
"""simple docstring"""




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__   :					Tuple							=   get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class 			__magic_name__       (       UpperCamelCase__   ,					unittest.TestCase ):
       UpperCamelCase_   =		XLMRobertaTokenizer
       UpperCamelCase_   =		XLMRobertaTokenizerFast
       UpperCamelCase_   =		True
       UpperCamelCase_   =		True
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase:			Any       =     XLMRobertaTokenizer(A_ ,				keep_accents=A_       )
         tokenizer.save_pretrained(self.tmpdirname       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     '''<pad>'''
         _lowercase:			List[Any]       =     1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_       ) ,				A_       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_       ) ,				A_       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			str       =     list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0] ,				'''<s>'''       )
         self.assertEqual(vocab_keys[1] ,				'''<pad>'''       )
         self.assertEqual(vocab_keys[-1] ,				'''<mask>'''       )
         self.assertEqual(len(A_       ) ,				1002       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         self.assertEqual(self.get_tokenizer().vocab_size ,				1002       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			int       =     XLMRobertaTokenizer(A_ ,				keep_accents=A_       )

         _lowercase:			Any       =     tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(A_ ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(A_       ) ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,				)

         _lowercase:			Any       =     tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
         self.assertListEqual(
             A_ ,				[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''9''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''é''',
                 '''.''',
             ] ,				)
         _lowercase:			str       =     tokenizer.convert_tokens_to_ids(A_       )
         self.assertListEqual(
             A_ ,				[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ] ,				)

         _lowercase:			Optional[Any]       =     tokenizer.convert_ids_to_tokens(A_       )
         self.assertListEqual(
             A_ ,				[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''<unk>''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''<unk>''',
                 '''.''',
             ] ,				)
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         if not self.test_slow_tokenizer:
           # as we don't have a slow version, we can't compare the outputs between slow and fast versions
           return

         _lowercase:			Dict       =     (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
             _lowercase:			Any       =     self.rust_tokenizer_class.from_pretrained(A_ ,				**A_       )
             _lowercase:			Any       =     self.tokenizer_class.from_pretrained(A_ ,				**A_       )

             _lowercase:			Union[str, Any]       =     tempfile.mkdtemp()

             _lowercase:			Tuple       =     tokenizer_r.save_pretrained(A_       )
             _lowercase:			List[str]       =     tokenizer_p.save_pretrained(A_       )

             # Checks it save with the same files + the tokenizer.json file for the fast one
             self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )
             _lowercase:			List[Any]       =     tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
             self.assertSequenceEqual(A_ ,				A_       )

             # Checks everything loads correctly in the same way
             _lowercase:			List[Any]       =     tokenizer_r.from_pretrained(A_       )
             _lowercase:			str       =     tokenizer_p.from_pretrained(A_       )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(A_ ,				A_       )       )
               # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
               # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

             shutil.rmtree(A_       )

             # Save tokenizer rust, legacy_format=True
             _lowercase:			Optional[Any]       =     tempfile.mkdtemp()

             _lowercase:			List[Any]       =     tokenizer_r.save_pretrained(A_ ,				legacy_format=A_       )
             _lowercase:			Tuple       =     tokenizer_p.save_pretrained(A_       )

             # Checks it save with the same files
             self.assertSequenceEqual(A_ ,				A_       )

             # Checks everything loads correctly in the same way
             _lowercase:			List[str]       =     tokenizer_r.from_pretrained(A_       )
             _lowercase:			int       =     tokenizer_p.from_pretrained(A_       )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(A_ ,				A_       )       )

             shutil.rmtree(A_       )

             # Save tokenizer rust, legacy_format=False
             _lowercase:			Union[str, Any]       =     tempfile.mkdtemp()

             _lowercase:			List[Any]       =     tokenizer_r.save_pretrained(A_ ,				legacy_format=A_       )
             _lowercase:			Dict       =     tokenizer_p.save_pretrained(A_       )

             # Checks it saved the tokenizer.json file
             self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )

             # Checks everything loads correctly in the same way
             _lowercase:			int       =     tokenizer_r.from_pretrained(A_       )
             _lowercase:			Union[str, Any]       =     tokenizer_p.from_pretrained(A_       )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(A_ ,				A_       )       )

             shutil.rmtree(A_       )
       @cached_property
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         with tempfile.NamedTemporaryFile() as f:
           shutil.copyfile(A_ ,				f.name       )
           _lowercase:			str       =     XLMRobertaTokenizer(f.name ,				keep_accents=A_       )
           _lowercase:			List[Any]       =     pickle.dumps(A_       )
         pickle.loads(A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         if not self.test_rust_tokenizer:
           return

         _lowercase:			List[str]       =     self.get_tokenizer()
         _lowercase:			int       =     self.get_rust_tokenizer()

         _lowercase:			Dict       =     '''I was born in 92000, and this is falsé.'''

         _lowercase:			str       =     tokenizer.tokenize(A_       )
         _lowercase:			Union[str, Any]       =     rust_tokenizer.tokenize(A_       )
         self.assertListEqual(A_ ,				A_       )

         _lowercase:			Any       =     tokenizer.encode(A_ ,				add_special_tokens=A_       )
         _lowercase:			Any       =     rust_tokenizer.encode(A_ ,				add_special_tokens=A_       )
         self.assertListEqual(A_ ,				A_       )

         _lowercase:			Tuple       =     self.get_rust_tokenizer()
         _lowercase:			Optional[Any]       =     tokenizer.encode(A_       )
         _lowercase:			str       =     rust_tokenizer.encode(A_       )
         self.assertListEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Dict       =     '''Hello World!'''
         _lowercase:			Union[str, Any]       =     [0, 3_5378, 6661, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(A_ ,				self.big_tokenizer.encode(A_       )       )
       @slow
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Any       =     (
             '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'''
             ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
         )
         _lowercase:			Dict       =     [
             0,
             3293,
             83,
             10,
             4552,
             4989,
             7986,
             678,
             10,
             5915,
             111,
             17_9459,
             12_4850,
             4,
             6044,
             237,
             12,
             6,
             5,
             6,
             4,
             6780,
             705,
             15,
             1388,
             44,
             378,
             1_0114,
             711,
             152,
             20,
             6,
             5,
             2_2376,
             642,
             1221,
             1_5190,
             3_4153,
             450,
             5608,
             959,
             1119,
             5_7702,
             136,
             186,
             47,
             1098,
             2_9367,
             47,
             # 4426, # What fairseq tokenizes from "<unk>": "_<"
             # 3678, # What fairseq tokenizes from "<unk>": "unk"
             # 2740, # What fairseq tokenizes from "<unk>": ">"
             3,  # What we tokenize from "<unk>": "<unk>"
             6,  # Residue from the tokenization: an extra sentencepiece underline
             4,
             6044,
             237,
             6284,
             5_0901,
             528,
             31,
             90,
             34,
             927,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(A_ ,				self.big_tokenizer.encode(A_       )       )



       @slow
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=A_ ,				model_name='''xlm-roberta-base''' ,				revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' ,				)


"""simple docstring"""




import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


A__   :					Optional[Any]							=   '<<<<<<< This should probably be modified because it mentions: '

A__   :					Any							=   '=======\n>>>>>>>\n'

A__   :					int							=   [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A__   :					Union[str, Any]							=   [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return ConvertCommand(args.tfds_path ,				args.datasets_directory						)







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       @staticmethod
       def 							lowercase_							(     A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			int       =     parser.add_parser(
             '''convert''' ,				help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.''' ,				)
         train_parser.add_argument(
             '''--tfds_path''' ,				type=A_ ,				required=A_ ,				help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.''' ,				)
         train_parser.add_argument(
             '''--datasets_directory''' ,				type=A_ ,				required=A_ ,				help='''Path to the HuggingFace Datasets folder.'''       )
         train_parser.set_defaults(func=A_       )
       def __init__(     self ,				A_ ,				A_ ,				*A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     get_logger('''datasets-cli/converting'''       )

         _lowercase:			Optional[Any]       =     tfds_path
         _lowercase:			Dict       =     datasets_directory



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if os.path.isdir(self._tfds_path       ):
           _lowercase:			Optional[Any]       =     os.path.abspath(self._tfds_path       )
         elif os.path.isfile(self._tfds_path       ):
           _lowercase:			Any       =     os.path.dirname(self._tfds_path       )
         else:
           raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''       )

         _lowercase:			Dict       =     os.path.abspath(self._datasets_directory       )

         self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''       )

         _lowercase:			Tuple       =     []
         _lowercase:			List[str]       =     []
         _lowercase:			int       =     {}

         if os.path.isdir(self._tfds_path       ):
           _lowercase:			str       =     os.listdir(A_       )
         else:
           _lowercase:			Tuple       =     [os.path.basename(self._tfds_path       )]

         for f_name in file_names:
           self._logger.info(f'''Looking at file {f_name}'''       )
           _lowercase:			Optional[int]       =     os.path.join(A_ ,				A_       )
           _lowercase:			Optional[Any]       =     os.path.join(A_ ,				A_       )

           if not os.path.isfile(A_       ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
             self._logger.info('''Skipping file'''       )
             continue

           with open(A_ ,				encoding='''utf-8'''       ) as f:
             _lowercase:			Any       =     f.readlines()

           _lowercase:			str       =     []
           _lowercase:			Dict       =     False
           _lowercase:			Any       =     False
           _lowercase:			Tuple       =     []
           for line in lines:
             _lowercase:			str       =     line

             # Convert imports
             if "import tensorflow.compat.v2 as tf" in out_line:
               continue
             elif "@tfds.core" in out_line:
               continue
             elif "builder=self" in out_line:
               continue
             elif "import tensorflow_datasets.public_api as tfds" in out_line:
               _lowercase:			Optional[Any]       =     '''import datasets\n'''
             elif "import tensorflow" in out_line:
               # order is important here
               _lowercase:			Any       =     ''''''
               continue
             elif "from absl import logging" in out_line:
               _lowercase:			Dict       =     '''from datasets import logging\n'''
             elif "getLogger" in out_line:
               _lowercase:			Optional[Any]       =     out_line.replace('''getLogger''' ,				'''get_logger'''       )
             elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
               _lowercase:			int       =     True
               _lowercase:			List[str]       =     list(filter(lambda A_       : e in out_line ,				A_       )       )
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(A_       ) + '''\n'''       )
               out_lines.append(A_       )
               out_lines.append(A_       )
               continue
             else:
               for pattern, replacement in TO_CONVERT:
                 _lowercase:			Tuple       =     re.sub(A_ ,				A_ ,				A_       )

                # Take care of saving utilities (to later move them together with main script)
             if "tensorflow_datasets" in out_line:
               _lowercase:			Optional[int]       =     re.match(R'''from\stensorflow_datasets.*import\s([^\.\r\n]+)''' ,				A_       )
               tfds_imports.extend(imp.strip() for imp in match.group(1       ).split(''','''       )       )
               _lowercase:			Dict       =     '''from . import ''' + match.group(1       )

             # Check we have not forget anything
             if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(f'''Error converting {out_line.strip()}'''       )

             if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               _lowercase:			Tuple       =     True
             out_lines.append(A_       )

           if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             _lowercase:			List[str]       =     f_name.replace('''.py''' ,				''''''       )
             _lowercase:			Dict       =     os.path.join(A_ ,				A_       )
             _lowercase:			Dict       =     os.path.join(A_ ,				A_       )
             os.makedirs(A_ ,				exist_ok=A_       )
             self._logger.info(f'''Adding directory {output_dir}'''       )
             imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}       )
           else:
             # Utilities will be moved at the end
             utils_files.append(A_       )

           if needs_manual_update:
             with_manual_update.append(A_       )

           with open(A_ ,				'''w''' ,				encoding='''utf-8'''       ) as f:
             f.writelines(A_       )
           self._logger.info(f'''Converted in {output_file}'''       )

         for utils_file in utils_files:
           try:
             _lowercase:			Optional[int]       =     os.path.basename(A_       )
             _lowercase:			List[Any]       =     imports_to_builder_map[f_name.replace('''.py''' ,				''''''       )]
             self._logger.info(f'''Moving {dest_folder} to {utils_file}'''       )
             shutil.copy(A_ ,				A_       )
           except KeyError:
             self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.'''       )

         if with_manual_update:
           for file_path in with_manual_update:
             self._logger.warning(
                 f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''       )
