


"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict=None,     _lowerCAmelCase     :					Union[str, Any]=None,     _lowerCAmelCase     :					int=None,     _lowerCAmelCase     :					List[str]=None,     _lowerCAmelCase     :					List[Any]=None,     _lowerCAmelCase     :					str=None,     ):






     """simple docstring"""

     if attention_mask is None:
          _a    	=				np.where(input_ids != config.pad_token_id,     1,     0       )
     if decoder_attention_mask is None:
          _a    	=				np.where(decoder_input_ids != config.pad_token_id,     1,     0       )
     if head_mask is None:
          _a    	=				np.ones((config.encoder_layers, config.encoder_attention_heads)       )
     if decoder_head_mask is None:
          _a    	=				np.ones((config.decoder_layers, config.decoder_attention_heads)       )
     if cross_attn_head_mask is None:
          _a    	=				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     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=13    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=99    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase="gelu"    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=1    ,				__UpperCAmelCase=0    ,				__UpperCAmelCase=0.02    ,				)       ->							Tuple:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				seq_length
          _a    	=				is_training
          _a    	=				use_labels
          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				intermediate_size
          _a    	=				hidden_act
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				max_position_embeddings
          _a    	=				eos_token_id
          _a    	=				pad_token_id
          _a    	=				bos_token_id
          _a    	=				initializer_range


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,				self.vocab_size							)    ,				3    ,				self.vocab_size							)
          _a    	=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,				dtype=np.intaa							))    ,				-1							)

          _a    	=				shift_tokens_right(__UpperCAmelCase    ,				1    ,				2							)

          _a    	=				BlenderbotSmallConfig(
              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=__UpperCAmelCase    ,				)
          _a    	=				prepare_blenderbot_inputs_dict(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)
          return config, inputs_dict


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a	,      _a    	=				self.prepare_config_and_inputs()
          return config, inputs_dict


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
          _a    	=				20
          _a    	=				model_class_name(__UpperCAmelCase							)

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

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

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

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

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

          _a    	=				model.decode(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				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    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							str:
          _a    	=				20
          _a    	=				model_class_name(__UpperCAmelCase							)

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

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

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

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

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

          _a    	=				model.decode(__UpperCAmelCase    ,				__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase							)

          _a    	=				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     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Optional[int]    =		99


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				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    	=				input_ids.shape[0]
          _a    	=				BlenderbotSmallConfig(
              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							)       ->							int:
          _a	,      _a	,      _a    	=				self._get_config_and_data()
          _a    	=				FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
          _a    	=				lm_model(input_ids=__UpperCAmelCase							)
          _a    	=				(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				BlenderbotSmallConfig(
              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    	=				FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
          _a    	=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,				dtype=np.intaa							)
          _a    	=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,				dtype=np.intaa							)
          _a    	=				lm_model(input_ids=__UpperCAmelCase    ,				decoder_input_ids=__UpperCAmelCase							)
          _a    	=				(*summary.shape, config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape    ,				__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,				dtype=np.intaa							)
          _a    	=				shift_tokens_right(__UpperCAmelCase    ,				1    ,				2							)
          _a    	=				np.equal(__UpperCAmelCase    ,				1							).astype(np.floataa							).sum()
          _a    	=				np.equal(__UpperCAmelCase    ,				1							).astype(np.floataa							).sum()
          self.assertEqual(shifted.shape    ,				input_ids.shape							)
          self.assertEqual(__UpperCAmelCase    ,				n_pad_before - 1							)
          self.assertTrue(np.equal(shifted[:, 0]    ,				2							).all()							)







@require_flax
class     __lowerCamelCase  (					a__  ,					unittest.TestCase  ,					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		True
     A_       :	List[Any]    =		(
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     A_       :	List[str]    =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				FlaxBlenderbotSmallModelTester(self							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a	,      _a    	=				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(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                    _a    	=				self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)
                    _a    	=				model_class(__UpperCAmelCase							)

                    @jax.jit
                    def encode_jitted(__UpperCAmelCase    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							):
                         return model.encode(input_ids=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							)

                    with self.subTest('''JIT Enabled'''							):
                         _a    	=				encode_jitted(**__UpperCAmelCase							).to_tuple()

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

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


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

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

                    _a    	=				{
                        '''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(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							):
                         return model.decode(
                             decoder_input_ids=__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase    ,				encoder_outputs=__UpperCAmelCase    ,				)

                    with self.subTest('''JIT Enabled'''							):
                         _a    	=				decode_jitted(**__UpperCAmelCase							).to_tuple()

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

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





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							int:
          for model_class_name in self.all_model_classes:
               _a    	=				model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''							)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _a    	=				np.ones((1, 1)							) * model.config.eos_token_id
               _a    	=				model(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)



"""simple docstring"""






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		['input_ids', 'attention_mask']


     def __init__(      self    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase=125    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase    ,				)       ->							None:
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
               _a    	=				[F'<extra_id_{i}>' for i in range(__UpperCAmelCase							)]
          elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra_id special tokens
               _a    	=				len(set(filter(lambda __UpperCAmelCase							: bool('''extra_id''' in str(__UpperCAmelCase							)							)    ,				__UpperCAmelCase							)							)							)
               if extra_tokens != extra_ids:
                    raise ValueError(
                        F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                        ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                        ''' extra_ids tokens'''							)

          _a    	=				AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							) else pad_token
          _a    	=				AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							) else eos_token
          _a    	=				AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							) else unk_token

          super().__init__(
              eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				extra_ids=__UpperCAmelCase    ,				additional_special_tokens=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

          _a    	=				extra_ids

          _a    	=				2**8  # utf is 8 bits

          # define special tokens dict
          _a    	=				{
              self.pad_token: 0,
              self.eos_token: 1,
              self.unk_token: 2,
          }
          _a    	=				len(self.special_tokens_encoder							)
          _a    	=				len(__UpperCAmelCase							)
          for i, token in enumerate(__UpperCAmelCase							):
               _a    	=				self.vocab_size + i - n
          _a    	=				{v: k for k, v in self.special_tokens_encoder.items()}


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

          # normal case: some special tokens
          if token_ids_a is None:
               return ([0] * len(__UpperCAmelCase							)) + [1]
          return ([0] * len(__UpperCAmelCase							)) + [1] + ([0] * len(__UpperCAmelCase							)) + [1]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[int]:
          if len(__UpperCAmelCase							) > 0 and token_ids[-1] == self.eos_token_id:
               warnings.warn(
                   F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                   ''' eos tokens being added.'''							)
               return token_ids
          else:
               return token_ids + [self.eos_token_id]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.eos_token_id]

          if token_ids_a is None:
               return len(token_ids_a + eos							) * [0]
          return len(token_ids_a + eos + token_ids_a + eos							) * [0]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				self._add_eos_if_not_present(__UpperCAmelCase							)
          if token_ids_a is None:
               return token_ids_a
          else:
               _a    	=				self._add_eos_if_not_present(__UpperCAmelCase							)
               return token_ids_a + token_ids_a


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				[chr(__UpperCAmelCase							) for i in text.encode('''utf-8'''							)]
          return tokens


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Union[str, Any]:
          if token in self.special_tokens_encoder:
               _a    	=				self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
               _a    	=				self.added_tokens_encoder[token]
          elif len(__UpperCAmelCase							) != 1:
               _a    	=				self.unk_token_id
          else:
               _a    	=				ord(__UpperCAmelCase							) + self._num_special_tokens
          return token_id


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          if index in self.special_tokens_decoder:
               _a    	=				self.special_tokens_decoder[index]
          else:
               _a    	=				chr(index - self._num_special_tokens							)
          return token


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				B''''''
          for token in tokens:
               if token in self.special_tokens_decoder:
                    _a    	=				self.special_tokens_decoder[token].encode('''utf-8'''							)
               elif token in self.added_tokens_decoder:
                    _a    	=				self.special_tokens_decoder[token].encode('''utf-8'''							)
               elif token in self.special_tokens_encoder:
                    _a    	=				token.encode('''utf-8'''							)
               elif token in self.added_tokens_encoder:
                    _a    	=				token.encode('''utf-8'''							)
               else:
                    _a    	=				bytes([ord(__UpperCAmelCase							)]							)
               bstring += tok_string
          _a    	=				bstring.decode('''utf-8'''    ,				errors='''ignore'''							)
          return string





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          return ()