'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 snake_case  =			'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import MarianTokenizer
 from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							int  ,  _lowerCamelCase			:							Dict=None  ,  _lowerCamelCase			:							Dict=None  ,  _lowerCamelCase			:							Any=None  ,  _lowerCamelCase			:							int=None  ,  _lowerCamelCase			:							Any=None  ,  _lowerCamelCase			:							List[Any]=None  ,  ):
      if attention_mask is None:
            _lowerCAmelCase		   =   np.where(input_ids != config.pad_token_id  ,  1  ,  0    )
      if decoder_attention_mask is None:
            _lowerCAmelCase		   =   np.where(decoder_input_ids != config.pad_token_id  ,  1  ,  0    )
      if head_mask is None:
            _lowerCAmelCase		   =   np.ones((config.encoder_layers, config.encoder_attention_heads)    )
      if decoder_head_mask is None:
            _lowerCAmelCase		   =   np.ones((config.decoder_layers, config.decoder_attention_heads)    )
      if cross_attn_head_mask is None:
            _lowerCAmelCase		   =   np.ones((config.decoder_layers, config.decoder_attention_heads)    )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }





class        SCREAMING_SNAKE_CASE   :
  """simple docstring"""







  def __init__(  self   :					Union[str, Any]					,					__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					Optional[int]=13					,					__lowerCAmelCase   :					List[Any]=7					,					__lowerCAmelCase   :					Dict=True					,					__lowerCAmelCase   :					Optional[Any]=False					,					__lowerCAmelCase   :					List[str]=99					,					__lowerCAmelCase   :					List[str]=16					,					__lowerCAmelCase   :					List[str]=2					,					__lowerCAmelCase   :					Union[str, Any]=4					,					__lowerCAmelCase   :					Optional[Any]=4					,					__lowerCAmelCase   :					Optional[int]="gelu"					,					__lowerCAmelCase   :					List[str]=0.1					,					__lowerCAmelCase   :					Dict=0.1					,					__lowerCAmelCase   :					Union[str, Any]=32					,					__lowerCAmelCase   :					Tuple=2					,					__lowerCAmelCase   :					List[str]=1					,					__lowerCAmelCase   :					int=0					,					__lowerCAmelCase   :					Union[str, Any]=0.0_2					,					):


        """simple docstring"""



        _lowerCAmelCase		   =   parent
        _lowerCAmelCase		   =   batch_size
        _lowerCAmelCase		   =   seq_length
        _lowerCAmelCase		   =   is_training
        _lowerCAmelCase		   =   use_labels
        _lowerCAmelCase		   =   vocab_size
        _lowerCAmelCase		   =   hidden_size
        _lowerCAmelCase		   =   num_hidden_layers
        _lowerCAmelCase		   =   num_attention_heads
        _lowerCAmelCase		   =   intermediate_size
        _lowerCAmelCase		   =   hidden_act
        _lowerCAmelCase		   =   hidden_dropout_prob
        _lowerCAmelCase		   =   attention_probs_dropout_prob
        _lowerCAmelCase		   =   max_position_embeddings
        _lowerCAmelCase		   =   eos_token_id
        _lowerCAmelCase		   =   pad_token_id
        _lowerCAmelCase		   =   bos_token_id
        _lowerCAmelCase		   =   initializer_range



  def   a   (  self   :					Optional[int]					):


        """simple docstring"""



        _lowerCAmelCase		   =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,					self.vocab_size					)					,					3					,					self.vocab_size					)
        _lowerCAmelCase		   =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,					dtype=np.intaa					))					,					-1					)

        _lowerCAmelCase		   =   shift_tokens_right(__lowerCAmelCase					,					1					,					2					)

        _lowerCAmelCase		   =   MarianConfig(
            vocab_size=self.vocab_size					,					d_model=self.hidden_size					,					encoder_layers=self.num_hidden_layers					,					decoder_layers=self.num_hidden_layers					,					encoder_attention_heads=self.num_attention_heads					,					decoder_attention_heads=self.num_attention_heads					,					encoder_ffn_dim=self.intermediate_size					,					decoder_ffn_dim=self.intermediate_size					,					dropout=self.hidden_dropout_prob					,					attention_dropout=self.attention_probs_dropout_prob					,					max_position_embeddings=self.max_position_embeddings					,					eos_token_id=self.eos_token_id					,					bos_token_id=self.bos_token_id					,					pad_token_id=self.pad_token_id					,					initializer_range=self.initializer_range					,					use_cache=__lowerCAmelCase					,					)
        _lowerCAmelCase		   =   prepare_marian_inputs_dict(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase					)
        return config, inputs_dict



  def   a   (  self   :					Dict					):


        """simple docstring"""



        _lowerCAmelCase   ,  _lowerCAmelCase		   =   self.prepare_config_and_inputs()
        return config, inputs_dict



  def   a   (  self   :					List[str]					,					__lowerCAmelCase   :					str					,					__lowerCAmelCase   :					Optional[Any]					,					__lowerCAmelCase   :					Tuple					):


        """simple docstring"""



        _lowerCAmelCase		   =   20
        _lowerCAmelCase		   =   model_class_name(__lowerCAmelCase					)

        _lowerCAmelCase		   =   model.encode(inputs_dict['input_ids']					)

        _lowerCAmelCase   ,  _lowerCAmelCase		   =   (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        _lowerCAmelCase		   =   model.init_cache(decoder_input_ids.shape[0]					,					__lowerCAmelCase					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,					dtype='i4'					)

        _lowerCAmelCase		   =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]					,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,					)
        _lowerCAmelCase		   =   model.decode(
            decoder_input_ids[:, :-1]					,					__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					,					past_key_values=__lowerCAmelCase					,					decoder_position_ids=__lowerCAmelCase					,					)

        _lowerCAmelCase		   =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,					dtype='i4'					)
        _lowerCAmelCase		   =   model.decode(
            decoder_input_ids[:, -1:]					,					__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					,					past_key_values=outputs_cache.past_key_values					,					decoder_position_ids=__lowerCAmelCase					,					)

        _lowerCAmelCase		   =   model.decode(__lowerCAmelCase					,					__lowerCAmelCase					)

        _lowerCAmelCase		   =   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   a   (  self   :					List[Any]					,					__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					Union[str, Any]					,					__lowerCAmelCase   :					Any					):


        """simple docstring"""



        _lowerCAmelCase		   =   20
        _lowerCAmelCase		   =   model_class_name(__lowerCAmelCase					)

        _lowerCAmelCase		   =   model.encode(inputs_dict['input_ids']					)

        _lowerCAmelCase   ,  _lowerCAmelCase		   =   (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        _lowerCAmelCase		   =   jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
            ]					,					axis=-1					,					)

        _lowerCAmelCase		   =   model.init_cache(decoder_input_ids.shape[0]					,					__lowerCAmelCase					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]					,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,					)

        _lowerCAmelCase		   =   model.decode(
            decoder_input_ids[:, :-1]					,					__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					,					past_key_values=__lowerCAmelCase					,					decoder_position_ids=__lowerCAmelCase					,					)
        _lowerCAmelCase		   =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,					dtype='i4'					)
        _lowerCAmelCase		   =   model.decode(
            decoder_input_ids[:, -1:]					,					__lowerCAmelCase					,					past_key_values=outputs_cache.past_key_values					,					decoder_attention_mask=__lowerCAmelCase					,					decoder_position_ids=__lowerCAmelCase					,					)

        _lowerCAmelCase		   =   model.decode(__lowerCAmelCase					,					__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					)

        _lowerCAmelCase		   =   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        SCREAMING_SNAKE_CASE   (				__a	,						unittest.TestCase	,						__a ):
  """simple docstring"""







  __A     =		True
  __A     =		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  __A     =		(FlaxMarianMTModel,) if is_flax_available() else ()



  def   a   (  self   :					Optional[int]					):


        """simple docstring"""



        _lowerCAmelCase		   =   FlaxMarianModelTester(self					)



  def   a   (  self   :					str					):


        """simple docstring"""



        _lowerCAmelCase   ,  _lowerCAmelCase		   =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase					)



  def   a   (  self   :					Union[str, Any]					):


        """simple docstring"""



        _lowerCAmelCase   ,  _lowerCAmelCase		   =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase					)



  def   a   (  self   :					Optional[int]					):


        """simple docstring"""



        _lowerCAmelCase   ,  _lowerCAmelCase		   =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__					):
                    _lowerCAmelCase		   =   self._prepare_for_class(__lowerCAmelCase					,					__lowerCAmelCase					)
                    _lowerCAmelCase		   =   model_class(__lowerCAmelCase					)

                    @jax.jit
                    def encode_jitted(__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					List[Any]=None					,					**__lowerCAmelCase   :					Optional[int]					):
                          return model.encode(input_ids=__lowerCAmelCase					,					attention_mask=__lowerCAmelCase					)

                    with self.subTest('JIT Enabled'					):
                          _lowerCAmelCase		   =   encode_jitted(**__lowerCAmelCase					).to_tuple()

                    with self.subTest('JIT Disabled'					):
                          with jax.disable_jit():
                                _lowerCAmelCase		   =   encode_jitted(**__lowerCAmelCase					).to_tuple()

                    self.assertEqual(len(__lowerCAmelCase					)					,					len(__lowerCAmelCase					)					)
                    for jitted_output, output in zip(__lowerCAmelCase					,					__lowerCAmelCase					):
                          self.assertEqual(jitted_output.shape					,					output.shape					)



  def   a   (  self   :					Optional[Any]					):


        """simple docstring"""



        _lowerCAmelCase   ,  _lowerCAmelCase		   =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__					):
                    _lowerCAmelCase		   =   model_class(__lowerCAmelCase					)
                    _lowerCAmelCase		   =   model.encode(inputs_dict['input_ids']					,					inputs_dict['attention_mask']					)

                    _lowerCAmelCase		   =   {
                        'decoder_input_ids': inputs_dict['decoder_input_ids'],
                        'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                        'encoder_outputs': encoder_outputs,
                    }

                    @jax.jit
                    def decode_jitted(__lowerCAmelCase   :					Dict					,					__lowerCAmelCase   :					List[Any]					,					__lowerCAmelCase   :					Union[str, Any]					):
                          return model.decode(
                              decoder_input_ids=__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					,					encoder_outputs=__lowerCAmelCase					,					)

                    with self.subTest('JIT Enabled'					):
                          _lowerCAmelCase		   =   decode_jitted(**__lowerCAmelCase					).to_tuple()

                    with self.subTest('JIT Disabled'					):
                          with jax.disable_jit():
                                _lowerCAmelCase		   =   decode_jitted(**__lowerCAmelCase					).to_tuple()

                    self.assertEqual(len(__lowerCAmelCase					)					,					len(__lowerCAmelCase					)					)
                    for jitted_output, output in zip(__lowerCAmelCase					,					__lowerCAmelCase					):
                          self.assertEqual(jitted_output.shape					,					output.shape					)



  @slow
  def   a   (  self   :					Union[str, Any]					):


        """simple docstring"""



        for model_class_name in self.all_model_classes:
              _lowerCAmelCase		   =   model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'					)
              # FlaxMarianForSequenceClassification expects eos token in input_ids
              _lowerCAmelCase		   =   np.ones((1, 1)					) * model.config.eos_token_id
              _lowerCAmelCase		   =   model(__lowerCAmelCase					)
              self.assertIsNotNone(__lowerCAmelCase					)





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				unittest.TestCase ):
  """simple docstring"""







  __A     =		None
  __A     =		None



  @classmethod
  def   a   (  cls   :					Union[str, Any]					):


        """simple docstring"""



        _lowerCAmelCase		   =   F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
        return cls



  @cached_property
  def   a   (  self   :					Dict					):


        """simple docstring"""



        return MarianTokenizer.from_pretrained(self.model_name					)



  @property
  def   a   (  self   :					List[str]					):


        """simple docstring"""



        return self.tokenizer.eos_token_id



  @cached_property
  def   a   (  self   :					Dict					):


        """simple docstring"""



        _lowerCAmelCase		   =   FlaxMarianMTModel.from_pretrained(self.model_name					)
        self.assertEqual(model.config.decoder_start_token_id					,					model.config.pad_token_id					)
        return model



  def   a   (  self   :					Optional[int]					,					**__lowerCAmelCase   :					Tuple					):


        """simple docstring"""



        _lowerCAmelCase		   =   self.translate_src_text(**__lowerCAmelCase					)
        self.assertListEqual(self.expected_text					,					__lowerCAmelCase					)



  def   a   (  self   :					int					,					**__lowerCAmelCase   :					int					):


        """simple docstring"""



        _lowerCAmelCase		   =   self.tokenizer(self.src_text					,					padding=__lowerCAmelCase					,					return_tensors='np'					,					**__lowerCAmelCase					)
        _lowerCAmelCase		   =   self.model.generate(
            model_inputs.input_ids					,					attention_mask=model_inputs.attention_mask					,					num_beams=2					,					max_length=128					,					).sequences
        _lowerCAmelCase		   =   self.tokenizer.batch_decode(__lowerCAmelCase					,					skip_special_tokens=__lowerCAmelCase					)
        return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"en"
  __A     =		"fr"
  __A     =		[
      "I am a small frog.",
      "Now I can forget the 100 words of german that I know.",
  ]
  __A     =		[
      "Je suis une petite grenouille.",
      "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
  ]



  @slow
  def   a   (  self   :					int					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"fr"
  __A     =		"en"
  __A     =		[
      "Donnez moi le micro.",
      "Tom et Mary étaient assis à une table.",  # Accents
  ]
  __A     =		[
      "Give me the microphone.",
      "Tom and Mary were sitting at a table.",
  ]



  @slow
  def   a   (  self   :					Optional[Any]					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"mt"
  __A     =		"en"
  __A     =		["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
  __A     =		["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]



  @slow
  def   a   (  self   :					Dict					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"en"
  __A     =		"de"
  __A     =		[
      "I am a small frog.",
      "Now I can forget the 100 words of german that I know.",
      "Tom asked his teacher for advice.",
      "That's how I would do it.",
      "Tom really admired Mary's courage.",
      "Turn around and close your eyes.",
  ]
  __A     =		[
      "Ich bin ein kleiner Frosch.",
      "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
      "Tom bat seinen Lehrer um Rat.",
      "So würde ich das machen.",
      "Tom bewunderte Marias Mut wirklich.",
      "Drehen Sie sich um und schließen Sie die Augen.",
  ]



  @slow
  def   a   (  self   :					Tuple					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"en"
  __A     =		"zh"
  __A     =		["My name is Wolfgang and I live in Berlin"]
  __A     =		["我叫沃尔夫冈 我住在柏林"]



  @slow
  def   a   (  self   :					List[Any]					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"ru"
  __A     =		"fr"
  __A     =		["Он показал мне рукопись своей новой пьесы."]
  __A     =		["Il m'a montré le manuscrit de sa nouvelle pièce."]



  @slow
  def   a   (  self   :					Union[str, Any]					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"en"
  __A     =		"ROMANCE"
  __A     =		[
      ">>fr<< Don't spend so much time watching TV.",
      ">>pt<< Your message has been sent.",
      ">>es<< He's two years older than me.",
  ]
  __A     =		[
      "Ne passez pas autant de temps à regarder la télé.",
      "A sua mensagem foi enviada.",
      "Es dos años más viejo que yo.",
  ]



  @slow
  def   a   (  self   :					Optional[Any]					):


        """simple docstring"""



        self._assert_generated_batch_equal_expected()






'''simple docstring'''




from __future__ import annotations







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', '', ''
      _lowerCAmelCase		   =   []

      for character in message_part:
            tmp.append(character_to_number[character]    )

      for each in tmp:
            one += each[0]
            two += each[1]
            three += each[2]

      return one + two + three







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''
      _lowerCAmelCase		   =   []

      for character in message_part:
            this_part += character_to_number[character]

      for digit in this_part:
            tmp += digit
            if len(_lowerCamelCase    ) == len(_lowerCamelCase    ):
                  result.append(_lowerCamelCase    )
                  _lowerCAmelCase		   =   ''

      return result[0], result[1], result[2]







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):
      # Validate message and alphabet, set to upper and remove spaces
      _lowerCAmelCase		   =   alphabet.replace(' '  ,  ''    ).upper()
      _lowerCAmelCase		   =   message.replace(' '  ,  ''    ).upper()

      # Check length and characters
      if len(_lowerCamelCase    ) != 27:
            raise KeyError('Length of alphabet has to be 27.'    )
      for each in message:
            if each not in alphabet:
                  raise ValueError('Each message character has to be included in alphabet!'    )

    # Generate dictionares
      _lowerCAmelCase		   =   (
          '111',
          '112',
          '113',
          '121',
          '122',
          '123',
          '131',
          '132',
          '133',
          '211',
          '212',
          '213',
          '221',
          '222',
          '223',
          '231',
          '232',
          '233',
          '311',
          '312',
          '313',
          '321',
          '322',
          '323',
          '331',
          '332',
          '333',
      )
      _lowerCAmelCase		   =   {}
      _lowerCAmelCase		   =   {}
      for letter, number in zip(_lowerCamelCase  ,  _lowerCamelCase    ):
            _lowerCAmelCase		   =   number
            _lowerCAmelCase		   =   letter

      return message, alphabet, character_to_number, number_to_character







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''

      for i in range(0  ,  len(_lowerCamelCase    ) + 1  ,  _lowerCamelCase    ):
            encrypted_numeric += __encrypt_part(
                message[i : i + period]  ,  _lowerCamelCase    )

      for i in range(0  ,  len(_lowerCamelCase    )  ,  3    ):
            encrypted += number_to_character[encrypted_numeric[i : i + 3]]

      return encrypted







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase		   =   []
      _lowerCAmelCase		   =   ''

      for i in range(0  ,  len(_lowerCamelCase    ) + 1  ,  _lowerCamelCase    ):
            _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __decrypt_part(message[i : i + period]  ,  _lowerCamelCase    )

            for j in range(0  ,  len(_lowerCamelCase    )    ):
                  decrypted_numeric.append(a[j] + b[j] + c[j]    )

      for each in decrypted_numeric:
            decrypted += number_to_character[each]

      return decrypted


if __name__ == "__main__":
 snake_case  =			'''DEFEND THE EAST WALL OF THE CASTLE.'''
 snake_case  =			encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 snake_case  =			decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')





