






import random
import sys

__A  							=  "ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def 			lowercase__  (				)  ->							None:







       """simple docstring"""






       __UpperCAmelCase    							=input("""Enter message: """		)
       __UpperCAmelCase    							="""LFWOAYUISVKMNXPBDCRJTQEGHZ"""
       __UpperCAmelCase    							=input("""Encrypt/Decrypt [e/d]: """		)

       check_valid_key(__snake_case		)

       if resp.lower().startswith("""e"""		):
              __UpperCAmelCase    							="""encrypt"""
              __UpperCAmelCase    							=encrypt_message(__snake_case     ,			__snake_case		)
       elif resp.lower().startswith("""d"""		):
              __UpperCAmelCase    							="""decrypt"""
              __UpperCAmelCase    							=decrypt_message(__snake_case     ,			__snake_case		)

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







def 			lowercase__  (				A_:	List[Any]		)  ->							None:







       """simple docstring"""






       __UpperCAmelCase    							=list(__snake_case		)
       __UpperCAmelCase    							=list(__snake_case		)
       key_list.sort()
       letters_list.sort()

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







def 			lowercase__  (				A_:	Optional[Any]     ,			A_:	Optional[Any]		)  ->							str:







       """simple docstring"""






       return translate_message(__snake_case     ,			__snake_case     ,			"""encrypt"""		)







def 			lowercase__  (				A_:	Dict     ,			A_:	List[str]		)  ->							str:







       """simple docstring"""






       return translate_message(__snake_case     ,			__snake_case     ,			"""decrypt"""		)







def 			lowercase__  (				A_:	Any     ,			A_:	int     ,			A_:	Tuple		)  ->							str:







       """simple docstring"""






       __UpperCAmelCase    							=""""""
       __UpperCAmelCase    							=LETTERS
       __UpperCAmelCase    							=key

       if mode == "decrypt":
              __UpperCAmelCase							,   __UpperCAmelCase    							=chars_b, chars_a

       for symbol in message:
              if symbol.upper() in chars_a:
                     __UpperCAmelCase    							=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 			lowercase__  (				)  ->							str:







       """simple docstring"""






       __UpperCAmelCase    							=list(__snake_case		)
       random.shuffle(__snake_case		)
       return "".join(__snake_case		)


if __name__ == "__main__":
   main()












import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       ErnieMForInformationExtraction,
       ErnieMForMultipleChoice,
       ErnieMForQuestionAnswering,
       ErnieMForSequenceClassification,
       ErnieMForTokenClassification,
       ErnieMModel,
   )
   from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class 							_A :
    """simple docstring"""

    def __init__(							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    List[str]=13   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=7   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    str=99   ,    __SCREAMING_SNAKE_CASE			:    int=32   ,    __SCREAMING_SNAKE_CASE			:    str=5   ,    __SCREAMING_SNAKE_CASE			:    int=4   ,    __SCREAMING_SNAKE_CASE			:    List[str]=37   ,    __SCREAMING_SNAKE_CASE			:    int="gelu"   ,    __SCREAMING_SNAKE_CASE			:    Tuple=0.1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0.1   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=512   ,    __SCREAMING_SNAKE_CASE			:    int=16   ,    __SCREAMING_SNAKE_CASE			:    int=2   ,    __SCREAMING_SNAKE_CASE			:    Any=0.02   ,    __SCREAMING_SNAKE_CASE			:    Dict=3   ,    __SCREAMING_SNAKE_CASE			:    Dict=4   ,    __SCREAMING_SNAKE_CASE			:    str=None   ,    )			->       Optional[Any]:
           __UpperCAmelCase    							=parent
           __UpperCAmelCase    							=batch_size
           __UpperCAmelCase    							=seq_length
           __UpperCAmelCase    							=is_training
           __UpperCAmelCase    							=use_input_mask
           __UpperCAmelCase    							=use_labels
           __UpperCAmelCase    							=vocab_size
           __UpperCAmelCase    							=hidden_size
           __UpperCAmelCase    							=num_hidden_layers
           __UpperCAmelCase    							=num_attention_heads
           __UpperCAmelCase    							=intermediate_size
           __UpperCAmelCase    							=hidden_act
           __UpperCAmelCase    							=hidden_dropout_prob
           __UpperCAmelCase    							=attention_probs_dropout_prob
           __UpperCAmelCase    							=max_position_embeddings
           __UpperCAmelCase    							=type_vocab_size
           __UpperCAmelCase    							=type_sequence_label_size
           __UpperCAmelCase    							=initializer_range
           __UpperCAmelCase    							=num_labels
           __UpperCAmelCase    							=num_choices
           __UpperCAmelCase    							=scope



    def _a       (							self			:    str			)			->       Optional[Any]:
           __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)

           __UpperCAmelCase    							=None
           if self.use_input_mask:
                  __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)

           __UpperCAmelCase    							=None
           __UpperCAmelCase    							=None
           __UpperCAmelCase    							=None
           if self.use_labels:
                  __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.type_sequence_label_size			)
                  __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels			)
                  __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.num_choices			)

           __UpperCAmelCase    							=self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



    def _a       (							self			:    Optional[Any]			)			->       Any:
           __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)

           __UpperCAmelCase    							=None
           if self.use_input_mask:
                  __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)
           __UpperCAmelCase    							=self.get_config()

           return config, input_ids, input_mask



    def _a       (							self			:    List[str]			)			->       List[str]:
           return ErnieMConfig(
               vocab_size=self.vocab_size   ,    hidden_size=self.hidden_size   ,    num_hidden_layers=self.num_hidden_layers   ,    num_attention_heads=self.num_attention_heads   ,    intermediate_size=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    hidden_dropout_prob=self.hidden_dropout_prob   ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    type_vocab_size=self.type_vocab_size   ,    initializer_range=self.initializer_range   ,    )



    def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[int]:
           __UpperCAmelCase    							=ErnieMModel(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    return_dict=__SCREAMING_SNAKE_CASE			)
           self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)			)



    def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       int:
           __UpperCAmelCase    							=ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=model(
               __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_SNAKE_CASE   ,    )
           self.parent.assertEqual(result.start_logits.shape   ,    (self.batch_size, self.seq_length)			)
           self.parent.assertEqual(result.end_logits.shape   ,    (self.batch_size, self.seq_length)			)



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Any:
           __UpperCAmelCase    							=ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=torch.ones_like(__SCREAMING_SNAKE_CASE   ,    dtype=torch.floataa			)
           __UpperCAmelCase    							=model(
               __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_SNAKE_CASE   ,    )
           self.parent.assertEqual(result.start_logits.shape   ,    (self.batch_size, self.seq_length)			)
           self.parent.assertEqual(result.end_logits.shape   ,    (self.batch_size, self.seq_length)			)



    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       str:
           __UpperCAmelCase    							=self.num_labels
           __UpperCAmelCase    							=ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
           self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)			)



    def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Dict:
           __UpperCAmelCase    							=self.num_labels
           __UpperCAmelCase    							=ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           input_ids.to(__SCREAMING_SNAKE_CASE			)
           input_mask.to(__SCREAMING_SNAKE_CASE			)
           token_labels.to(__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)

           self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.num_labels)			)



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       List[Any]:
           __UpperCAmelCase    							=self.num_choices
           __UpperCAmelCase    							=ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()
           __UpperCAmelCase    							=input_ids.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
           __UpperCAmelCase    							=input_mask.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
           __UpperCAmelCase    							=model(
               __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE   ,    )
           self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_choices)			)



    def _a       (							self			:    Any			)			->       List[str]:
           __UpperCAmelCase    							=self.prepare_config_and_inputs()
           (
               (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   (
               __UpperCAmelCase
           )							,   
           )    							=config_and_inputs
           __UpperCAmelCase    							={"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict




@require_torch
class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       Union[str, Any]   =   (
        (
            ErnieMModel,
            ErnieMForMultipleChoice,
            ErnieMForQuestionAnswering,
            ErnieMForSequenceClassification,
            ErnieMForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase  :       Optional[Any]   =   ()
    lowerCamelCase  :       str   =   (
        {
            'feature-extraction': ErnieMModel,
            'question-answering': ErnieMForQuestionAnswering,
            'text-classification': ErnieMForSequenceClassification,
            'token-classification': ErnieMForTokenClassification,
            'zero-shot': ErnieMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase  :       Optional[int]   =   False



    def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Optional[int]:
           if pipeline_test_casse_name == "QAPipelineTests":
                  return True

           return False



    def _a       (							self			:    Optional[int]			)			->       List[Any]:
           __UpperCAmelCase    							=ErnieMModelTester(self			)
           __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE   ,    hidden_size=37			)



    def _a       (							self			:    int			)			->       List[str]:
           self.config_tester.run_common_tests()



    def _a       (							self			:    str			)			->       str:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Optional[int]			)			->       List[str]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  __UpperCAmelCase    							=type
                  self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Optional[int]			)			->       str:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    List[str]			)			->       Union[str, Any]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Any			)			->       str:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    str			)			->       List[Any]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    List[Any]			)			->       List[str]:
           __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE			)



    @slow
    def _a       (							self			:    List[str]			)			->       Optional[Any]:
           for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __UpperCAmelCase    							=ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)




@require_torch
class 							_A (    unittest.TestCase							):
    """simple docstring"""

    @slow
    def _a       (							self			:    Tuple			)			->       Optional[Any]:
           __UpperCAmelCase    							=ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""			)
           model.eval()
           __UpperCAmelCase    							=torch.tensor([[0, 1, 2, 3, 4, 5]]			)
           __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)[0]

           # TODO Replace vocab size
           __UpperCAmelCase    							=768

           __UpperCAmelCase    							=torch.Size((1, 6, hidden_size)			)
           self.assertEqual(output.shape   ,    __SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=torch.tensor(
               [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]			)

           self.assertTrue(torch.allclose(output[:, :3, :3]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)




