
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


lowercase_       :						List[str]     					=	get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
lowercase_       :						str     					=	get_tests_dir('''fixtures/vocab.json''')
lowercase_       :						Any     					=	get_tests_dir('''fixtures''')
class 			UpperCamelCase      (							unittest.TestCase  ):
     A__      =			["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = 0
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"				)
       self.assertIsInstance(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = WavaVecaConfig()
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"				)

         # save in new folder
         model_config.save_pretrained(snake_case__				)
         processor.save_pretrained(snake_case__				)

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoProcessor.from_pretrained(snake_case__				)

       self.assertIsInstance(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       with tempfile.TemporaryDirectory() as tmpdirname:
         # copy relevant files
         copyfile(snake_case__  ,     os.path.join(snake_case__  ,     snake_case__				)				)
         copyfile(snake_case__  ,     os.path.join(snake_case__  ,     "vocab.json"				)				)

         _SCREAMING_SNAKE_CASE   :				Tuple							    = AutoProcessor.from_pretrained(snake_case__				)

       self.assertIsInstance(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = WavaVecaFeatureExtractor()
         _SCREAMING_SNAKE_CASE   :				Tuple							    = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"				)

         _SCREAMING_SNAKE_CASE   :				Any							    = WavaVecaProcessor(snake_case__  ,     snake_case__				)

         # save in new folder
         processor.save_pretrained(snake_case__				)

         # drop `processor_class` in tokenizer
         with open(os.path.join(snake_case__  ,     snake_case__				)  ,     "r"				) as f:
           _SCREAMING_SNAKE_CASE   :				Dict							    = json.load(snake_case__				)
           config_dict.pop("processor_class"				)

         with open(os.path.join(snake_case__  ,     snake_case__				)  ,     "w"				) as f:
           f.write(json.dumps(snake_case__				)				)

         _SCREAMING_SNAKE_CASE   :				Dict							    = AutoProcessor.from_pretrained(snake_case__				)

       self.assertIsInstance(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = WavaVecaFeatureExtractor()
         _SCREAMING_SNAKE_CASE   :				Tuple							    = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"				)

         _SCREAMING_SNAKE_CASE   :				List[str]							    = WavaVecaProcessor(snake_case__  ,     snake_case__				)

         # save in new folder
         processor.save_pretrained(snake_case__				)

         # drop `processor_class` in feature extractor
         with open(os.path.join(snake_case__  ,     snake_case__				)  ,     "r"				) as f:
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = json.load(snake_case__				)
           config_dict.pop("processor_class"				)

         with open(os.path.join(snake_case__  ,     snake_case__				)  ,     "w"				) as f:
           f.write(json.dumps(snake_case__				)				)

         _SCREAMING_SNAKE_CASE   :				List[Any]							    = AutoProcessor.from_pretrained(snake_case__				)

       self.assertIsInstance(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       with tempfile.TemporaryDirectory() as tmpdirname:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = WavaVecaConfig(processor_class="Wav2Vec2Processor"				)
         model_config.save_pretrained(snake_case__				)
         # copy relevant files
         copyfile(snake_case__  ,     os.path.join(snake_case__  ,     "vocab.json"				)				)
         # create emtpy sample processor
         with open(os.path.join(snake_case__  ,     snake_case__				)  ,     "w"				) as f:
           f.write("{}"				)

         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = AutoProcessor.from_pretrained(snake_case__				)

       self.assertIsInstance(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       with self.assertRaises(snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Any							    = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"				)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Tuple							    = AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=snake_case__				)
       self.assertTrue(processor.special_attribute_present				)
       self.assertEqual(processor.__class__.__name__  ,     "NewProcessor"				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = processor.feature_extractor
       self.assertTrue(feature_extractor.special_attribute_present				)
       self.assertEqual(feature_extractor.__class__.__name__  ,     "NewFeatureExtractor"				)

       _SCREAMING_SNAKE_CASE   :				str							    = processor.tokenizer
       self.assertTrue(tokenizer.special_attribute_present				)
       if is_tokenizers_available():
         self.assertEqual(tokenizer.__class__.__name__  ,     "NewTokenizerFast"				)

         # Test we can also load the slow version
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=snake_case__  ,     use_fast=snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = new_processor.tokenizer
         self.assertTrue(new_tokenizer.special_attribute_present				)
         self.assertEqual(new_tokenizer.__class__.__name__  ,     "NewTokenizer"				)
       else:
         self.assertEqual(tokenizer.__class__.__name__  ,     "NewTokenizer"				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       try:
         AutoConfig.register("custom"  ,     snake_case__				)
         AutoFeatureExtractor.register(snake_case__  ,     snake_case__				)
         AutoTokenizer.register(snake_case__  ,     slow_tokenizer_class=snake_case__				)
         AutoProcessor.register(snake_case__  ,     snake_case__				)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(snake_case__				):
           AutoProcessor.register(snake_case__  ,     snake_case__				)

         # Now that the config is registered, it can be used as any other config with the auto-API
         _SCREAMING_SNAKE_CASE   :				List[str]							    = CustomFeatureExtractor.from_pretrained(snake_case__				)

         with tempfile.TemporaryDirectory() as tmp_dir:
           _SCREAMING_SNAKE_CASE   :				Any							    = os.path.join(snake_case__  ,     "vocab.txt"				)
           with open(snake_case__  ,     "w"  ,     encoding="utf-8"				) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]				)				)
           _SCREAMING_SNAKE_CASE   :				Any							    = CustomTokenizer(snake_case__				)

         _SCREAMING_SNAKE_CASE   :				int							    = CustomProcessor(snake_case__  ,     snake_case__				)

         with tempfile.TemporaryDirectory() as tmp_dir:
           processor.save_pretrained(snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Any							    = AutoProcessor.from_pretrained(snake_case__				)
           self.assertIsInstance(snake_case__  ,     snake_case__				)

       finally:
         if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
           del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
         if CustomConfig in TOKENIZER_MAPPING._extra_content:
           del TOKENIZER_MAPPING._extra_content[CustomConfig]
         if CustomConfig in PROCESSOR_MAPPING._extra_content:
           del PROCESSOR_MAPPING._extra_content[CustomConfig]
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
            A__      =			False
       class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
            A__      =			False
       class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
            A__      =			"""AutoFeatureExtractor"""
            A__      =			"""AutoTokenizer"""
            A__      =			False

       try:
         AutoConfig.register("custom"  ,     snake_case__				)
         AutoFeatureExtractor.register(snake_case__  ,     snake_case__				)
         AutoTokenizer.register(snake_case__  ,     slow_tokenizer_class=snake_case__				)
         AutoProcessor.register(snake_case__  ,     snake_case__				)
         # If remote code is not set, the default is to use local classes.
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"				)
         self.assertEqual(processor.__class__.__name__  ,     "NewProcessor"				)
         self.assertFalse(processor.special_attribute_present				)
         self.assertFalse(processor.feature_extractor.special_attribute_present				)
         self.assertFalse(processor.tokenizer.special_attribute_present				)

         # If remote code is disabled, we load the local ones.
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=snake_case__				)
         self.assertEqual(processor.__class__.__name__  ,     "NewProcessor"				)
         self.assertFalse(processor.special_attribute_present				)
         self.assertFalse(processor.feature_extractor.special_attribute_present				)
         self.assertFalse(processor.tokenizer.special_attribute_present				)

         # If remote is enabled, we load from the Hub.
         _SCREAMING_SNAKE_CASE   :				int							    = AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=snake_case__				)
         self.assertEqual(processor.__class__.__name__  ,     "NewProcessor"				)
         self.assertTrue(processor.special_attribute_present				)
         self.assertTrue(processor.feature_extractor.special_attribute_present				)
         self.assertTrue(processor.tokenizer.special_attribute_present				)

       finally:
         if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
           del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
         if CustomConfig in TOKENIZER_MAPPING._extra_content:
           del TOKENIZER_MAPPING._extra_content[CustomConfig]
         if CustomConfig in PROCESSOR_MAPPING._extra_content:
           del PROCESSOR_MAPPING._extra_content[CustomConfig]
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"				)
       self.assertEqual(processor.__class__.__name__  ,     "BertTokenizerFast"				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"				)
       self.assertEqual(processor.__class__.__name__  ,     "ConvNextImageProcessor"				)
@is_staging_test
class 			UpperCamelCase      (							unittest.TestCase  ):
     A__      =			["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
     @classmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = TOKEN
       HfFolder.save_token(snake_case__				)
     @classmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       try:
         delete_repo(token=cls._token  ,     repo_id="test-processor"				)
       except HTTPError:
         pass

       try:
         delete_repo(token=cls._token  ,     repo_id="valid_org/test-processor-org"				)
       except HTTPError:
         pass

       try:
         delete_repo(token=cls._token  ,     repo_id="test-dynamic-processor"				)
       except HTTPError:
         pass
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = WavaVecaProcessor.from_pretrained(snake_case__				)
       with tempfile.TemporaryDirectory() as tmp_dir:
         processor.save_pretrained(
             os.path.join(snake_case__  ,     "test-processor"				)  ,     push_to_hub=snake_case__  ,     use_auth_token=self._token				)

         _SCREAMING_SNAKE_CASE   :				List[str]							    = WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor'''				)
         for k, v in processor.feature_extractor.__dict__.items():
           self.assertEqual(snake_case__  ,     getattr(new_processor.feature_extractor  ,     snake_case__				)				)
         self.assertDictEqual(new_processor.tokenizer.get_vocab()  ,     processor.tokenizer.get_vocab()				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = WavaVecaProcessor.from_pretrained(snake_case__				)

       with tempfile.TemporaryDirectory() as tmp_dir:
         processor.save_pretrained(
             os.path.join(snake_case__  ,     "test-processor-org"				)  ,     push_to_hub=snake_case__  ,     use_auth_token=self._token  ,     organization="valid_org"  ,     )

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"				)
         for k, v in processor.feature_extractor.__dict__.items():
           self.assertEqual(snake_case__  ,     getattr(new_processor.feature_extractor  ,     snake_case__				)				)
         self.assertDictEqual(new_processor.tokenizer.get_vocab()  ,     processor.tokenizer.get_vocab()				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       CustomFeatureExtractor.register_for_auto_class()
       CustomTokenizer.register_for_auto_class()
       CustomProcessor.register_for_auto_class()

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = CustomFeatureExtractor.from_pretrained(snake_case__				)

       with tempfile.TemporaryDirectory() as tmp_dir:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = os.path.join(snake_case__  ,     "vocab.txt"				)
         with open(snake_case__  ,     "w"  ,     encoding="utf-8"				) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]				)				)
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = CustomTokenizer(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = CustomProcessor(snake_case__  ,     snake_case__				)

       with tempfile.TemporaryDirectory() as tmp_dir:
         create_repo(F'''{USER}/test-dynamic-processor'''  ,     token=self._token				)
         _SCREAMING_SNAKE_CASE   :				int							    = Repository(snake_case__  ,     clone_from=F'''{USER}/test-dynamic-processor'''  ,     token=self._token				)
         processor.save_pretrained(snake_case__				)

         # This has added the proper auto_map field to the feature extractor config
         self.assertDictEqual(
             processor.feature_extractor.auto_map  ,     {
                 "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
                 "AutoProcessor": "custom_processing.CustomProcessor",
             }  ,     )

         # This has added the proper auto_map field to the tokenizer config
         with open(os.path.join(snake_case__  ,     "tokenizer_config.json"				)				) as f:
           _SCREAMING_SNAKE_CASE   :				str							    = json.load(snake_case__				)
         self.assertDictEqual(
             tokenizer_config["auto_map"]  ,     {
                 "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
                 "AutoProcessor": "custom_processing.CustomProcessor",
             }  ,     )

         # The code has been copied from fixtures
         self.assertTrue(os.path.isfile(os.path.join(snake_case__  ,     "custom_feature_extraction.py"				)				)				)
         self.assertTrue(os.path.isfile(os.path.join(snake_case__  ,     "custom_tokenization.py"				)				)				)
         self.assertTrue(os.path.isfile(os.path.join(snake_case__  ,     "custom_processing.py"				)				)				)

         repo.push_to_hub()

       _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor'''  ,     trust_remote_code=snake_case__				)
       # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
       self.assertEqual(new_processor.__class__.__name__  ,     "CustomProcessor"				)





"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

  def is_too_big(lowerCamelCase__							:							List[Any]				):
    return tok(lowerCamelCase__,						return_tensors="pt"				).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]				):
    _SCREAMING_SNAKE_CASE   :				int							    = new_src + " " + src
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_tgt + " " + tgt
    if is_too_big(lowerCamelCase__				) or is_too_big(lowerCamelCase__				):  # cant fit, finalize example
      finished_src.append(lowerCamelCase__				)
      finished_tgt.append(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = src, tgt
    else:  # can fit, keep adding
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = cand_src, cand_tgt

    # cleanup
  if new_src:
    assert new_tgt
    finished_src.append(lowerCamelCase__				)
    finished_tgt.append(lowerCamelCase__				)
  return finished_src, finished_tgt





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Path,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[str]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = Path(lowerCamelCase__				)
  save_path.mkdir(exist_ok=lowerCamelCase__				)
  for split in ["train"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    _SCREAMING_SNAKE_CASE   :				Any							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE   :				int							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = pack_examples(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
    print(f'''packed {split} split from {len(lowerCamelCase__				)} examples -> {len(lowerCamelCase__				)}.'''				)
    Path(save_path / f'''{split}.source'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
    Path(save_path / f'''{split}.target'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
  for split in ["val", "test"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.source'''				)
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.target'''				)





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



