code: from __future__ import annotations
  
  import copy
  import os
  import tempfile
  from importlib import import_module
  from math import isnan
  
  from transformers import is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow
  
  from ..test_modeling_tf_common import ids_tensor
  
  
  if is_tf_available():
      import numpy as np
      import tensorflow as tf
  
      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )
  
      if _tf_gpu_memory_limit is not None:
          A_:      Union[str, Any]        =			tf.config.list_physical_devices('GPU')
          for gpu in gpus:
              # Restrict TensorFlow to only allocate x GB of memory on the GPUs
              try:
                  tf.config.set_logical_device_configuration(
                      gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                  )
                  A_:      Union[str, Any]        =			tf.config.list_logical_devices('GPU')
                  print('Logical GPUs', logical_gpus)
              except RuntimeError as e:
                  # Virtual devices must be set before GPUs have been initialized
                  print(e)
  
  
  @require_tf
  class _lowercase					:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
         lowerCAmelCase__       =					None
         lowerCAmelCase__       =					()
         lowerCAmelCase__       =					()
         lowerCAmelCase__       =					True
         lowerCAmelCase__       =					True
         lowerCAmelCase__       =					True
         lowerCAmelCase__       =					False
  
  
  
         def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=False  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	copy.deepcopy(UpperCAmelCase  )
  
                if model_class in get_values(UpperCAmelCase  ):
                       _lowercase				=	{
                           k: tf.tile(tf.expand_dims(UpperCAmelCase     ,   1  )     ,   (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)  )
                           if isinstance(UpperCAmelCase     ,   tf.Tensor  ) and v.ndim > 0
                           else v
                           for k, v in inputs_dict.items()
                       }
  
                if return_labels:
                       if model_class in get_values(UpperCAmelCase  ):
                              _lowercase				=	tf.ones(self.model_tester.batch_size     ,   dtype=tf.intaa  )
                       elif model_class in get_values(UpperCAmelCase  ):
                              _lowercase				=	tf.zeros(self.model_tester.batch_size     ,   dtype=tf.intaa  )
                              _lowercase				=	tf.zeros(self.model_tester.batch_size     ,   dtype=tf.intaa  )
                       elif model_class in [
                           *get_values(UpperCAmelCase  ),
                           *get_values(UpperCAmelCase  ),
                       ]:
                              _lowercase				=	tf.zeros(self.model_tester.batch_size     ,   dtype=tf.intaa  )
                       elif model_class in get_values(UpperCAmelCase  ):
                              _lowercase				=	tf.zeros(self.model_tester.batch_size     ,   dtype=tf.intaa  )
                       elif model_class in [
                           *get_values(UpperCAmelCase  ),
                           *get_values(UpperCAmelCase  ),
                           *get_values(UpperCAmelCase  ),
                           *get_values(UpperCAmelCase  ),
                           *get_values(UpperCAmelCase  ),
                       ]:
                              _lowercase				=	tf.zeros(
                                  (self.model_tester.batch_size, self.model_tester.seq_length)     ,   dtype=tf.intaa  )
                return inputs_dict
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
                for model_class in self.all_model_classes[:2]:
                       _lowercase				=	self._prepare_for_class(UpperCAmelCase     ,   UpperCAmelCase  )
                       _lowercase				=	model_class(UpperCAmelCase  )
  
                       @tf.function
                       def run_in_graph_mode():
                              return model(UpperCAmelCase  )
  
                       _lowercase				=	run_in_graph_mode()
                       self.assertIsNotNone(UpperCAmelCase  )
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
                for model_class in self.all_model_classes[:2]:
                       _lowercase				=	self._prepare_for_class(UpperCAmelCase     ,   UpperCAmelCase  )
                       _lowercase				=	model_class(UpperCAmelCase  )
  
                       @tf.function(experimental_compile=UpperCAmelCase  )
                       def run_in_graph_mode():
                              return model(UpperCAmelCase  )
  
                       _lowercase				=	run_in_graph_mode()
                       self.assertIsNotNone(UpperCAmelCase  )
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
                for model_class in self.all_model_classes[:2]:
                       _lowercase				=	model_class(UpperCAmelCase  )
                       if getattr(UpperCAmelCase     ,   """hf_compute_loss"""     ,   UpperCAmelCase  ):
                              # Test that model correctly compute the loss with kwargs
                              _lowercase				=	self._prepare_for_class(inputs_dict.copy()     ,   UpperCAmelCase     ,   return_labels=UpperCAmelCase  )
                              # Is there a better way to remove these decoder inputs?
                              _lowercase				=	{
                                  key: val
                                  for key, val in prepared_for_class.items()
                                  if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                              }
  
                              _lowercase				=	{
                                  "labels",
                                  "label",
                                  "label_ids",
                                  "start_positions",
                                  "start_position",
                                  "end_positions",
                                  "end_position",
                                  "next_sentence_label",
                              }
                              _lowercase				=	possible_label_cols.intersection(set(UpperCAmelCase  )  )
                              self.assertGreater(len(UpperCAmelCase  )     ,   0     ,   msg="""No matching label names found!"""  )
                              _lowercase				=	{key: val for key, val in prepared_for_class.items() if key in label_names}
                              _lowercase				=	{key: val for key, val in prepared_for_class.items() if key not in label_names}
                              self.assertGreater(len(UpperCAmelCase  )     ,   0  )
  
                              # Make sure it works with XLA!
                              model.compile(optimizer=tf.keras.optimizers.SGD(0.0  )     ,   jit_compile=UpperCAmelCase  )
                              # Make sure the model fits without crashing regardless of where we pass the labels
                              _lowercase				=	model.fit(
                                  UpperCAmelCase     ,   validation_data=UpperCAmelCase     ,   steps_per_epoch=1     ,   validation_steps=1     ,   shuffle=UpperCAmelCase     ,   verbose=0     ,   )
                              _lowercase				=	history.history["loss"][0]
                              self.assertTrue(not isnan(UpperCAmelCase  )  )
                              _lowercase				=	history.history["val_loss"][0]
                              self.assertTrue(not isnan(UpperCAmelCase  )  )
  
                              # Now test it with separate labels, to make sure that path works in XLA too.
                              _lowercase				=	model_class(UpperCAmelCase  )
                              model.compile(optimizer=tf.keras.optimizers.SGD(0.0  )     ,   jit_compile=UpperCAmelCase  )
                              _lowercase				=	model.fit(
                                  UpperCAmelCase     ,   UpperCAmelCase     ,   validation_data=(inputs_minus_labels, labels)     ,   steps_per_epoch=1     ,   validation_steps=1     ,   shuffle=UpperCAmelCase     ,   verbose=0     ,   )
  
                              _lowercase				=	history.history["loss"][0]
                              self.assertTrue(not isnan(UpperCAmelCase  )  )
                              _lowercase				=	history.history["val_loss"][0]
                              self.assertTrue(not isnan(UpperCAmelCase  )  )
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
                _lowercase				=	True
                _lowercase				=	True
  
                if hasattr(UpperCAmelCase     ,   """use_cache"""  ):
                       _lowercase				=	True
  
                _lowercase				=	getattr(self.model_tester     ,   """encoder_seq_length"""     ,   self.model_tester.seq_length  )
                _lowercase				=	getattr(self.model_tester     ,   """key_length"""     ,   UpperCAmelCase  )
  
                for model_class in self.all_model_classes[:2]:
                       _lowercase				=	self._prepare_for_class(UpperCAmelCase     ,   UpperCAmelCase  )
                       _lowercase				=	model_class(UpperCAmelCase  )
                       model.build()
                       _lowercase				=	len(model(UpperCAmelCase  )  )
  
                       for key in list(class_inputs_dict.keys()  ):
                              # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                              if key not in model.input_signature:
                                     del class_inputs_dict[key]
                              # Check it's a tensor, in case the inputs dict has some bools in it too
                              elif isinstance(class_inputs_dict[key]     ,   tf.Tensor  ) and class_inputs_dict[key].dtype.is_integer:
                                     _lowercase				=	tf.cast(class_inputs_dict[key]     ,   tf.intaa  )
  
                       if set(class_inputs_dict.keys()  ) != set(model.input_signature.keys()  ):
                              continue  # Some models have inputs that the preparation functions don't create, we skip those
  
                       with tempfile.TemporaryDirectory() as tmpdirname:
                              model.save_pretrained(UpperCAmelCase     ,   saved_model=UpperCAmelCase  )
                              _lowercase				=	os.path.join(UpperCAmelCase     ,   """saved_model"""     ,   """1"""  )
                              _lowercase				=	tf.keras.models.load_model(UpperCAmelCase  )
                              _lowercase				=	model(UpperCAmelCase  )
  
                              if self.is_encoder_decoder:
                                     _lowercase				=	outputs["encoder_hidden_states"]
                                     _lowercase				=	outputs["encoder_attentions"]
                              else:
                                     _lowercase				=	outputs["hidden_states"]
                                     _lowercase				=	outputs["attentions"]
  
                              self.assertEqual(len(UpperCAmelCase  )     ,   UpperCAmelCase  )
  
                              _lowercase				=	getattr(
                                  self.model_tester     ,   """expected_num_hidden_layers"""     ,   self.model_tester.num_hidden_layers + 1  )
  
                              self.assertEqual(len(UpperCAmelCase  )     ,   UpperCAmelCase  )
                              self.assertListEqual(
                                  list(output_hidden_states[0].shape[-2:]  )     ,   [self.model_tester.seq_length, self.model_tester.hidden_size]     ,   )
  
                              self.assertEqual(len(UpperCAmelCase  )     ,   self.model_tester.num_hidden_layers  )
                              self.assertListEqual(
                                  list(output_attentions[0].shape[-3:]  )     ,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]     ,   )
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                tf.keras.mixed_precision.set_global_policy("""mixed_float16"""  )
  
                # try/finally block to ensure subsequent tests run in float32
                try:
                       _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
                       for model_class in self.all_model_classes[:2]:
                              _lowercase				=	self._prepare_for_class(UpperCAmelCase     ,   UpperCAmelCase  )
                              _lowercase				=	model_class(UpperCAmelCase  )
                              _lowercase				=	model(UpperCAmelCase  )
  
                              self.assertIsNotNone(UpperCAmelCase  )
                finally:
                       tf.keras.mixed_precision.set_global_policy("""float32"""  )
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
                # head_mask and decoder_head_mask has different shapes than other input args
                if "head_mask" in inputs_dict:
                       del inputs_dict["head_mask"]
                if "decoder_head_mask" in inputs_dict:
                       del inputs_dict["decoder_head_mask"]
                if "cross_attn_head_mask" in inputs_dict:
                       del inputs_dict["cross_attn_head_mask"]
                _lowercase				=	{
                    module_member
                    for model_class in self.all_model_classes
                    for module in (import_module(model_class.__module__  ),)
                    for module_member_name in dir(UpperCAmelCase  )
                    if module_member_name.endswith("""MainLayer"""  )
                    for module_member in (getattr(UpperCAmelCase     ,   UpperCAmelCase  ),)
                    if isinstance(UpperCAmelCase     ,   UpperCAmelCase  )
                    and tf.keras.layers.Layer in module_member.__bases__
                    and getattr(UpperCAmelCase     ,   """_keras_serializable"""     ,   UpperCAmelCase  )
                }
  
                for main_layer_class in tf_main_layer_classes:
                       # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
                       if "T5" in main_layer_class.__name__:
                              # Take the same values than in TFT5ModelTester for this shared layer
                              _lowercase				=	TFSharedEmbeddings(self.model_tester.vocab_size     ,   self.model_tester.hidden_size     ,   name="""shared"""  )
                              _lowercase				=	False
                              _lowercase				=	main_layer_class(UpperCAmelCase     ,   embed_tokens=UpperCAmelCase  )
                       else:
                              _lowercase				=	main_layer_class(UpperCAmelCase  )
  
                       _lowercase				=	{
                           name: tf.keras.Input(tensor.shape[1:]     ,   dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
                       }
  
                       if hasattr(self.model_tester     ,   """num_labels"""  ):
                              _lowercase				=	self.model_tester.num_labels
                       else:
                              _lowercase				=	2
  
                       _lowercase				=	tf.data.Dataset.from_tensor_slices(
                           (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)  ))  ).batch(1  )
  
                       _lowercase				=	main_layer(UpperCAmelCase  )[0]
                       _lowercase				=	tf.keras.layers.Dense(UpperCAmelCase     ,   activation="""softmax"""     ,   name="""outputs"""  )(UpperCAmelCase  )
                       _lowercase				=	tf.keras.models.Model(inputs=UpperCAmelCase     ,   outputs=[outputs]  )
  
                       model.compile(loss="""binary_crossentropy"""     ,   optimizer="""adam"""     ,   metrics=["""binary_accuracy"""]  )
                       model.fit(UpperCAmelCase     ,   epochs=1  )
  
                       with tempfile.TemporaryDirectory() as tmpdirname:
                              _lowercase				=	os.path.join(UpperCAmelCase     ,   """keras_model.h5"""  )
                              model.save(UpperCAmelCase  )
                              if "T5" in main_layer_class.__name__:
                                     _lowercase				=	tf.keras.models.load_model(
                                         UpperCAmelCase     ,   custom_objects={
                                             main_layer_class.__name__: main_layer_class,
                                             """TFSharedEmbeddings""": TFSharedEmbeddings,
                                         }     ,   )
                              else:
                                     _lowercase				=	tf.keras.models.load_model(
                                         UpperCAmelCase     ,   custom_objects={main_layer_class.__name__: main_layer_class}  )
                              assert isinstance(UpperCAmelCase     ,   tf.keras.Model  )
                              model(UpperCAmelCase  )
  
  
  
         @slow
         def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	self.model_tester.prepare_config_and_inputs_for_common()
  
                for model_class in self.all_model_classes[:2]:
                       _lowercase				=	model_class(UpperCAmelCase  )
  
                       _lowercase				=	copy.deepcopy(UpperCAmelCase  )
  
                       if not self.is_encoder_decoder:
                              _lowercase				=	inputs["input_ids"]
                              del inputs["input_ids"]
                       else:
                              _lowercase				=	inputs["input_ids"]
                              _lowercase				=	inputs.get("""decoder_input_ids"""     ,   UpperCAmelCase  )
                              del inputs["input_ids"]
                              inputs.pop("""decoder_input_ids"""     ,   UpperCAmelCase  )
  
                       if not self.is_encoder_decoder:
                              _lowercase				=	model.get_input_embeddings()(UpperCAmelCase  )
                       else:
                              _lowercase				=	model.get_input_embeddings()(UpperCAmelCase  )
                              _lowercase				=	model.get_input_embeddings()(UpperCAmelCase  )
  
                       _lowercase				=	self._prepare_for_class(UpperCAmelCase     ,   UpperCAmelCase  )
  
                       @tf.function
                       def run_in_graph_mode():
                              return model(UpperCAmelCase  )
  
                       _lowercase				=	run_in_graph_mode()
                       self.assertIsNotNone(UpperCAmelCase  )
  
  
  
         def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                _lowercase				=	[]
                if model.config.bos_token_id is not None:
                       special_tokens.append(model.config.bos_token_id  )
                if model.config.pad_token_id is not None:
                       special_tokens.append(model.config.pad_token_id  )
                if model.config.eos_token_id is not None:
                       special_tokens.append(model.config.eos_token_id  )
  
                # create random bad tokens that are not special tokens
                _lowercase				=	[]
                while len(UpperCAmelCase  ) < num_bad_tokens:
                       _lowercase				=	tf.squeeze(ids_tensor((1, 1)     ,   self.model_tester.vocab_size  )     ,   0  ).numpy()[0]
                       if token not in special_tokens:
                              bad_tokens.append(UpperCAmelCase  )
                return bad_tokens
  
  
  
         def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                for token_id in output_ids[0].numpy().tolist():
                       self.assertGreaterEqual(UpperCAmelCase     ,   0  )
                       self.assertLess(UpperCAmelCase     ,   self.model_tester.vocab_size  )
  
  
  
         def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
                '''simple docstring'''
  
  
  
  
  
  
                for bad_word_ids in bad_words_ids:
                       # for all slices in batch
                       for generated_ids_slice in generated_ids:
                              # for all word idx
                              for i in range(len(UpperCAmelCase  )     ,   len(UpperCAmelCase  )  ):
                                     # if tokens match
                                     if generated_ids_slice[i - len(UpperCAmelCase  ) : i] == bad_word_ids:
                                            return True
                return False
  
  
  
style_context: import numpy as np
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	bin(int.from_bytes(text.encode(_A ,_A					) ,"""big"""					)					)[2:]
  							return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	int(_A ,2					)
  							return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""					).decode(_A ,_A					) or "\0"
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
  														raise ValueError("""size of parity don't match with size of data"""					)
  
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							# sorted information data for the size of the output data
  							_lowercase				=	[]
  							# data position template + parity
  							_lowercase				=	[]
  							# parity bit counter
  							_lowercase				=	0
  							# counter position of data bits
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par:
  																					if (np.log(_A					) / np.log(2					)).is_integer():
  																												data_out_gab.append("""P"""					)
  																												_lowercase				=	qtd_bp + 1
  																					else:
  																												data_out_gab.append("""D"""					)
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a given parity
  														_lowercase				=	0
  														# counter to control the loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(cont_bo % 2					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							return data_out
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  							# list of parity received
  							_lowercase				=	[]
  							_lowercase				=	[]
  
  							for x in range(1 ,len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														#  and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_output.append(data[cont_data]					)
  														else:
  																					parity_received.append(data[cont_data]					)
  														cont_data += 1
  
  							# -----------calculates the parity with the data
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							#  sorted information data for the size of the output data
  							_lowercase				=	[]
  							# Data position feedback + parity
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template position of bits - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data_output[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a certain parity
  														_lowercase				=	0
  														# Counter to control loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(str(cont_bo % 2					)					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # Parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							_lowercase				=	parity_received == parity
  							return data_output, ack
  
  
  