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

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


_lowercase											=    """<<<<<<< This should probably be modified because it mentions: """

_lowercase											=    """=======
>>>>>>>
"""

_lowercase											=    [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

_lowercase											=    [
    # (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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Namespace				)			->						Any:
       return ConvertCommand(args.tfds_path			,							args.datasets_directory				)


class 			lowercase_		(      A       ):


       @staticmethod
       def 	_snake_case				(    __A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=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				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Tuple		=get_logger('''datasets-cli/converting'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tfds_path
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=datasets_directory





       def 	_snake_case				(    self				)						->    str:
              if os.path.isdir(self._tfds_path				):
                     SCREAMING_SNAKE_CASE_							:				Tuple		=os.path.abspath(self._tfds_path				)
              elif os.path.isfile(self._tfds_path				):
                     SCREAMING_SNAKE_CASE_							:				Any		=os.path.dirname(self._tfds_path				)
              else:
                     raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''				)

              SCREAMING_SNAKE_CASE_							:				int		=os.path.abspath(self._datasets_directory				)

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

              SCREAMING_SNAKE_CASE_							:				Dict		=[]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[]
              SCREAMING_SNAKE_CASE_							:				Tuple		={}

              if os.path.isdir(self._tfds_path				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=os.listdir(__A				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Any		=[os.path.basename(self._tfds_path				)]

              for f_name in file_names:
                     self._logger.info(F'Looking at file {f_name}'				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=os.path.join(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				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:
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=f.readlines()

                     SCREAMING_SNAKE_CASE_							:				str		=[]
                     SCREAMING_SNAKE_CASE_							:				Dict		=False
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=False
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]
                     for line in lines:
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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:
                                   SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''import datasets\n'''
                            elif "import tensorflow" in out_line:
                                   # order is important here
                                   SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=''''''
                                   continue
                            elif "from absl import logging" in out_line:
                                   SCREAMING_SNAKE_CASE_							:				Dict		='''from datasets import logging\n'''
                            elif "getLogger" in out_line:
                                   SCREAMING_SNAKE_CASE_							:				Tuple		=out_line.replace('''getLogger'''						,       '''get_logger'''				)
                            elif any(expression in out_line for expression in TO_HIGHLIGHT				):
                                   SCREAMING_SNAKE_CASE_							:				Any		=True
                                   SCREAMING_SNAKE_CASE_							:				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:
                                          SCREAMING_SNAKE_CASE_							:				List[Any]		=re.sub(__A						,       __A						,       __A				)

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

                     if is_builder or "wmt" in f_name:
                            # We create a new directory for each dataset
                            SCREAMING_SNAKE_CASE_							:				str		=f_name.replace('''.py'''						,       ''''''				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=os.path.join(__A						,       __A				)
                            SCREAMING_SNAKE_CASE_							:				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:
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=os.path.basename(__A				)
                            SCREAMING_SNAKE_CASE_							:				int		=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\'.'				)


import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase											=    """▁"""

_lowercase											=    get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BigBirdTokenizer
       __lowerCamelCase				       =						BigBirdTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    Tuple:
              super().setUp()

              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer_class(__A						,       keep_accents=__A				)
              tokenizer.save_pretrained(self.tmpdirname				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		='''<s>'''
              SCREAMING_SNAKE_CASE_							:				Union[str, 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 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		=list(self.get_tokenizer().get_vocab().keys()				)

              self.assertEqual(vocab_keys[0]						,       '''<unk>'''				)
              self.assertEqual(vocab_keys[1]						,       '''<s>'''				)
              self.assertEqual(vocab_keys[-1]						,       '''[MASK]'''				)
              self.assertEqual(len(__A				)						,       1_004				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              self.assertEqual(self.get_tokenizer().vocab_size						,       1_000				)


       def 	_snake_case				(    self				)						->    str:
              if not self.test_rust_tokenizer:
                     return

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_tokenizer()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_rust_tokenizer()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''I was born in 92000, and this is falsé.'''

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rust_tokenizer.tokenize(__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode(__A						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=rust_tokenizer.encode(__A						,       add_special_tokens=__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_rust_tokenizer()
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=rust_tokenizer.encode(__A				)
              self.assertListEqual(__A						,       __A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=BigBirdTokenizer(__A						,       keep_accents=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.tokenize('''This is a test'''				)
              self.assertListEqual(__A						,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(__A				)						,       [285, 46, 10, 170, 382]						,       )

              SCREAMING_SNAKE_CASE_							:				List[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''',
                      '''.''',
                  ]						,       )
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.convert_tokens_to_ids(__A				)
              self.assertListEqual(
                  __A						,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,       )

              SCREAMING_SNAKE_CASE_							:				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>''',
                      '''.''',
                  ]						,       )


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		='''Hello World!'''
              SCREAMING_SNAKE_CASE_							:				Dict		=[65, 18_536, 2_260, 101, 66]

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


       @slow
       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, 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'''
              )
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
              # fmt: on
              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @require_torch
       @slow
       def 	_snake_case				(    self				)						->    int:
              import torch

              from transformers import BigBirdConfig, BigBirdModel

              # Build sequence
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(self.big_tokenizer.get_vocab().keys()				)[:10]
              SCREAMING_SNAKE_CASE_							:				Any		=''' '''.join(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=self.big_tokenizer.encode_plus(__A						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.big_tokenizer.batch_encode_plus(
                  [sequence + ''' ''' + sequence]						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BigBirdConfig(attention_type='''original_full'''				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=BigBirdModel(__A				)

              assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

              with torch.no_grad():
                     model(**__A				)
                     model(**__A				)


       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Any		=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.decode(tokenizer('''Paris is the [MASK].'''				).input_ids				)

              self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''				)





       @slow
       def 	_snake_case				(    self				)						->    Any:
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''input_ids''': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 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], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 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]], '''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, 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, 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='''google/bigbird-roberta-base'''						,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''						,       )
