

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
 import faiss



@require_faiss
class    _a						(__magic_name__    ):


     '''simple docstring'''






     def   __A      (			self      ):
      A__							:   List[Any]					       =		tempfile.mkdtemp()
      A__							:   Any					       =		8

      # DPR tok
      A__							:   str					       =		[
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      A__							:   int					       =		os.path.join(self.tmpdirname   ,	"""dpr_tokenizer"""      )
      os.makedirs(A__   ,	exist_ok=A__      )
      A__							:   List[Any]					       =		os.path.join(A__   ,	DPR_VOCAB_FILES_NAMES["""vocab_file"""]      )
      with open(self.vocab_file   ,	"""w"""   ,	encoding="""utf-8"""      ) as vocab_writer:
       vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )

      # BART tok
      A__							:   Dict					       =		[
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      A__							:   Any					       =		dict(zip(A__   ,	range(len(A__      )      )      )      )
      A__							:   Dict					       =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      A__							:   Dict					       =		{"""unk_token""": """<unk>"""}

      A__							:   Dict					       =		os.path.join(self.tmpdirname   ,	"""bart_tokenizer"""      )
      os.makedirs(A__   ,	exist_ok=A__      )
      A__							:   Optional[Any]					       =		os.path.join(A__   ,	BART_VOCAB_FILES_NAMES["""vocab_file"""]      )
      A__							:   Optional[Any]					       =		os.path.join(A__   ,	BART_VOCAB_FILES_NAMES["""merges_file"""]      )
      with open(self.vocab_file   ,	"""w"""   ,	encoding="""utf-8"""      ) as fp:
       fp.write(json.dumps(A__      ) + """\n"""      )
      with open(self.merges_file   ,	"""w"""   ,	encoding="""utf-8"""      ) as fp:
       fp.write("""\n""".join(A__      )      )






     def   __A      (			self      ):
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,	"""dpr_tokenizer"""      )      )






     def   __A      (			self      ):
      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,	"""dpr_tokenizer"""      )      )






     def   __A      (			self      ):
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,	"""bart_tokenizer"""      )      )






     def   __A      (			self      ):
      shutil.rmtree(self.tmpdirname      )






     def   __A      (			self      ):
      A__							:   List[str]					       =		Dataset.from_dict(
          {
              """id""": ["""0""", """1"""],
              """text""": ["""foo""", """bar"""],
              """title""": ["""Foo""", """Bar"""],
              """embeddings""": [np.ones(self.retrieval_vector_size      ), 2 * np.ones(self.retrieval_vector_size      )],
          }      )
      dataset.add_faiss_index("""embeddings"""   ,	string_factory="""Flat"""   ,	metric_type=faiss.METRIC_INNER_PRODUCT      )
      return dataset






     def   __A      (			self      ):
      A__							:   int					       =		self.get_dummy_dataset()
      A__							:   Optional[Any]					       =		RagConfig(
          retrieval_vector_size=self.retrieval_vector_size   ,	question_encoder=DPRConfig().to_dict()   ,	generator=BartConfig().to_dict()   ,	)
      with patch("""transformers.models.rag.retrieval_rag.load_dataset"""      ) as mock_load_dataset:
       A__							:   Dict					       =		dataset
       A__							:   Tuple					       =		RagRetriever(
           A__   ,	question_encoder_tokenizer=self.get_dpr_tokenizer()   ,	generator_tokenizer=self.get_bart_tokenizer()   ,	)
      return retriever






     def   __A      (			self   ,	A__      ):
      A__							:   Any					       =		self.get_dummy_dataset()
      A__							:   Dict					       =		RagConfig(
          retrieval_vector_size=self.retrieval_vector_size   ,	question_encoder=DPRConfig().to_dict()   ,	generator=BartConfig().to_dict()   ,	index_name="""custom"""   ,	)
      if from_disk:
       A__							:   Optional[Any]					       =		os.path.join(self.tmpdirname   ,	"""dataset"""      )
       A__							:   Tuple					       =		os.path.join(self.tmpdirname   ,	"""index.faiss"""      )
       dataset.get_index("""embeddings"""      ).save(os.path.join(self.tmpdirname   ,	"""index.faiss"""      )      )
       dataset.drop_index("""embeddings"""      )
       dataset.save_to_disk(os.path.join(self.tmpdirname   ,	"""dataset"""      )      )
       del dataset
       A__							:   Optional[Any]					       =		RagRetriever(
           A__   ,	question_encoder_tokenizer=self.get_dpr_tokenizer()   ,	generator_tokenizer=self.get_bart_tokenizer()   ,	)
      else:
       A__							:   Tuple					       =		RagRetriever(
           A__   ,	question_encoder_tokenizer=self.get_dpr_tokenizer()   ,	generator_tokenizer=self.get_bart_tokenizer()   ,	index=CustomHFIndex(config.retrieval_vector_size   ,	A__      )   ,	)
      return retriever






     def   __A      (			self      ):
      A__							:   Union[str, Any]					       =		Dataset.from_dict(
          {
              """id""": ["""0""", """1"""],
              """text""": ["""foo""", """bar"""],
              """title""": ["""Foo""", """Bar"""],
              """embeddings""": [np.ones(self.retrieval_vector_size + 1      ), 2 * np.ones(self.retrieval_vector_size + 1      )],
          }      )
      dataset.add_faiss_index("""embeddings"""   ,	string_factory="""Flat"""   ,	metric_type=faiss.METRIC_INNER_PRODUCT      )

      A__							:   List[str]					       =		os.path.join(self.tmpdirname   ,	"""hf_bert_base.hnswSQ8_correct_phi_128.c_index"""      )
      dataset.save_faiss_index("""embeddings"""   ,	index_file_name + """.index.dpr"""      )
      pickle.dump(dataset["""id"""]   ,	open(index_file_name + """.index_meta.dpr"""   ,	"""wb"""      )      )

      A__							:   Tuple					       =		os.path.join(self.tmpdirname   ,	"""psgs_w100.tsv.pkl"""      )
      A__							:   Any					       =		{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
      pickle.dump(A__   ,	open(A__   ,	"""wb"""      )      )

      A__							:   int					       =		RagConfig(
          retrieval_vector_size=self.retrieval_vector_size   ,	question_encoder=DPRConfig().to_dict()   ,	generator=BartConfig().to_dict()   ,	index_name="""legacy"""   ,	index_path=self.tmpdirname   ,	)
      A__							:   Any					       =		RagRetriever(
          A__   ,	question_encoder_tokenizer=self.get_dpr_tokenizer()   ,	generator_tokenizer=self.get_bart_tokenizer()      )
      return retriever






     def   __A      (			self      ):
      A__							:   Optional[Any]					       =		1
      A__							:   Tuple					       =		self.get_dummy_canonical_hf_index_retriever()
      A__							:   Optional[int]					       =		np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
      A__							:   Dict					       =		retriever.retrieve(A__   ,	n_docs=A__      )
      self.assertEqual(retrieved_doc_embeds.shape   ,	(2, n_docs, self.retrieval_vector_size)      )
      self.assertEqual(len(A__      )   ,	2      )
      self.assertEqual(sorted(doc_dicts[0]      )   ,	["""embeddings""", """id""", """text""", """title"""]      )
      self.assertEqual(len(doc_dicts[0]["""id"""]      )   ,	A__      )
      self.assertEqual(doc_dicts[0]["""id"""][0]   ,	"""1"""      )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]   ,	"""0"""      )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()   ,	[[1], [0]]      )






     def   __A      (			self      ):
      A__							:   int					       =		self.get_dummy_canonical_hf_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
       with patch("""transformers.models.rag.retrieval_rag.load_dataset"""      ) as mock_load_dataset:
        A__							:   Optional[int]					       =		self.get_dummy_dataset()
        retriever.save_pretrained(A__      )
        A__							:   Optional[Any]					       =		RagRetriever.from_pretrained(A__      )
        self.assertIsInstance(A__   ,	A__      )
        A__							:   Tuple					       =		np.array(
            [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
        A__							:   Any					       =		retriever.retrieve(A__   ,	n_docs=1      )
        self.assertTrue(out is not None      )






     def   __A      (			self      ):
      A__							:   Tuple					       =		1
      A__							:   Optional[int]					       =		self.get_dummy_custom_hf_index_retriever(from_disk=A__      )
      A__							:   Any					       =		np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
      A__							:   List[str]					       =		retriever.retrieve(A__   ,	n_docs=A__      )
      self.assertEqual(retrieved_doc_embeds.shape   ,	(2, n_docs, self.retrieval_vector_size)      )
      self.assertEqual(len(A__      )   ,	2      )
      self.assertEqual(sorted(doc_dicts[0]      )   ,	["""embeddings""", """id""", """text""", """title"""]      )
      self.assertEqual(len(doc_dicts[0]["""id"""]      )   ,	A__      )
      self.assertEqual(doc_dicts[0]["""id"""][0]   ,	"""1"""      )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]   ,	"""0"""      )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()   ,	[[1], [0]]      )






     def   __A      (			self      ):
      A__							:   Optional[Any]					       =		self.get_dummy_custom_hf_index_retriever(from_disk=A__      )
      with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(A__      )
       A__							:   Tuple					       =		RagRetriever.from_pretrained(A__      )
       self.assertIsInstance(A__   ,	A__      )
       A__							:   Optional[Any]					       =		np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
       A__							:   Dict					       =		retriever.retrieve(A__   ,	n_docs=1      )
       self.assertTrue(out is not None      )






     def   __A      (			self      ):
      A__							:   Tuple					       =		1
      A__							:   List[str]					       =		self.get_dummy_custom_hf_index_retriever(from_disk=A__      )
      A__							:   int					       =		np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
      A__							:   Tuple					       =		retriever.retrieve(A__   ,	n_docs=A__      )
      self.assertEqual(retrieved_doc_embeds.shape   ,	(2, n_docs, self.retrieval_vector_size)      )
      self.assertEqual(len(A__      )   ,	2      )
      self.assertEqual(sorted(doc_dicts[0]      )   ,	["""embeddings""", """id""", """text""", """title"""]      )
      self.assertEqual(len(doc_dicts[0]["""id"""]      )   ,	A__      )
      self.assertEqual(doc_dicts[0]["""id"""][0]   ,	"""1"""      )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]   ,	"""0"""      )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()   ,	[[1], [0]]      )






     def   __A      (			self      ):
      A__							:   Any					       =		self.get_dummy_custom_hf_index_retriever(from_disk=A__      )
      with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(A__      )
       A__							:   Optional[int]					       =		RagRetriever.from_pretrained(A__      )
       self.assertIsInstance(A__   ,	A__      )
       A__							:   int					       =		np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
       A__							:   Union[str, Any]					       =		retriever.retrieve(A__   ,	n_docs=1      )
       self.assertTrue(out is not None      )






     def   __A      (			self      ):
      A__							:   Optional[int]					       =		1
      A__							:   List[Any]					       =		self.get_dummy_legacy_index_retriever()
      A__							:   Dict					       =		np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
      A__							:   Optional[int]					       =		retriever.retrieve(A__   ,	n_docs=A__      )
      self.assertEqual(retrieved_doc_embeds.shape   ,	(2, n_docs, self.retrieval_vector_size)      )
      self.assertEqual(len(A__      )   ,	2      )
      self.assertEqual(sorted(doc_dicts[0]      )   ,	["""text""", """title"""]      )
      self.assertEqual(len(doc_dicts[0]["""text"""]      )   ,	A__      )
      self.assertEqual(doc_dicts[0]["""text"""][0]   ,	"""bar"""      )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""text"""][0]   ,	"""foo"""      )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()   ,	[[1], [0]]      )






     def   __A      (			self      ):
      A__							:   List[Any]					       =		self.get_dummy_legacy_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(A__      )
       A__							:   Tuple					       =		RagRetriever.from_pretrained(A__      )
       self.assertIsInstance(A__   ,	A__      )
       A__							:   Tuple					       =		np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
       A__							:   Dict					       =		retriever.retrieve(A__   ,	n_docs=1      )
       self.assertTrue(out is not None      )






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def   __A      (			self      ):
      import torch

      A__							:   str					       =		1
      A__							:   Optional[int]					       =		self.get_dummy_canonical_hf_index_retriever()
      A__							:   Optional[Any]					       =		[[5, 7], [10, 11]]
      A__							:   Union[str, Any]					       =		np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
      A__							:   Tuple					       =		retriever(A__   ,	A__   ,	prefix=retriever.config.generator.prefix   ,	n_docs=A__      )
      A__							:   Tuple					       =		(
          out["""context_input_ids"""],
          out["""context_attention_mask"""],
          out["""retrieved_doc_embeds"""],
      )
      self.assertEqual(retrieved_doc_embeds.shape   ,	(2, n_docs, self.retrieval_vector_size)      )
      self.assertIsInstance(A__   ,	A__      )
      self.assertIsInstance(A__   ,	A__      )
      self.assertIsInstance(A__   ,	np.ndarray      )

      A__							:   List[str]					       =		retriever(
          A__   ,	A__   ,	prefix=retriever.config.generator.prefix   ,	n_docs=A__   ,	return_tensors="""pt"""   ,	)
      A__							:   Optional[Any]					       =		(  # noqa: F841
          out["""context_input_ids"""],
          out["""context_attention_mask"""],
          out["""retrieved_doc_embeds"""],
          out["""doc_ids"""],
      )
      self.assertEqual(retrieved_doc_embeds.shape   ,	(2, n_docs, self.retrieval_vector_size)      )
      self.assertIsInstance(A__   ,	torch.Tensor      )
      self.assertIsInstance(A__   ,	torch.Tensor      )
      self.assertIsInstance(A__   ,	torch.Tensor      )






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def   __A      (			self      ):
      A__							:   Any					       =		self.get_dpr_ctx_encoder_tokenizer()
      A__							:   Optional[int]					       =		1
      A__							:   Any					       =		self.get_dummy_custom_hf_index_retriever(from_disk=A__      )
      retriever.set_ctx_encoder_tokenizer(A__      )

      A__							:   List[str]					       =		[[5, 7], [10, 11]]
      A__							:   List[Any]					       =		np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]   ,	dtype=np.floataa      )
      A__							:   Tuple					       =		retriever(A__   ,	A__   ,	prefix=retriever.config.generator.prefix   ,	n_docs=A__      )

      self.assertEqual(
          len(A__      )   ,	6      )  # check whether the retriever output consist of 6 attributes including tokenized docs
      self.assertEqual(
          all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")      )   ,	A__      )  # check for doc token related keys in dictionary.






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[int]  	=      '<<<<<<< This should probably be modified because it mentions: '

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[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 						UpperCamelCase (lowercase_:			Namespace )					->				str:
	return ConvertCommand(args.tfds_path   ,				args.datasets_directory )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		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__      ):
						A__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

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






					def   __A      (			self      ):
						if os.path.isdir(self._tfds_path      ):
							A__							:   Any					       =		os.path.abspath(self._tfds_path      )
						elif os.path.isfile(self._tfds_path      ):
							A__							:   int					       =		os.path.dirname(self._tfds_path      )
						else:
							raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""      )

						A__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

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

						A__							:   Optional[Any]					       =		[]
						A__							:   List[Any]					       =		[]
						A__							:   List[Any]					       =		{}

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[Any]					       =		[os.path.basename(self._tfds_path      )]

						for f_name in file_names:
							self._logger.info(F"""Looking at file {f_name}"""      )
							A__							:   Any					       =		os.path.join(A__   ,	A__      )
							A__							:   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:
								A__							:   Dict					       =		f.readlines()

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

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

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



