAutoModelForSeqaSeqLM,
                TFAutoModelForSequenceClassification,
                TFAutoModelForSpeechSeqaSeq,
                TFAutoModelForTableQuestionAnswering,
                TFAutoModelForTextEncoding,
                TFAutoModelForTokenClassification,
                TFAutoModelForVisionaSeq,
                TFAutoModelForZeroShotImageClassification,
                TFAutoModelWithLMHead,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_auto import (
                FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
                FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_MASKED_LM_MAPPING,
                FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                FLAX_MODEL_FOR_PRETRAINING_MAPPING,
                FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
                FLAX_MODEL_MAPPING,
                FlaxAutoModel,
                FlaxAutoModelForCausalLM,
                FlaxAutoModelForImageClassification,
                FlaxAutoModelForMaskedLM,
                FlaxAutoModelForMultipleChoice,
                FlaxAutoModelForNextSentencePrediction,
                FlaxAutoModelForPreTraining,
                FlaxAutoModelForQuestionAnswering,
                FlaxAutoModelForSeqaSeqLM,
                FlaxAutoModelForSequenceClassification,
                FlaxAutoModelForSpeechSeqaSeq,
                FlaxAutoModelForTokenClassification,
                FlaxAutoModelForVisionaSeq,
            )

else:
      import sys

      UpperCamelCase      :							Optional[Any]          =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""

UpperCamelCase      :							int          =					1_0**8






def 				A		(							snake_case :int						) ->      bool:
 if n % 1_0 == 0:
  return False
 __UpperCamelCase							    =			str(snake_case						)
 return s == s[::-1]






def 				A		(							) ->      int:
 __UpperCamelCase							    =			set()
 __UpperCamelCase							    =			1
 __UpperCamelCase							    =			5
 while sum_squares < LIMIT:
  __UpperCamelCase							    =			first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(snake_case						):
    answer.add(snake_case						)
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  __UpperCamelCase							    =			first_square**2 + (first_square + 1) ** 2

 return sum(snake_case						)


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



