



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A_		:  List[str]										=  {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:  Optional[Any]										=  [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     A_		:  Optional[int]										=  ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:  int										=  [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     A_		:  int										=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)





'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher






def __UpperCamelCase (      )   ->  None:







     '''simple docstring'''






     _a            =		"Prehistoric Men.txt"
     _a            =		"Output.txt"
     _a            =		int(input("Enter key: "					)					)
     _a            =		input("Encrypt/Decrypt [e/d]: "					)

     if not os.path.exists(__lowerCamelCase					):
          print(F"File {input_file} does not exist. Quitting..."					)
          sys.exit()
     if os.path.exists(__lowerCamelCase					):
          print(F"Overwrite {output_file}? [y/n]"					)
          _a            =		input("> "					)
          if not response.lower().startswith("y"					):
               sys.exit()

     _a            =		time.time()
     if mode.lower().startswith("e"					):
          with open(__lowerCamelCase					) as f:
               _a            =		f.read()
          _a            =		trans_cipher.encrypt_message(__lowerCamelCase   ,	__lowerCamelCase					)
     elif mode.lower().startswith("d"					):
          with open(__lowerCamelCase					) as f:
               _a            =		f.read()
          _a            =		trans_cipher.decrypt_message(__lowerCamelCase   ,	__lowerCamelCase					)

     with open(__lowerCamelCase   ,	"w"					) as output_obj:
          output_obj.write(__lowerCamelCase					)

     _a            =		round(time.time() - start_time   ,	2					)
     print(("Done (", total_time, "seconds )")					)


if __name__ == "__main__":
   main()

