'''simple docstring'''
a=	{
    '''A''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''B''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''C''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''D''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''E''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''F''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''G''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''H''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''I''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''J''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''K''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''L''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''M''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''N''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''O''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''P''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''Q''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''R''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''S''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''T''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''U''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''V''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''W''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''X''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''Y''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
    '''Z''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
}







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



    return [alphabet[char] for char in key.upper()]







def 			_UpperCamelCase			(  _a						:						str					,  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Dict		     =	''
    __UpperCamelCase						:						Optional[int]		     =	0
    __UpperCamelCase						:						Any		     =	generate_table(_a      )
    for char in words.upper():
        cipher += get_opponent(table[count]					,  _a      )
        __UpperCamelCase						:						Tuple		     =	(count + 1) % len(_a      )
    return cipher







def 			_UpperCamelCase			(  _a						:						str					,  _a						:						str      ):




    """simple docstring"""



    return encrypt(_a					,  _a      )







def 			_UpperCamelCase			(  _a						:						tuple[str, str]					,  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	0 if char in table[0] else 1
    __UpperCamelCase						:						str		     =	table[row].index(_a      )
    return row, col







def 			_UpperCamelCase			(  _a						:						tuple[str, str]					,  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	get_position(_a					,  char.upper()      )
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
  import doctest

  doctest.testmod()  # Fist ensure that all our tests are passing...
  a=	input('''Enter key: ''').strip()
  a=	input('''Enter text to encrypt: ''').strip()
  a=	encrypt(key, text)

  print(F"""Encrypted: {cipher_text}""")
  print(F"""Decrypted with key: {decrypt(key, cipher_text)}""")




'''simple docstring'''
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch






class       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_4  ,      _lowerCamelCase=7  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=False  ,      _lowerCamelCase=True  ,      _lowerCamelCase=9_9  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=5  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=0.0_2  ,      ):
         __UpperCamelCase						:						int		     =	parent
         __UpperCamelCase						:						Union[str, Any]		     =	batch_size
         __UpperCamelCase						:						Tuple		     =	seq_length
         __UpperCamelCase						:						Tuple		     =	is_training
         __UpperCamelCase						:						str		     =	use_input_mask
         __UpperCamelCase						:						int		     =	use_token_type_ids
         __UpperCamelCase						:						List[Any]		     =	use_labels
         __UpperCamelCase						:						Tuple		     =	vocab_size
         __UpperCamelCase						:						Any		     =	hidden_size
         __UpperCamelCase						:						int		     =	num_hidden_layers
         __UpperCamelCase						:						List[Any]		     =	num_attention_heads
         __UpperCamelCase						:						str		     =	intermediate_size
         __UpperCamelCase						:						Any		     =	hidden_act
         __UpperCamelCase						:						Tuple		     =	hidden_dropout_prob
         __UpperCamelCase						:						Any		     =	attention_probs_dropout_prob
         __UpperCamelCase						:						Union[str, Any]		     =	max_position_embeddings
         __UpperCamelCase						:						Optional[int]		     =	initializer_range
         __UpperCamelCase						:						str		     =	None
         __UpperCamelCase						:						Dict		     =	vocab_size - 1
         __UpperCamelCase						:						Tuple		     =	vocab_size - 1
         __UpperCamelCase						:						int		     =	vocab_size - 1







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size     )

         __UpperCamelCase						:						str		     =	None
         if self.use_input_mask:
             __UpperCamelCase						:						List[Any]		     =	random_attention_mask([self.batch_size, self.seq_length]     )

         __UpperCamelCase						:						Optional[Any]		     =	GPTaConfig(
             vocab_size=self.vocab_size  ,      n_embd=self.hidden_size  ,      n_layer=self.num_hidden_layers  ,      n_head=self.num_attention_heads  ,      n_positions=self.max_position_embeddings  ,      use_cache=_lowerCamelCase  ,      bos_token_id=self.bos_token_id  ,      eos_token_id=self.eos_token_id  ,      pad_token_id=self.pad_token_id  ,      )

         return (config, input_ids, input_mask)







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	self.prepare_config_and_inputs()
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	config_and_inputs
         __UpperCamelCase						:						Optional[Any]		     =	{'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.prepare_config_and_inputs()

         __UpperCamelCase						:						Optional[Any]		     =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
         __UpperCamelCase						:						List[Any]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2     )

         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	2_0
         __UpperCamelCase						:						List[Any]		     =	model_class_name(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	model.init_cache(input_ids.shape[0]  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Union[str, Any]		     =	jnp.ones((input_ids.shape[0], max_decoder_length)  ,      dtype='i4'     )

         __UpperCamelCase						:						List[str]		     =	jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )
         __UpperCamelCase						:						List[Any]		     =	model(
             input_ids[:, :-1]  ,      attention_mask=_lowerCamelCase  ,      past_key_values=_lowerCamelCase  ,      position_ids=_lowerCamelCase  ,      )

         __UpperCamelCase						:						int		     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,      dtype='i4'     )
         __UpperCamelCase						:						Tuple		     =	model(
             input_ids[:, -1:]  ,      attention_mask=_lowerCamelCase  ,      past_key_values=outputs_cache.past_key_values  ,      position_ids=_lowerCamelCase  ,      )

         __UpperCamelCase						:						List[str]		     =	model(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
         self.parent.assertTrue(diff < 1E-3  ,      msg=f"""Max diff is {diff}"""     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[Any]		     =	2_0
         __UpperCamelCase						:						Any		     =	model_class_name(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]  ,      axis=-1  ,      )

         __UpperCamelCase						:						Any		     =	model.init_cache(input_ids.shape[0]  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Optional[Any]		     =	jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )

         __UpperCamelCase						:						List[Any]		     =	model(
             input_ids[:, :-1]  ,      attention_mask=_lowerCamelCase  ,      past_key_values=_lowerCamelCase  ,      position_ids=_lowerCamelCase  ,      )
         __UpperCamelCase						:						Any		     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,      dtype='i4'     )
         __UpperCamelCase						:						Dict		     =	model(
             input_ids[:, -1:]  ,      past_key_values=outputs_cache.past_key_values  ,      attention_mask=_lowerCamelCase  ,      position_ids=_lowerCamelCase  ,      )

         __UpperCamelCase						:						Optional[Any]		     =	model(_lowerCamelCase  ,      attention_mask=_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
         self.parent.assertTrue(diff < 1E-3  ,      msg=f"""Max diff is {diff}"""     )






@require_flax
class       __lowercase		(    _lowerCamelCase      ,				_lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE__		 =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	FlaxGPTaModelTester(self     )







     def lowerCAmelCase  (		self     ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     )







     @slow
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	GPTaTokenizer.from_pretrained('gpt2'  ,      pad_token='</s>'  ,      padding_side='left'     )
         __UpperCamelCase						:						str		     =	tokenizer(['Hello this is a long string', 'Hey']  ,      return_tensors='np'  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase     )

         __UpperCamelCase						:						int		     =	FlaxGPTaLMHeadModel.from_pretrained('gpt2'     )
         __UpperCamelCase						:						Union[str, Any]		     =	False
         __UpperCamelCase						:						Optional[Any]		     =	model.config.eos_token_id

         __UpperCamelCase						:						List[str]		     =	jax.jit(model.generate     )

         __UpperCamelCase						:						List[str]		     =	jit_generate(inputs['input_ids']  ,      attention_mask=inputs['attention_mask']     ).sequences

         __UpperCamelCase						:						Optional[int]		     =	tokenizer.batch_decode(_lowerCamelCase  ,      skip_special_tokens=_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	[
             'Hello this is a long string of words. I\'m going to start with the first one.\n',
             'Hey, I\'m not sure if I\'m going to be able to do',
         ]

         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase     )







     @is_pt_flax_cross_test
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                 # prepare inputs
                 __UpperCamelCase						:						Tuple		     =	self._prepare_for_class(_lowerCamelCase  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						Dict		     =	{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 __UpperCamelCase						:						Union[str, Any]		     =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 __UpperCamelCase						:						List[str]		     =	getattr(_lowerCamelCase  ,      _lowerCamelCase     )

                 __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	pt_inputs['input_ids'].shape
                 __UpperCamelCase						:						int		     =	np.random.randint(0  ,      seq_length - 1  ,      size=(batch_size,)     )
                 for batch_idx, start_index in enumerate(_lowerCamelCase     ):
                     __UpperCamelCase						:						int		     =	0
                     __UpperCamelCase						:						Tuple		     =	1
                     __UpperCamelCase						:						Optional[int]		     =	0
                     __UpperCamelCase						:						List[Any]		     =	1
                 __UpperCamelCase						:						str		     =	pt_model_class(_lowerCamelCase     ).eval()
                 __UpperCamelCase						:						Union[str, Any]		     =	model_class(_lowerCamelCase  ,      dtype=jnp.floataa     )

                 __UpperCamelCase						:						Optional[int]		     =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						List[str]		     =	fx_state

                 with torch.no_grad():
                     __UpperCamelCase						:						List[Any]		     =	pt_model(**_lowerCamelCase     ).to_tuple()

                 __UpperCamelCase						:						str		     =	fx_model(**_lowerCamelCase     ).to_tuple()
                 self.assertEqual(len(_lowerCamelCase     )  ,      len(_lowerCamelCase     )  ,      'Output lengths differ between Flax and PyTorch'     )
                 for fx_output, pt_output in zip(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assert_almost_equals(fx_output[:, -1]  ,      pt_output[:, -1].numpy()  ,      4E-2     )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(_lowerCamelCase     )
                     __UpperCamelCase						:						Any		     =	model_class.from_pretrained(_lowerCamelCase  ,      from_pt=_lowerCamelCase     )

                 __UpperCamelCase						:						Union[str, Any]		     =	fx_model_loaded(**_lowerCamelCase     ).to_tuple()
                 self.assertEqual(
                     len(_lowerCamelCase     )  ,      len(_lowerCamelCase     )  ,      'Output lengths differ between Flax and PyTorch'     )
                 for fx_output_loaded, pt_output in zip(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assert_almost_equals(fx_output_loaded[:, -1]  ,      pt_output[:, -1].numpy()  ,      4E-2     )







     @is_pt_flax_cross_test
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                 # prepare inputs
                 __UpperCamelCase						:						Tuple		     =	self._prepare_for_class(_lowerCamelCase  ,      _lowerCamelCase     )
                 __UpperCamelCase						:						int		     =	{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 __UpperCamelCase						:						Optional[Any]		     =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 __UpperCamelCase						:						int		     =	getattr(_lowerCamelCase  ,      _lowerCamelCase     )

                 __UpperCamelCase						:						Optional[Any]		     =	pt_model_class(_lowerCamelCase     ).eval()
                 __UpperCamelCase						:						List[Any]		     =	model_class(_lowerCamelCase  ,      dtype=jnp.floataa     )

                 __UpperCamelCase						:						str		     =	load_flax_weights_in_pytorch_model(_lowerCamelCase  ,      fx_model.params     )
                 __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	pt_inputs['input_ids'].shape
                 __UpperCamelCase						:						Dict		     =	np.random.randint(0  ,      seq_length - 1  ,      size=(batch_size,)     )
                 for batch_idx, start_index in enumerate(_lowerCamelCase     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	0
                     __UpperCamelCase						:						List[str]		     =	1
                     __UpperCamelCase						:						Optional[int]		     =	0
                     __UpperCamelCase						:						Optional[int]		     =	1

                 # make sure weights are tied in PyTorch
                 pt_model.tie_weights()

                 with torch.no_grad():
                     __UpperCamelCase						:						Any		     =	pt_model(**_lowerCamelCase     ).to_tuple()

                 __UpperCamelCase						:						Optional[Any]		     =	fx_model(**_lowerCamelCase     ).to_tuple()
                 self.assertEqual(len(_lowerCamelCase     )  ,      len(_lowerCamelCase     )  ,      'Output lengths differ between Flax and PyTorch'     )
                 for fx_output, pt_output in zip(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assert_almost_equals(fx_output[:, -1]  ,      pt_output[:, -1].numpy()  ,      4E-2     )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(_lowerCamelCase     )
                     __UpperCamelCase						:						int		     =	pt_model_class.from_pretrained(_lowerCamelCase  ,      from_flax=_lowerCamelCase     )

                 with torch.no_grad():
                     __UpperCamelCase						:						Dict		     =	pt_model_loaded(**_lowerCamelCase     ).to_tuple()

                 self.assertEqual(
                     len(_lowerCamelCase     )  ,      len(_lowerCamelCase     )  ,      'Output lengths differ between Flax and PyTorch'     )
                 for fx_output, pt_output in zip(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assert_almost_equals(fx_output[:, -1]  ,      pt_output[:, -1].numpy()  ,      4E-2     )







     @slow
     def lowerCAmelCase  (		self     ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase						:						Union[str, Any]		     =	model_class_name.from_pretrained('gpt2'  ,      from_pt=_lowerCamelCase     )
             __UpperCamelCase						:						List[str]		     =	model(np.ones((1, 1)     )     )
             self.assertIsNotNone(_lowerCamelCase     )



