


import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
        SpeechaTextaDecoder,
        SpeechaTextaForCausalLM,
    )







@require_torch
class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      List[str]=99 ,				__lowerCamelCase							:      List[Any]=13 ,				__lowerCamelCase							:      Dict=16 ,				__lowerCamelCase							:      List[str]=7 ,				__lowerCamelCase							:      Optional[Any]=True ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      Optional[int]=False ,				__lowerCamelCase							:      Tuple=True ,				__lowerCamelCase							:      Optional[int]=2 ,				__lowerCamelCase							:      Any=32 ,				__lowerCamelCase							:      Any=4 ,				__lowerCamelCase							:      Any=4 ,				__lowerCamelCase							:      str=30 ,				__lowerCamelCase							:      Tuple=0 ,				__lowerCamelCase							:      List[Any]=1 ,				__lowerCamelCase							:      Any=2 ,				__lowerCamelCase							:      Union[str, Any]=None ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  parent
             lowerCamelCase__		:    Optional[int]   =  batch_size
             lowerCamelCase__		:    Union[str, Any]   =  decoder_seq_length
             # For common tests
             lowerCamelCase__		:    Optional[Any]   =  self.decoder_seq_length
             lowerCamelCase__		:    int   =  is_training
             lowerCamelCase__		:    List[str]   =  use_attention_mask
             lowerCamelCase__		:    List[Any]   =  use_labels

             lowerCamelCase__		:    Dict   =  vocab_size
             lowerCamelCase__		:    str   =  d_model
             lowerCamelCase__		:    Optional[Any]   =  d_model
             lowerCamelCase__		:    Optional[Any]   =  decoder_layers
             lowerCamelCase__		:    Tuple   =  decoder_layers
             lowerCamelCase__		:    Optional[Any]   =  decoder_ffn_dim
             lowerCamelCase__		:    List[str]   =  decoder_attention_heads
             lowerCamelCase__		:    int   =  decoder_attention_heads
             lowerCamelCase__		:    Union[str, Any]   =  eos_token_id
             lowerCamelCase__		:    List[str]   =  bos_token_id
             lowerCamelCase__		:    str   =  pad_token_id
             lowerCamelCase__		:    str   =  decoder_start_token_id
             lowerCamelCase__		:    int   =  use_cache
             lowerCamelCase__		:    Tuple   =  max_position_embeddings

             lowerCamelCase__		:    List[str]   =  None
             lowerCamelCase__		:    Any   =  decoder_seq_length
             lowerCamelCase__		:    Any   =  2
             lowerCamelCase__		:    Tuple   =  1





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  ids_tensor([self.batch_size, self.decoder_seq_length] ,				self.vocab_size	)

             lowerCamelCase__		:    Tuple   =  None
             if self.use_attention_mask:
                   lowerCamelCase__		:    Optional[int]   =  ids_tensor([self.batch_size, self.decoder_seq_length] ,				vocab_size=2	)

             lowerCamelCase__		:    Union[str, Any]   =  None
             if self.use_labels:
                   lowerCamelCase__		:    Tuple   =  ids_tensor([self.batch_size, self.decoder_seq_length] ,				self.vocab_size	)

             lowerCamelCase__		:    str   =  SpeechaTextaConfig(
                 vocab_size=self.vocab_size ,				d_model=self.d_model ,				decoder_layers=self.decoder_layers ,				decoder_ffn_dim=self.decoder_ffn_dim ,				decoder_attention_heads=self.decoder_attention_heads ,				eos_token_id=self.eos_token_id ,				bos_token_id=self.bos_token_id ,				use_cache=self.use_cache ,				pad_token_id=self.pad_token_id ,				decoder_start_token_id=self.decoder_start_token_id ,				max_position_embeddings=self.max_position_embeddings ,				)

             return (
                 config,
                 input_ids,
                 attention_mask,
                 lm_labels,
             )





       def 						lowerCAmelCase					(       self							:      Dict ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      Optional[int] ,				__lowerCamelCase							:      Dict ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  True
             lowerCamelCase__		:    Optional[int]   =  SpeechaTextaDecoder(config=__lowerCamelCase	).to(__lowerCamelCase	).eval()
             lowerCamelCase__		:    Optional[int]   =  input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             lowerCamelCase__		:    List[str]   =  model(__lowerCamelCase ,				use_cache=__lowerCamelCase	)
             lowerCamelCase__		:    Any   =  model(__lowerCamelCase	)
             lowerCamelCase__		:    Any   =  model(__lowerCamelCase ,				use_cache=__lowerCamelCase	)

             self.parent.assertTrue(len(__lowerCamelCase	) == len(__lowerCamelCase	)	)
             self.parent.assertTrue(len(__lowerCamelCase	) == len(__lowerCamelCase	) + 1	)

             lowerCamelCase__		:    Any   =  outputs["past_key_values"]

             # create hypothetical next token and extent to next_input_ids
             lowerCamelCase__		:    Dict   =  ids_tensor((2, 1) ,				config.vocab_size - 1	) + 1

             # append to next input_ids and
             lowerCamelCase__		:    Optional[Any]   =  torch.cat([input_ids, next_tokens] ,				dim=-1	)

             print(__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  model(__lowerCamelCase	)["last_hidden_state"]
             lowerCamelCase__		:    Optional[Any]   =  model(__lowerCamelCase ,				past_key_values=__lowerCamelCase	)["last_hidden_state"]

             # select random slice
             lowerCamelCase__		:    Dict   =  ids_tensor((1,) ,				output_from_past.shape[-1]	).item()
             lowerCamelCase__		:    Tuple   =  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
             lowerCamelCase__		:    Any   =  output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             assert torch.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       
             )		:    Any   =  config_and_inputs

             lowerCamelCase__		:    Tuple   =  {
                 "input_ids": input_ids,
                 "attention_mask": attention_mask,
             }
             return config, inputs_dict







@require_torch
class       _lowercase			(							lowercase__							,  lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       A__ 						=      (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       A__ 						=      {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
       A__ 						=      True
       A__ 						=      False





       def 						lowerCAmelCase					(       self							:      List[Any] ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  SpeechaTextaStandaloneDecoderModelTester(self ,				is_training=__lowerCamelCase	)
             lowerCamelCase__		:    int   =  ConfigTester(self ,				config_class=__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             return






A				:   Union[str, Any]		= {
    "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 				lowercase_		(		_A      :   str						):

      """simple docstring"""




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

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[str]   =  ""
      lowerCamelCase__		:    Union[str, Any]   =  0
      lowerCamelCase__		:    Any   =  generate_table(_A						)
      for char in words.upper():
            cipher += get_opponent(table[count]   , _A						)
            lowerCamelCase__		:    Optional[int]   =  (count + 1) % len(_A						)
      return cipher

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      return encrypt(_A   , _A						)

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[Any]   =  0 if char in table[0] else 1
      lowerCamelCase__		:    List[Any]   =  table[row].index(_A						)
      return row, col

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__   ,       lowerCamelCase__		:    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				:   List[str]		= input("Enter key: ").strip()
    A				:   Tuple		= input("Enter text to encrypt: ").strip()
    A				:   Dict		= encrypt(key, text)

    print(f'Encrypted: {cipher_text}')
    print(f'Decrypted with key: {decrypt(key, cipher_text)}')


