


from typing import TYPE_CHECKING

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


snake_case       :					int        =  {
    '''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:
     snake_case       :					List[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:
     snake_case       :					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:
     snake_case       :					str        =  [
         '''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

     snake_case       :					int        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









import os
import sys

from . import rsa_key_generator as rkg

snake_case       :					int        =  1_28
snake_case       :					Any        =  2_56
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:Union[str, Any]											= message.encode('''ascii'''		)

      a		:List[Any]											= []
      for block_start in range(0				,						len(UpperCAmelCase_		)				,						UpperCAmelCase_		):
            a		:List[str]											= 0
            for i in range(UpperCAmelCase_				,						min(block_start + block_size				,						len(UpperCAmelCase_		)		)		):
                  block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
            block_ints.append(UpperCAmelCase_		)
      return block_ints
def        __lowerCamelCase		(				UpperCAmelCase_		:	list[int]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:list[str]											= []
      for block_int in block_ints:
            a		:list[str]											= []
            for i in range(block_size - 1				,						-1				,						-1		):
                  if len(UpperCAmelCase_		) + i < message_length:
                        a		:List[str]											= block_int // (BYTE_SIZE**i)
                        a		:List[str]											= block_int % (BYTE_SIZE**i)
                        block_message.insert(0				,						chr(UpperCAmelCase_		)		)
            message.extend(UpperCAmelCase_		)
      return "".join(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	tuple[int, int]				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:Tuple											= []
      a							,       a		:Any											= key

      for block in get_blocks_from_text(UpperCAmelCase_				,						UpperCAmelCase_		):
            encrypted_blocks.append(pow(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)		)
      return encrypted_blocks
def        __lowerCamelCase		(				UpperCAmelCase_		:	list[int]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	tuple[int, int]				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE				,						):




      """simple docstring"""





      a		:Optional[Any]											= []
      a							,       a		:int											= key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)		)
      return get_text_from_blocks(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      with open(UpperCAmelCase_		) as fo:
            a		:Dict											= fo.read()
      a							,       a							,       a		:Tuple											= content.split(''','''		)
      return (int(UpperCAmelCase_		), int(UpperCAmelCase_		), int(UpperCAmelCase_		))
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE				,						):




      """simple docstring"""





      a							,       a							,       a		:List[str]											= read_key_file(UpperCAmelCase_		)
      if key_size < block_size * 8:
            sys.exit(
                '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
                '''requires the block size to be equal to or greater than the key size. '''
                '''Either decrease the block size or use different keys.'''.format(
                    block_size * 8				,						UpperCAmelCase_		)		)

      a		:int											= [str(UpperCAmelCase_		) for i in encrypt_message(UpperCAmelCase_				,						(n, e)				,						UpperCAmelCase_		)]

      a		:str											= ''','''.join(UpperCAmelCase_		)
      a		:Optional[Any]											= F'''{len(UpperCAmelCase_		)}_{block_size}_{encrypted_content}'''
      with open(UpperCAmelCase_				,						'''w'''		) as fo:
            fo.write(UpperCAmelCase_		)
      return encrypted_content
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str		):




      """simple docstring"""





      a							,       a							,       a		:List[Any]											= read_key_file(UpperCAmelCase_		)
      with open(UpperCAmelCase_		) as fo:
            a		:int											= fo.read()
      a							,       a							,       a		:List[str]											= content.split('''_'''		)
      a		:List[str]											= int(UpperCAmelCase_		)
      a		:List[str]											= int(UpperCAmelCase_		)

      if key_size < block_size * 8:
            sys.exit(
                '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
                '''requires the block size to be equal to or greater than the key size. '''
                '''Did you specify the correct key file and encrypted file?'''.format(
                    block_size * 8				,						UpperCAmelCase_		)		)

      a		:Dict											= []
      for block in encrypted_message.split(''','''		):
            encrypted_blocks.append(int(UpperCAmelCase_		)		)

      return decrypt_message(UpperCAmelCase_				,						UpperCAmelCase_				,						(n, d)				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Union[str, Any]											= '''encrypted_file.txt'''
      a		:List[Any]											= input(R'''Encrypt\Decrypt [e\d]: '''		)

      if response.lower().startswith('''e'''		):
            a		:int											= '''encrypt'''
      elif response.lower().startswith('''d'''		):
            a		:Optional[Any]											= '''decrypt'''

      if mode == "encrypt":
            if not os.path.exists('''rsa_pubkey.txt'''		):
                  rkg.make_key_files('''rsa'''				,						1024		)

            a		:List[str]											= input('''\nEnter message: '''		)
            a		:str											= '''rsa_pubkey.txt'''
            print(F'''Encrypting and writing to {filename}...'''		)
            a		:Optional[int]											= encrypt_and_write_to_file(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)

            print('''\nEncrypted text:'''		)
            print(UpperCAmelCase_		)

      elif mode == "decrypt":
            a		:str											= '''rsa_privkey.txt'''
            print(F'''Reading from {filename} and decrypting...'''		)
            a		:Optional[int]											= read_from_file_and_decrypt(UpperCAmelCase_				,						UpperCAmelCase_		)
            print('''writing decryption to rsa_decryption.txt...'''		)
            with open('''rsa_decryption.txt'''				,						'''w'''		) as dec:
                  dec.write(UpperCAmelCase_		)

            print('''\nDecryption:'''		)
            print(UpperCAmelCase_		)


if __name__ == "__main__":
     main()





