
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
					UpperCamelCase_  =	"platform"

					import jax
					import jax.numpy as jnp

					from transformers import AutoTokenizer
					from transformers.models.mbart.modeling_flax_mbart import (
					    FlaxMBartForConditionalGeneration,
					    FlaxMBartForQuestionAnswering,
					    FlaxMBartForSequenceClassification,
					    FlaxMBartModel,
					    shift_tokens_right,
					)





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  Union[str, Any]				,	lowerCamelCase_:  Any=None				,	lowerCamelCase_:  str=None				,	lowerCamelCase_:  Tuple=None				,	lowerCamelCase_:  Optional[Any]=None				,	lowerCamelCase_:  List[Any]=None				,	lowerCamelCase_:  Optional[int]=None				,	):



				'''simple docstring'''







				if attention_mask is None:
								A     :  List[Any]   =				np.where(input_ids != config.pad_token_id				,	1				,	0		)
				if decoder_attention_mask is None:
								A     :  int   =				np.where(decoder_input_ids != config.pad_token_id				,	1				,	0		)
				if head_mask is None:
								A     :  Optional[int]   =				np.ones((config.encoder_layers, config.encoder_attention_heads)		)
				if decoder_head_mask is None:
								A     :  List[Any]   =				np.ones((config.decoder_layers, config.decoder_attention_heads)		)
				if cross_attn_head_mask is None:
								A     :  Optional[Any]   =				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": decoder_attention_mask,
				}

class     _SCREAMING_SNAKE_CASE							(		unittest.TestCase							):





				def __init__(		self :		Any					,      snake_case_ :		Tuple					,      snake_case_ :		Tuple=13					,      snake_case_ :		int=7					,      snake_case_ :		Optional[int]=True					,      snake_case_ :		Optional[Any]=False					,      snake_case_ :		Optional[int]=99					,      snake_case_ :		List[Any]=16					,      snake_case_ :		Tuple=2					,      snake_case_ :		List[Any]=4					,      snake_case_ :		str=4					,      snake_case_ :		List[Any]="gelu"					,      snake_case_ :		Optional[Any]=0.1					,      snake_case_ :		Any=0.1					,      snake_case_ :		int=32					,      snake_case_ :		List[Any]=2					,      snake_case_ :		Union[str, Any]=1					,      snake_case_ :		Optional[int]=0					,      snake_case_ :		Dict=2					,      snake_case_ :		int=0.02					,      ):


								"""simple docstring"""

								A     :  Union[str, Any]   =				parent
								A     :  Dict   =				batch_size
								A     :  Optional[int]   =				seq_length
								A     :  int   =				is_training
								A     :  Any   =				use_labels
								A     :  Tuple   =				vocab_size
								A     :  List[str]   =				hidden_size
								A     :  Optional[Any]   =				num_hidden_layers
								A     :  Any   =				num_attention_heads
								A     :  List[str]   =				intermediate_size
								A     :  Tuple   =				hidden_act
								A     :  Dict   =				hidden_dropout_prob
								A     :  str   =				attention_probs_dropout_prob
								A     :  int   =				max_position_embeddings
								A     :  Tuple   =				eos_token_id
								A     :  List[Any]   =				pad_token_id
								A     :  Tuple   =				bos_token_id
								A     :  str   =				decoder_start_token_id
								A     :  Dict   =				initializer_range





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

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

								A     :  int   =				shift_tokens_right(snake_case_					,      1							)

								A     :  Union[str, Any]   =				MBartConfig(
								    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					,      decoder_start_token_id=self.decoder_start_token_id					,      initializer_range=self.initializer_range					,      use_cache=snake_case_					,      )
								A     :  List[Any]   =				prepare_mbart_inputs_dict(snake_case_					,      snake_case_					,      snake_case_							)
								return config, inputs_dict





				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								A ,       A     :  int   =				self.prepare_config_and_inputs()
								return config, inputs_dict





				def        _UpperCAmelCase						(		self :		Union[str, Any]					,      snake_case_ :		Tuple					,      snake_case_ :		int					,      snake_case_ :		Dict							):


								"""simple docstring"""

								A     :  List[str]   =				20
								A     :  Any   =				model_class_name(snake_case_							)

								A     :  Dict   =				model.encode(inputs_dict['''input_ids''']							)

								A ,       A     :  Union[str, Any]   =				(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								A     :  int   =				model.init_cache(decoder_input_ids.shape[0]					,      snake_case_					,      snake_case_							)
								A     :  List[Any]   =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,      dtype='''i4'''							)

								A     :  Dict   =				jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )
								A     :  List[str]   =				model.decode(
								    decoder_input_ids[:, :-1]					,      snake_case_					,      decoder_attention_mask=snake_case_					,      past_key_values=snake_case_					,      decoder_position_ids=snake_case_					,      )

								A     :  Union[str, Any]   =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype='''i4'''							)
								A     :  Optional[int]   =				model.decode(
								    decoder_input_ids[:, -1:]					,      snake_case_					,      decoder_attention_mask=snake_case_					,      past_key_values=outputs_cache.past_key_values					,      decoder_position_ids=snake_case_					,      )

								A     :  Dict   =				model.decode(snake_case_					,      snake_case_							)

								A     :  List[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}"""							)






				def        _UpperCAmelCase						(		self :		Optional[Any]					,      snake_case_ :		int					,      snake_case_ :		Any					,      snake_case_ :		Optional[Any]							):


								"""simple docstring"""

								A     :  Dict   =				20
								A     :  Any   =				model_class_name(snake_case_							)

								A     :  Optional[Any]   =				model.encode(inputs_dict['''input_ids''']							)

								A ,       A     :  Dict   =				(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

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

								A     :  List[Any]   =				model.init_cache(decoder_input_ids.shape[0]					,      snake_case_					,      snake_case_							)
								A     :  Union[str, Any]   =				jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )

								A     :  List[str]   =				model.decode(
								    decoder_input_ids[:, :-1]					,      snake_case_					,      decoder_attention_mask=snake_case_					,      past_key_values=snake_case_					,      decoder_position_ids=snake_case_					,      )
								A     :  List[str]   =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype='''i4'''							)
								A     :  Any   =				model.decode(
								    decoder_input_ids[:, -1:]					,      snake_case_					,      past_key_values=outputs_cache.past_key_values					,      decoder_attention_mask=snake_case_					,      decoder_position_ids=snake_case_					,      )

								A     :  List[Any]   =				model.decode(snake_case_					,      snake_case_					,      decoder_attention_mask=snake_case_							)

								A     :  List[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     _SCREAMING_SNAKE_CASE							(		unittest.TestCase							):
				lowerCamelCase_       =     9_9





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								A     :  str   =				np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ]					,      dtype=np.intaa					,      )

								A     :  str   =				input_ids.shape[0]
								A     :  int   =				MBartConfig(
								    vocab_size=self.vocab_size					,      d_model=24					,      encoder_layers=2					,      decoder_layers=2					,      encoder_attention_heads=2					,      decoder_attention_heads=2					,      encoder_ffn_dim=32					,      decoder_ffn_dim=32					,      max_position_embeddings=48					,      eos_token_id=2					,      pad_token_id=1					,      bos_token_id=0					,      )
								return config, input_ids, batch_size





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A ,       A ,       A     :  Union[str, Any]   =				self._get_config_and_data()
								A     :  Tuple   =				FlaxMBartForSequenceClassification(snake_case_							)
								A     :  List[Any]   =				model(input_ids=snake_case_					,      decoder_input_ids=snake_case_							)
								A     :  Union[str, Any]   =				(batch_size, config.num_labels)
								self.assertEqual(outputs['''logits'''].shape					,      snake_case_							)





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A ,       A ,       A     :  str   =				self._get_config_and_data()
								A     :  Any   =				FlaxMBartForQuestionAnswering(snake_case_							)
								A     :  Any   =				model(input_ids=snake_case_							)

								self.assertEqual(outputs['''start_logits'''].shape					,      input_ids.shape							)
								self.assertEqual(outputs['''end_logits'''].shape					,      input_ids.shape							)





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								A ,       A ,       A     :  Any   =				self._get_config_and_data()
								A     :  Any   =				FlaxMBartForConditionalGeneration(snake_case_							)
								A     :  str   =				lm_model(input_ids=snake_case_							)
								A     :  Optional[Any]   =				(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape					,      snake_case_							)





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  List[str]   =				MBartConfig(
								    vocab_size=self.vocab_size					,      d_model=14					,      encoder_layers=2					,      decoder_layers=2					,      encoder_attention_heads=2					,      decoder_attention_heads=2					,      encoder_ffn_dim=8					,      decoder_ffn_dim=8					,      max_position_embeddings=48					,      )
								A     :  Optional[int]   =				FlaxMBartForConditionalGeneration(snake_case_							)
								A     :  Union[str, Any]   =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,      dtype=np.intaa							)
								A     :  List[Any]   =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,      dtype=np.intaa							)
								A     :  Optional[Any]   =				lm_model(input_ids=snake_case_					,      decoder_input_ids=snake_case_							)
								A     :  Dict   =				(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape					,      snake_case_							)






				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  int   =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,      dtype=np.intaa							)
								A     :  Tuple   =				shift_tokens_right(snake_case_					,      1							)
								A     :  Tuple   =				np.equal(snake_case_					,      1							).astype(np.floataa							).sum()
								A     :  Dict   =				np.equal(snake_case_					,      1							).astype(np.floataa							).sum()
								self.assertEqual(shifted.shape					,      input_ids.shape							)
								self.assertEqual(snake_case_					,      n_pad_before - 1							)
								self.assertTrue(np.equal(shifted[:, 0]					,      2							).all()							)



@require_flax
class     _SCREAMING_SNAKE_CASE							(		snake_case,    unittest.TestCase,    snake_case							):
				lowerCamelCase_       =     True
				lowerCamelCase_       =     (
				    (
				        FlaxMBartModel,
				        FlaxMBartForConditionalGeneration,
				        FlaxMBartForSequenceClassification,
				        FlaxMBartForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)
				lowerCamelCase_       =     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								A     :  List[str]   =				FlaxMBartModelTester(self							)





				def        _UpperCAmelCase						(		self :		Optional[int]							):


								"""simple docstring"""

								A ,       A     :  int   =				self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(snake_case_					,      snake_case_					,      snake_case_							)





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A ,       A     :  List[str]   =				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(snake_case_					,      snake_case_					,      snake_case_							)





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A ,       A     :  List[Any]   =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__							):
																A     :  Optional[int]   =				self._prepare_for_class(snake_case_					,      snake_case_							)
																A     :  Union[str, Any]   =				model_class(snake_case_							)

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

																with self.subTest('''JIT Enabled'''							):
																				A     :  Optional[int]   =				encode_jitted(**snake_case_							).to_tuple()

																with self.subTest('''JIT Disabled'''							):
																				with jax.disable_jit():
																								A     :  Any   =				encode_jitted(**snake_case_							).to_tuple()

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





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


								"""simple docstring"""

								A ,       A     :  Union[str, Any]   =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__							):
																A     :  Tuple   =				model_class(snake_case_							)
																A     :  List[Any]   =				model.encode(inputs_dict['''input_ids''']					,      inputs_dict['''attention_mask''']							)

																A     :  Optional[int]   =				{
																    '''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(snake_case_ :		int					,      snake_case_ :		Dict					,      snake_case_ :		Dict							):
																				return model.decode(
																				    decoder_input_ids=snake_case_					,      decoder_attention_mask=snake_case_					,      encoder_outputs=snake_case_					,      )

																with self.subTest('''JIT Enabled'''							):
																				A     :  Any   =				decode_jitted(**snake_case_							).to_tuple()

																with self.subTest('''JIT Disabled'''							):
																				with jax.disable_jit():
																								A     :  str   =				decode_jitted(**snake_case_							).to_tuple()

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






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


								"""simple docstring"""

								for model_class_name in self.all_model_classes:
												A     :  List[str]   =				model_class_name.from_pretrained('''facebook/mbart-large-cc25'''					,      from_pt=snake_case_							)
												# FlaxMBartForSequenceClassification expects eos token in input_ids
												A     :  List[Any]   =				np.ones((1, 1)							) * model.config.eos_token_id
												A     :  Optional[int]   =				model(snake_case_							)
												self.assertIsNotNone(snake_case_							)



@require_flax
@require_sentencepiece
@require_tokenizers
class     _SCREAMING_SNAKE_CASE							(		unittest.TestCase							):
				lowerCamelCase_       =     [
				    ' UN Chief Says There Is No Military Solution in Syria',
				]
				lowerCamelCase_       =     [
				    'Şeful ONU declară că nu există o soluţie militară în Siria',
				]
				lowerCamelCase_       =     'facebook/mbart-large-en-ro'





				@cached_property
				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								return AutoTokenizer.from_pretrained(self.model_name							)





				@cached_property
				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								A     :  List[Any]   =				FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					,      from_pt=snake_case_							)
								return model





				def        _UpperCAmelCase						(		self :		Optional[Any]					,      **snake_case_ :		Union[str, Any]							):


								"""simple docstring"""

								A     :  str   =				self.translate_src_text(**snake_case_							)
								self.assertListEqual(self.expected_text					,      snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      **snake_case_ :		Tuple							):


								"""simple docstring"""

								A     :  int   =				self.tokenizer(self.src_text					,      **snake_case_					,      return_tensors='''np'''							)
								A     :  Tuple   =				self.model.generate(
								    model_inputs.input_ids					,      attention_mask=model_inputs.attention_mask					,      decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']					,      early_stopping=snake_case_					,      num_beams=2					,      ).sequences
								A     :  Union[str, Any]   =				self.tokenizer.batch_decode(snake_case_					,      skip_special_tokens=snake_case_							)
								return generated_words






				@slow
				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								self._assert_generated_batch_equal_expected()

from __future__ import annotations





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  dict[str, str]		):



				'''simple docstring'''







				A ,       A ,       A     :  str   =				'''''', '''''', ''''''
				A     :  Union[str, Any]   =				[]

				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 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  dict[str, str]		):



				'''simple docstring'''







				A ,       A     :  Tuple   =				'''''', ''''''
				A     :  str   =				[]

				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_		)
												A     :  Dict   =				''''''

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





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str		):



				'''simple docstring'''







				A     :  Dict   =				alphabet.replace(''' '''				,	''''''		).upper()
				A     :  List[str]   =				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
				A     :  str   =				(
				    '''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''',
				)
				A     :  Union[str, Any]   =				{}
				A     :  int   =				{}
				for letter, number in zip(lowerCamelCase_				,	lowerCamelCase_		):
								A     :  Optional[Any]   =				number
								A     :  Union[str, Any]   =				letter

				return message, alphabet, character_to_number, number_to_character





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,	lowerCamelCase_:  int = 5		):



				'''simple docstring'''







				A ,       A ,       A ,       A     :  Optional[int]   =				__prepare(
				    lowerCamelCase_				,	lowerCamelCase_		)
				A ,       A     :  int   =				'''''', ''''''

				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 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,	lowerCamelCase_:  int = 5		):



				'''simple docstring'''







				A ,       A ,       A ,       A     :  Optional[int]   =				__prepare(
				    lowerCamelCase_				,	lowerCamelCase_		)
				A     :  Tuple   =				[]
				A     :  Optional[Any]   =				''''''

				for i in range(0				,	len(lowerCamelCase_		) + 1				,	lowerCamelCase_		):
								A ,       A ,       A     :  List[Any]   =				__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__":
					UpperCamelCase_  =	"DEFEND THE EAST WALL OF THE CASTLE."
					UpperCamelCase_  =	encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
					UpperCamelCase_  =	decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
					print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')