

import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.electra.modeling_flax_electra import (
     FlaxElectraForCausalLM,
     FlaxElectraForMaskedLM,
     FlaxElectraForMultipleChoice,
     FlaxElectraForPreTraining,
     FlaxElectraForQuestionAnswering,
     FlaxElectraForSequenceClassification,
     FlaxElectraForTokenClassification,
     FlaxElectraModel,
 )



class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def __init__(				self					:							Tuple      ,snake_case					:							str      ,snake_case					:							Optional[Any]=13      ,snake_case					:							List[str]=7      ,snake_case					:							Any=True      ,snake_case					:							str=True      ,snake_case					:							Tuple=True      ,snake_case					:							List[Any]=True      ,snake_case					:							Optional[int]=99      ,snake_case					:							Tuple=24      ,snake_case					:							Optional[int]=32      ,snake_case					:							Any=5      ,snake_case					:							str=4      ,snake_case					:							Optional[Any]=37      ,snake_case					:							Union[str, Any]="gelu"      ,snake_case					:							List[Any]=0.1      ,snake_case					:							Any=0.1      ,snake_case					:							List[str]=512      ,snake_case					:							Union[str, Any]=16      ,snake_case					:							Union[str, Any]=2      ,snake_case					:							List[Any]=0.02      ,snake_case					:							List[str]=4      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=seq_length
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=use_attention_mask
           SCREAMING_SNAKE_CASE										=use_token_type_ids
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=embedding_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=type_vocab_size
           SCREAMING_SNAKE_CASE										=type_sequence_label_size
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=num_choices
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

           SCREAMING_SNAKE_CASE										=None
           if self.use_attention_mask:
                SCREAMING_SNAKE_CASE										=random_attention_mask([self.batch_size, self.seq_length]    )

           SCREAMING_SNAKE_CASE										=None
           if self.use_token_type_ids:
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size    )

           SCREAMING_SNAKE_CASE										=ElectraConfig(
               vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_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      ,)

           return config, input_ids, token_type_ids, attention_mask


      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
           return config, inputs_dict







@require_flax
class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				True

      __UpperCAmelCase      		=				(
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=FlaxElectraModelTester(self    )


      @slow
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           for model_class_name in self.all_model_classes:
                if model_class_name == FlaxElectraForMaskedLM:
                     SCREAMING_SNAKE_CASE										=model_class_name.from_pretrained('google/electra-small-generator'    )
                else:
                     SCREAMING_SNAKE_CASE										=model_class_name.from_pretrained('google/electra-small-discriminator'    )
                SCREAMING_SNAKE_CASE										=model(np.ones((1, 1)    )    )
                self.assertIsNotNone(snake_case    )




import os
import sys
import time

from . import transposition_cipher as trans_cipher







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										='Prehistoric Men.txt'
     SCREAMING_SNAKE_CASE										='Output.txt'
     SCREAMING_SNAKE_CASE										=int(input('Enter key: '      )      )
     SCREAMING_SNAKE_CASE										=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]'      )
          SCREAMING_SNAKE_CASE										=input('> '      )
          if not response.lower().startswith('y'      ):
               sys.exit()

     SCREAMING_SNAKE_CASE										=time.time()
     if mode.lower().startswith('e'      ):
          with open(lowerCAmelCase_      ) as f:
               SCREAMING_SNAKE_CASE										=f.read()
          SCREAMING_SNAKE_CASE										=trans_cipher.encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif mode.lower().startswith('d'      ):
          with open(lowerCAmelCase_      ) as f:
               SCREAMING_SNAKE_CASE										=f.read()
          SCREAMING_SNAKE_CASE										=trans_cipher.decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     with open(lowerCAmelCase_,   'w'      ) as output_obj:
          output_obj.write(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=round(time.time() - start_time,   2      )
     print(('Done (', total_time, 'seconds )')      )


if __name__ == "__main__":
 main()

