
'''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
     A__ :		Tuple					='''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_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]=None      ,  __SCREAMING_SNAKE_CASE    :	Optional[Any]=None      ,  __SCREAMING_SNAKE_CASE    :	Any=None      ,  __SCREAMING_SNAKE_CASE    :	Tuple=None      ,  __SCREAMING_SNAKE_CASE    :	Dict=None      ,  __SCREAMING_SNAKE_CASE    :	List[Any]=None      ,  )					->							Tuple:





  """simple docstring"""

  if attention_mask is None:
    __A		:  Optional[int]       				=		np.where(input_ids != config.pad_token_id      ,  1      ,  0			)
  if decoder_attention_mask is None:
    __A		:  Dict       				=		np.where(decoder_input_ids != config.pad_token_id      ,  1      ,  0			)
  if head_mask is None:
    __A		:  int       				=		np.ones((config.encoder_layers, config.encoder_attention_heads)			)
  if decoder_head_mask is None:
    __A		:  Dict       				=		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": attention_mask,
  }







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



   """simple docstring"""





   __A		:  Any       				=		parent
   __A		:  List[Any]       				=		batch_size
   __A		:  List[str]       				=		seq_length
   __A		:  List[str]       				=		is_training
   __A		:  Any       				=		use_labels
   __A		:  Union[str, Any]       				=		vocab_size
   __A		:  List[Any]       				=		hidden_size
   __A		:  Tuple       				=		num_hidden_layers
   __A		:  Optional[int]       				=		num_attention_heads
   __A		:  Optional[Any]       				=		intermediate_size
   __A		:  Optional[Any]       				=		hidden_act
   __A		:  Any       				=		hidden_dropout_prob
   __A		:  Optional[Any]       				=		attention_probs_dropout_prob
   __A		:  List[str]       				=		max_position_embeddings
   __A		:  Optional[Any]       				=		eos_token_id
   __A		:  Tuple       				=		pad_token_id
   __A		:  Union[str, Any]       				=		bos_token_id
   __A		:  Tuple       				=		initializer_range
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





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

   __A		:  List[str]       				=		shift_tokens_right(_lowerCAmelCase					, 1					, 2							)

   __A		:  Dict       				=		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					, )
   __A		:  Optional[int]       				=		prepare_marian_inputs_dict(_lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase							)
   return config, inputs_dict
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A				,       __A		:  int       				=		self.prepare_config_and_inputs()
   return config, inputs_dict
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     Any					, lowerCamelCase      :     str					, lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		20
   __A		:  str       				=		model_class_name(_lowerCAmelCase							)

   __A		:  int       				=		model.encode(inputs_dict["""input_ids"""]							)

   __A				,       __A		:  int       				=		(
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   __A		:  Union[str, Any]       				=		model.init_cache(decoder_input_ids.shape[0]					, _lowerCAmelCase					, _lowerCAmelCase							)
   __A		:  List[Any]       				=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					, dtype="""i4"""							)

   __A		:  List[str]       				=		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[Any]       				=		model.decode(
       decoder_input_ids[:, :-1]					, _lowerCAmelCase					, decoder_attention_mask=_lowerCAmelCase					, past_key_values=_lowerCAmelCase					, decoder_position_ids=_lowerCAmelCase					, )

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

   __A		:  List[Any]       				=		model.decode(_lowerCAmelCase					, _lowerCAmelCase							)

   __A		:  List[str]       				=		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 		lowercase_( self      :     Dict					, lowerCamelCase      :     str					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		20
   __A		:  Optional[int]       				=		model_class_name(_lowerCAmelCase							)

   __A		:  Optional[int]       				=		model.encode(inputs_dict["""input_ids"""]							)

   __A				,       __A		:  Optional[int]       				=		(
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

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

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

   __A		:  Any       				=		model.decode(
       decoder_input_ids[:, :-1]					, _lowerCAmelCase					, decoder_attention_mask=_lowerCAmelCase					, past_key_values=_lowerCAmelCase					, decoder_position_ids=_lowerCAmelCase					, )
   __A		:  Dict       				=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					, dtype="""i4"""							)
   __A		:  Union[str, Any]       				=		model.decode(
       decoder_input_ids[:, -1:]					, _lowerCAmelCase					, past_key_values=outputs_cache.past_key_values					, decoder_attention_mask=_lowerCAmelCase					, decoder_position_ids=_lowerCAmelCase					, )

   __A		:  Optional[int]       				=		model.decode(_lowerCAmelCase					, _lowerCAmelCase					, decoder_attention_mask=_lowerCAmelCase							)

   __A		:  Dict       				=		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 			__A					(			_UpperCAmelCase     ,    unittest.TestCase     ,    _UpperCAmelCase      ):
 lowerCamelCase												=True
 lowerCamelCase												=(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 lowerCamelCase												=(FlaxMarianMTModel,) if is_flax_available() else ()
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  List[str]       				=		FlaxMarianModelTester(self							)
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A				,       __A		:  List[Any]       				=		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 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A				,       __A		:  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(_lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase							)
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A				,       __A		:  Optional[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		:  int       				=		self._prepare_for_class(_lowerCAmelCase					, _lowerCAmelCase							)
       __A		:  Optional[int]       				=		model_class(_lowerCAmelCase							)

       @jax.jit
       def encode_jitted(lowerCamelCase      :     Tuple					, lowerCamelCase      :     Any=None					, **lowerCamelCase      :     List[str]							):
         return model.encode(input_ids=_lowerCAmelCase					, attention_mask=_lowerCAmelCase							)

       with self.subTest("""JIT Enabled"""							):
         __A		:  Dict       				=		encode_jitted(**_lowerCAmelCase							).to_tuple()

       with self.subTest("""JIT Disabled"""							):
         with jax.disable_jit():
           __A		:  Tuple       				=		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 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A				,       __A		:  Optional[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[Any]       				=		model_class(_lowerCAmelCase							)
       __A		:  List[Any]       				=		model.encode(inputs_dict["""input_ids"""]					, inputs_dict["""attention_mask"""]							)

       __A		:  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(lowerCamelCase      :     int					, lowerCamelCase      :     Tuple					, lowerCamelCase      :     Optional[Any]							):
         return model.decode(
             decoder_input_ids=_lowerCAmelCase					, decoder_attention_mask=_lowerCAmelCase					, encoder_outputs=_lowerCAmelCase					, )

       with self.subTest("""JIT Enabled"""							):
         __A		:  Optional[Any]       				=		decode_jitted(**_lowerCAmelCase							).to_tuple()

       with self.subTest("""JIT Disabled"""							):
         with jax.disable_jit():
           __A		:  Any       				=		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 		lowercase_( self      :     Any							):



   """simple docstring"""





   for model_class_name in self.all_model_classes:
     __A		:  List[Any]       				=		model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""							)
     # FlaxMarianForSequenceClassification expects eos token in input_ids
     __A		:  Dict       				=		np.ones((1, 1)							) * model.config.eos_token_id
     __A		:  List[Any]       				=		model(_lowerCAmelCase							)
     self.assertIsNotNone(_lowerCAmelCase							)







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			unittest.TestCase      ):
 lowerCamelCase												=None
 lowerCamelCase												=None
 @classmethod
 def 		lowercase_( cls      :     List[str]							):



   """simple docstring"""





   __A		:  Optional[int]       				=		f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
   return cls
 @cached_property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   return MarianTokenizer.from_pretrained(self.model_name							)
 @property
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.eos_token_id
 @cached_property
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  List[Any]       				=		FlaxMarianMTModel.from_pretrained(self.model_name							)
   self.assertEqual(model.config.decoder_start_token_id					, model.config.pad_token_id							)
   return model
 def 		lowercase_( self      :     int					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Tuple       				=		self.translate_src_text(**_lowerCAmelCase							)
   self.assertListEqual(self.expected_text					, _lowerCAmelCase							)






 def 		lowercase_( self      :     Tuple					, **lowerCamelCase      :     List[Any]							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer(self.src_text					, padding=_lowerCAmelCase					, return_tensors="""np"""					, **_lowerCAmelCase							)
   __A		:  str       				=		self.model.generate(
       model_inputs.input_ids					, attention_mask=model_inputs.attention_mask					, num_beams=2					, max_length=1_28					, ).sequences
   __A		:  Tuple       				=		self.tokenizer.batch_decode(_lowerCAmelCase					, skip_special_tokens=_lowerCAmelCase							)
   return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="en"
 lowerCamelCase												="fr"
 lowerCamelCase												=[
     "I am a small frog.",
     "Now I can forget the 100 words of german that I know.",
 ]
 lowerCamelCase												=[
     "Je suis une petite grenouille.",
     "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
 ]
 @slow
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="fr"
 lowerCamelCase												="en"
 lowerCamelCase												=[
     "Donnez moi le micro.",
     "Tom et Mary étaient assis à une table.",  # Accents
 ]
 lowerCamelCase												=[
     "Give me the microphone.",
     "Tom and Mary were sitting at a table.",
 ]
 @slow
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="mt"
 lowerCamelCase												="en"
 lowerCamelCase												=["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
 lowerCamelCase												=["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
 @slow
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="en"
 lowerCamelCase												="de"
 lowerCamelCase												=[
     "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.",
 ]
 lowerCamelCase												=[
     "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 		lowercase_( self      :     int							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="en"
 lowerCamelCase												="zh"
 lowerCamelCase												=["My name is Wolfgang and I live in Berlin"]
 lowerCamelCase												=["我叫沃尔夫冈 我住在柏林"]
 @slow
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="ru"
 lowerCamelCase												="fr"
 lowerCamelCase												=["Он показал мне рукопись своей новой пьесы."]
 lowerCamelCase												=["Il m'a montré le manuscrit de sa nouvelle pièce."]
 @slow
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__A					(			_UpperCAmelCase      ):
 lowerCamelCase												="en"
 lowerCamelCase												="ROMANCE"
 lowerCamelCase												=[
     ">>fr<< Don't spend so much time watching TV.",
     ">>pt<< Your message has been sent.",
     ">>es<< He's two years older than me.",
 ]
 lowerCamelCase												=[
     "Ne passez pas autant de temps à regarder la télé.",
     "A sua mensagem foi enviada.",
     "Es dos años más viejo que yo.",
 ]
 @slow
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   self._assert_generated_batch_equal_expected()







import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

						def batch_iterator():
												for i in range(0						,      len(__snake_case  )						,      args.batch_size  ):
																		yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      UnigramTrainer(
						    unk_token='<unk>'						,      special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']						,      vocab_size=args.vocab_size						,      )

						logger.info('Training the tokenizer.'  )
						tokenizer.train_from_iterator(batch_iterator()						,      trainer=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      processors.TemplateProcessing(
						    single='[CLS]:0 $A:0 [SEP]:0'						,      pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'						,      special_tokens=[
						        ('[CLS]', cls_token_id),
						        ('[SEP]', sep_token_id),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

