'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import numpy as np
 import tensorflow as tf

 from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
 from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor






class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   int   =      self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(snake_case_      ,		'hidden_sizes'						)						)
       self.parent.assertTrue(hasattr(snake_case_      ,		'neck_hidden_sizes'						)						)
       self.parent.assertTrue(hasattr(snake_case_      ,		'num_attention_heads'						)						)







class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_=1_3      ,		snake_case_=3_2      ,		snake_case_=2      ,		snake_case_=3      ,		snake_case_=6_4_0      ,		snake_case_=4      ,		snake_case_="silu"      ,		snake_case_=3      ,		snake_case_=3_2      ,		snake_case_=0.1      ,		snake_case_=0.1      ,		snake_case_=0.1      ,		snake_case_=0.02      ,		snake_case_=True      ,		snake_case_=True      ,		snake_case_=1_0      ,		snake_case_=None      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      parent
       UpperCAmelCase_							:   int   =      batch_size
       UpperCAmelCase_							:   Union[str, Any]   =      image_size
       UpperCAmelCase_							:   Dict   =      patch_size
       UpperCAmelCase_							:   Tuple   =      num_channels
       UpperCAmelCase_							:   Optional[int]   =      last_hidden_size
       UpperCAmelCase_							:   str   =      num_attention_heads
       UpperCAmelCase_							:   int   =      hidden_act
       UpperCAmelCase_							:   Union[str, Any]   =      conv_kernel_size
       UpperCAmelCase_							:   Optional[int]   =      output_stride
       UpperCAmelCase_							:   Tuple   =      hidden_dropout_prob
       UpperCAmelCase_							:   int   =      attention_probs_dropout_prob
       UpperCAmelCase_							:   Tuple   =      classifier_dropout_prob
       UpperCAmelCase_							:   List[str]   =      use_labels
       UpperCAmelCase_							:   Optional[int]   =      is_training
       UpperCAmelCase_							:   Optional[Any]   =      num_labels
       UpperCAmelCase_							:   List[str]   =      initializer_range
       UpperCAmelCase_							:   Union[str, Any]   =      scope


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCAmelCase_							:   Any   =      None
       UpperCAmelCase_							:   Dict   =      None
       if self.use_labels:
            UpperCAmelCase_							:   str   =      ids_tensor([self.batch_size]      ,		self.num_labels						)
            UpperCAmelCase_							:   Optional[int]   =      ids_tensor([self.batch_size, self.image_size, self.image_size]      ,		self.num_labels						)

       UpperCAmelCase_							:   List[Any]   =      self.get_config()

       return config, pixel_values, labels, pixel_labels


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       return MobileViTConfig(
           image_size=self.image_size      ,		patch_size=self.patch_size      ,		num_channels=self.num_channels      ,		num_attention_heads=self.num_attention_heads      ,		hidden_act=self.hidden_act      ,		conv_kernel_size=self.conv_kernel_size      ,		output_stride=self.output_stride      ,		hidden_dropout_prob=self.hidden_dropout_prob      ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,		classifier_dropout_prob=self.classifier_dropout_prob      ,		initializer_range=self.initializer_range      ,		)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      TFMobileViTModel(config=snake_case_						)
       UpperCAmelCase_							:   Any   =      model(snake_case_      ,		training=snake_case_						)
       UpperCAmelCase_							:   int   =      self.image_size // self.output_stride
       self.parent.assertEqual(
           result.last_hidden_state.shape      ,		(self.batch_size, self.last_hidden_size, expected_height, expected_width)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self.num_labels
       UpperCAmelCase_							:   int   =      TFMobileViTForImageClassification(snake_case_						)
       UpperCAmelCase_							:   int   =      model(snake_case_      ,		labels=snake_case_      ,		training=snake_case_						)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.num_labels
       UpperCAmelCase_							:   int   =      TFMobileViTForSemanticSegmentation(snake_case_						)
       UpperCAmelCase_							:   str   =      self.image_size // self.output_stride

       UpperCAmelCase_							:   Tuple   =      model(snake_case_      ,		training=snake_case_						)
       self.parent.assertEqual(
           result.logits.shape      ,		(self.batch_size, self.num_labels, expected_height, expected_width)						)

       UpperCAmelCase_							:   Union[str, Any]   =      model(snake_case_      ,		labels=snake_case_      ,		training=snake_case_						)
       self.parent.assertEqual(
           result.logits.shape      ,		(self.batch_size, self.num_labels, expected_height, expected_width)						)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.prepare_config_and_inputs()
       UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_							:   Dict   =      config_and_inputs
       UpperCAmelCase_							:   Any   =      {'pixel_values': pixel_values}
       return config, inputs_dict







@require_tf
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_						,	lowerCamelCase_						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:Optional[Any]						    =		(
      (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
      if is_tf_available()
      else ()
  )
  lowerCamelCase_		:Union[str, Any]						    =		(
      {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
      if is_tf_available()
      else {}
  )

  lowerCamelCase_		:List[str]						    =		False
  lowerCamelCase_		:Dict						    =		False
  lowerCamelCase_		:List[str]						    =		False
  lowerCamelCase_		:Optional[int]						    =		False
  lowerCamelCase_		:Union[str, Any]						    =		False


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      TFMobileViTModelTester(self						)
       UpperCAmelCase_							:   Dict   =      TFMobileViTConfigTester(self      ,		config_class=snake_case_      ,		has_text_modality=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  @unittest.skip(reason='MobileViT does not use inputs_embeds'						)
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       pass


  @unittest.skip(reason='MobileViT does not support input and output embeddings'						)
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       pass


  @unittest.skip(reason='MobileViT does not output attentions'						)
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:   Dict   =      model_class(snake_case_						)
            UpperCAmelCase_							:   List[Any]   =      inspect.signature(model.call						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase_							:   Optional[int]   =      [*signature.parameters.keys()]

            UpperCAmelCase_							:   List[str]   =      ['pixel_values']
            self.assertListEqual(arg_names[:1]      ,		snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_      ,		snake_case_      ,		snake_case_						):
            UpperCAmelCase_							:   List[Any]   =      model_class(snake_case_						)

            UpperCAmelCase_							:   Dict   =      model(**self._prepare_for_class(snake_case_      ,		snake_case_						)						)

            UpperCAmelCase_							:   Optional[Any]   =      outputs.hidden_states

            UpperCAmelCase_							:   Tuple   =      5
            self.assertEqual(len(snake_case_						)      ,		snake_case_						)

            # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
            # with the width and height being successively divided by 2.
            UpperCAmelCase_							:   Dict   =      2
            for i in range(len(snake_case_						)						):
                 self.assertListEqual(
                     list(hidden_states[i].shape[-2:]						)      ,		[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]      ,		)
                 divisor *= 2

            self.assertEqual(self.model_tester.output_stride      ,		divisor // 2						)

       UpperCAmelCase_   ,			UpperCAmelCase_							:   Dict   =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:   List[Any]   =      True
            check_hidden_states_output(snake_case_      ,		snake_case_      ,		snake_case_						)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCAmelCase_							:   Optional[int]   =      True

            check_hidden_states_output(snake_case_      ,		snake_case_      ,		snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_						)


  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU'						)						) == 0      ,		reason='TF does not support backprop for grouped convolutions on CPU.'      ,		)
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       super().test_dataset_conversion()


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_=2E-1      ,		snake_case_=2E-1						):




       '''simple docstring'''

       self.assertTrue(np.allclose(snake_case_      ,		snake_case_      ,		atol=snake_case_      ,		rtol=snake_case_						)						)


  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU'						)						) == 0      ,		reason='TF does not support backprop for grouped convolutions on CPU.'      ,		)
  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

       for model_class in self.all_model_classes:
            # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
            if model_class.__name__ != "TFMobileViTModel":
                 UpperCAmelCase_							:   int   =      model_class(snake_case_						)
                 if getattr(snake_case_      ,		'hf_compute_loss'      ,		snake_case_						):
                      super().test_keras_fit()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
            # set an ignore index to correctly test the masked loss used in
            # `TFMobileViTForSemanticSegmentation`.
            if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                 UpperCAmelCase_							:   Tuple   =      5

            UpperCAmelCase_							:   Optional[int]   =      model_class(snake_case_						)
            if getattr(snake_case_      ,		'hf_compute_loss'      ,		snake_case_						):
                 # The number of elements in the loss should be the same as the number of elements in the label
                 UpperCAmelCase_							:   List[str]   =      self._prepare_for_class(inputs_dict.copy()      ,		snake_case_      ,		return_labels=snake_case_						)
                 UpperCAmelCase_							:   Dict   =      prepared_for_class[
                     sorted(prepared_for_class.keys() - inputs_dict.keys()      ,		reverse=snake_case_						)[0]
                 ]
                 UpperCAmelCase_							:   Tuple   =      added_label.shape.as_list()[:1]

                 # Test that model correctly compute the loss with kwargs
                 UpperCAmelCase_							:   Optional[Any]   =      self._prepare_for_class(inputs_dict.copy()      ,		snake_case_      ,		return_labels=snake_case_						)
                 UpperCAmelCase_							:   Tuple   =      {'input_ids', 'pixel_values', 'input_features'}
                 UpperCAmelCase_							:   Tuple   =      possible_input_names.intersection(set(snake_case_						)						).pop()
                 UpperCAmelCase_							:   int   =      prepared_for_class.pop(snake_case_						)

                 UpperCAmelCase_							:   Any   =      model(snake_case_      ,		**snake_case_						)[0]
                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)

                 # Test that model correctly compute the loss when we mask some positions
                 UpperCAmelCase_							:   int   =      self._prepare_for_class(inputs_dict.copy()      ,		snake_case_      ,		return_labels=snake_case_						)
                 UpperCAmelCase_							:   Dict   =      {'input_ids', 'pixel_values', 'input_features'}
                 UpperCAmelCase_							:   Any   =      possible_input_names.intersection(set(snake_case_						)						).pop()
                 UpperCAmelCase_							:   Any   =      prepared_for_class.pop(snake_case_						)
                 if "labels" in prepared_for_class:
                      UpperCAmelCase_							:   str   =      prepared_for_class['labels'].numpy()
                      if len(labels.shape						) > 1 and labels.shape[1] != 1:
                           # labels[0] = -100
                           UpperCAmelCase_							:   str   =      tf.convert_to_tensor(snake_case_						)
                           UpperCAmelCase_							:   int   =      model(snake_case_      ,		**snake_case_						)[0]
                           self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)
                           self.assertTrue(not np.any(np.isnan(loss.numpy()						)						)						)

                # Test that model correctly compute the loss with a dict
                 UpperCAmelCase_							:   Union[str, Any]   =      self._prepare_for_class(inputs_dict.copy()      ,		snake_case_      ,		return_labels=snake_case_						)
                 UpperCAmelCase_							:   Tuple   =      model(snake_case_						)[0]
                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)

                 # Test that model correctly compute the loss with a tuple
                 UpperCAmelCase_							:   List[Any]   =      self._prepare_for_class(inputs_dict.copy()      ,		snake_case_      ,		return_labels=snake_case_						)

                 # Get keys that were added with the _prepare_for_class function
                 UpperCAmelCase_							:   Union[str, Any]   =      prepared_for_class.keys() - inputs_dict.keys()
                 UpperCAmelCase_							:   List[Any]   =      inspect.signature(model.call						).parameters
                 UpperCAmelCase_							:   str   =      list(signature.keys()						)

                 # Create a dictionary holding the location of the tensors in the tuple
                 UpperCAmelCase_							:   List[str]   =      {0: input_name}
                 for label_key in label_keys:
                      UpperCAmelCase_							:   List[Any]   =      signature_names.index(snake_case_						)
                      UpperCAmelCase_							:   Union[str, Any]   =      label_key
                 UpperCAmelCase_							:   List[str]   =      sorted(tuple_index_mapping.items()						)
                 # Initialize a list with their default values, update the values and convert to a tuple
                 UpperCAmelCase_							:   Optional[Any]   =      []

                 for name in signature_names:
                      if name != "kwargs":
                           list_input.append(signature[name].default						)

                 for index, value in sorted_tuple_index_mapping:
                      UpperCAmelCase_							:   List[Any]   =      prepared_for_class[value]

                 UpperCAmelCase_							:   Any   =      tuple(snake_case_						)

                 # Send to model
                 UpperCAmelCase_							:   Optional[int]   =      model(tuple_input[:-1]						)[0]

                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_							:   Any   =      TFMobileViTModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)







def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
     return image






@require_tf
class      __SCREAMING_SNAKE_CASE						(       unittest.TestCase       ):







  '''simple docstring'''






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'						)

       UpperCAmelCase_							:   Optional[int]   =      MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'						)
       UpperCAmelCase_							:   Optional[int]   =      prepare_img()
       UpperCAmelCase_							:   List[Any]   =      image_processor(images=snake_case_      ,		return_tensors='tf'						)

       # forward pass
       UpperCAmelCase_							:   Tuple   =      model(**snake_case_      ,		training=snake_case_						)

       # verify the logits
       UpperCAmelCase_							:   Any   =      tf.TensorShape((1, 1_0_0_0)						)
       self.assertEqual(outputs.logits.shape      ,		snake_case_						)

       UpperCAmelCase_							:   Any   =      tf.constant([-1.93_64, -1.23_27, -0.46_53]						)

       tf.debugging.assert_near(outputs.logits[0, :3]      ,		snake_case_      ,		atol=1E-4      ,		rtol=1E-04						)






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)

       UpperCAmelCase_							:   Union[str, Any]   =      MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)

       UpperCAmelCase_							:   int   =      prepare_img()
       UpperCAmelCase_							:   List[str]   =      image_processor(images=snake_case_      ,		return_tensors='tf'						)

       # forward pass
       UpperCAmelCase_							:   Dict   =      model(inputs.pixel_values      ,		training=snake_case_						)
       UpperCAmelCase_							:   Optional[Any]   =      outputs.logits

       # verify the logits
       UpperCAmelCase_							:   Optional[Any]   =      tf.TensorShape((1, 2_1, 3_2, 3_2)						)
       self.assertEqual(logits.shape      ,		snake_case_						)

       UpperCAmelCase_							:   int   =      tf.constant(
           [
               [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
               [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
               [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
           ]						)

       tf.debugging.assert_near(logits[0, :3, :3, :3]      ,		snake_case_      ,		rtol=1E-4      ,		atol=1E-4						)







'''simple docstring'''


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


snake_case__     :			Optional[int]      			=  logging.getLogger(__name__)


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'   )
     parser.add_argument(
         '--dataset_name'	,     type=lowerCamelCase_	,     default='wikitext'	,     help='Name of the training. Explore datasets at: hf.co/datasets.'	,     )
     parser.add_argument(
         '--dataset_config'	,     type=lowerCamelCase_	,     default='wikitext-103-raw-v1'	,     help='Configuration name of the dataset.'   )
     parser.add_argument(
         '--batch_size'	,     type=lowerCamelCase_	,     default=1000	,     help='Batch size during training.'	,     )
     parser.add_argument(
         '--vocab_size'	,     type=lowerCamelCase_	,     default=10048	,     help='Size of the desired vocabulary.'	,     )
     parser.add_argument(
         '--limit'	,     default=lowerCamelCase_	,     type=lowerCamelCase_	,     help='Limit the number of shards (used for debugging).'	,     )
     parser.add_argument(
         '--export_to_hub'	,     action='store_true'	,     )

     UpperCAmelCase_							:   str   =      parser.parse_args()
     return args


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict   ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      datasets.load_dataset(args.dataset_name	,     args.dataset_config	,     split='train'   )

     if args.limit is not None:
          UpperCAmelCase_							:   Union[str, Any]   =      min(len(lowerCamelCase_   )	,     args.limit   )
          UpperCAmelCase_							:   int   =      dataset.select(range(lowerCamelCase_   )   )
          logger.info(F'''Limiting the dataset to {args.limit} entries.'''   )

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

    # Prepare the tokenizer.
     UpperCAmelCase_							:   Optional[Any]   =      Tokenizer(Unigram()   )
     UpperCAmelCase_							:   Union[str, Any]   =      normalizers.Sequence([normalizers.Replace('``'	,     '"'   ), normalizers.Replace('\'\''	,     '"'   )]   )
     UpperCAmelCase_							:   Any   =      pre_tokenizers.Metaspace()

     # Prepare the trainer.
     UpperCAmelCase_							:   Tuple   =      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=lowerCamelCase_   )
     logger.info('Tokenizer training complete!'   )

     UpperCAmelCase_							:   List[str]   =      tokenizer.token_to_id('[CLS]'   )
     UpperCAmelCase_							:   Optional[int]   =      tokenizer.token_to_id('[SEP]'   )
     UpperCAmelCase_							:   Optional[Any]   =      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),
         ]	,     )
     UpperCAmelCase_							:   Any   =      decoders.Metaspace()

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


if __name__ == "__main__":
 snake_case__     :			Optional[Any]      			=  parse_args()
 main(args)






