import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812


def      a__ (		):
      print("Making key files..."				)
      make_key_files("rsa"     ,      1_0_2_4				)
      print("Key files generation successful."				)


def      a__ (		__UpperCamelCase				):
      print("Generating prime p..."				)
      SCREAMING_SNAKE_CASE_          =  rabinMiller.generate_large_prime(__UpperCamelCase				)
      print("Generating prime q..."				)
      SCREAMING_SNAKE_CASE_          =  rabinMiller.generate_large_prime(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  p * q

      print("Generating e that is relatively prime to (p - 1) * (q - 1)..."				)
      while True:
            SCREAMING_SNAKE_CASE_          =  random.randrange(2 ** (key_size - 1)     ,      2 ** (key_size)				)
            if cryptoMath.gcd(__UpperCamelCase     ,      (p - 1) * (q - 1)				) == 1:
                  break

      print("Calculating d that is mod inverse of e..."				)
      SCREAMING_SNAKE_CASE_          =  cryptoMath.find_mod_inverse(__UpperCamelCase     ,      (p - 1) * (q - 1)				)

      SCREAMING_SNAKE_CASE_          =  (n, e)
      SCREAMING_SNAKE_CASE_          =  (n, d)
      return (public_key, private_key)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      if os.path.exists(F'''{name}_pubkey.txt'''				) or os.path.exists(F'''{name}_privkey.txt'''				):
            print("\nWARNING:"				)
            print(
                F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
                "Use a different name or delete these files and re-run this program."				)
            sys.exit()

      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  generate_key(__UpperCamelCase				)
      print(F'''\nWriting public key to file {name}_pubkey.txt...'''				)
      with open(F'''{name}_pubkey.txt'''     ,      "w"				) as out_file:
            out_file.write(F'''{key_size},{public_key[0]},{public_key[1]}'''				)

      print(F'''Writing private key to file {name}_privkey.txt...'''				)
      with open(F'''{name}_privkey.txt'''     ,      "w"				) as out_file:
            out_file.write(F'''{key_size},{private_key[0]},{private_key[1]}'''				)


if __name__ == "__main__":
      main()



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


