



"""simple docstring"""


from typing import TYPE_CHECKING

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


snake_case_								=			{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			["""GPT2TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """GPT2DoubleHeadsModel""",
				    """GPT2ForQuestionAnswering""",
				    """GPT2ForSequenceClassification""",
				    """GPT2ForTokenClassification""",
				    """GPT2LMHeadModel""",
				    """GPT2Model""",
				    """GPT2PreTrainedModel""",
				    """load_tf_weights_in_gpt2""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFGPT2DoubleHeadsModel""",
				    """TFGPT2ForSequenceClassification""",
				    """TFGPT2LMHeadModel""",
				    """TFGPT2MainLayer""",
				    """TFGPT2Model""",
				    """TFGPT2PreTrainedModel""",
				]

try:
				if not is_keras_nlp_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			["""TFGPT2Tokenizer"""]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
				from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
				from .tokenization_gpta import GPTaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_fast import GPTaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gpta import (
								    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTaDoubleHeadsModel,
								    GPTaForQuestionAnswering,
								    GPTaForSequenceClassification,
								    GPTaForTokenClassification,
								    GPTaLMHeadModel,
								    GPTaModel,
								    GPTaPreTrainedModel,
								    load_tf_weights_in_gpta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_gpta import (
								    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFGPTaDoubleHeadsModel,
								    TFGPTaForSequenceClassification,
								    TFGPTaLMHeadModel,
								    TFGPTaMainLayer,
								    TFGPTaModel,
								    TFGPTaPreTrainedModel,
								)

				try:
								if not is_keras_nlp_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_tf import TFGPTaTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
				import sys

				snake_case_								=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











import random
import sys

UpperCAmelCase__    :							str       		=     'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Any      =     input('Enter message: '				)
  _lowercase:			List[str]      =     'LFWOAYUISVKMNXPBDCRJTQEGHZ'
  _lowercase:			Tuple      =     input('Encrypt/Decrypt [e/d]: '				)

  check_valid_key(UpperCamelCase				)

  if resp.lower().startswith('e'				):
    _lowercase:			int      =     'encrypt'
    _lowercase:			Optional[int]      =     encrypt_message(UpperCamelCase       ,  UpperCamelCase				)
  elif resp.lower().startswith('d'				):
    _lowercase:			str      =     'decrypt'
    _lowercase:			Dict      =     decrypt_message(UpperCamelCase       ,  UpperCamelCase				)

  print(F'\n{mode.title()}ion: \n{translated}'				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		None:





  """simple docstring"""





  _lowercase:			List[Any]      =     list(UpperCamelCase				)
  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  key_list.sort()
  letters_list.sort()

  if key_list != letters_list:
    sys.exit('Error in the key or symbol set.'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'encrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'decrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     ''
  _lowercase:			Any      =     LETTERS
  _lowercase:			Tuple      =     key

  if mode == "decrypt":
    _lowercase  ,       _lowercase:			Tuple      =     chars_b, chars_a

  for symbol in message:
    if symbol.upper() in chars_a:
      _lowercase:			int      =     chars_a.find(symbol.upper()				)
      if symbol.isupper():
        translated += chars_b[sym_index].upper()
      else:
        translated += chars_b[sym_index].lower()
    else:
      translated += symbol

  return translated


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  random.shuffle(UpperCamelCase				)
  return "".join(UpperCamelCase				)


if __name__ == "__main__":
   main()
