





'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {'''vocab_file''': '''spm_char.model'''}

_SCREAMING_SNAKE_CASE					=    {
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_SCREAMING_SNAKE_CASE					=    {
    '''microsoft/speecht5_asr''': 1_0_2_4,
    '''microsoft/speecht5_tts''': 1_0_2_4,
    '''microsoft/speecht5_vc''': 1_0_2_4,
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Any											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				str											=       ["input_ids", "attention_mask"]


	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase="<s>"     ,_lowerCamelCase="</s>"     ,_lowerCamelCase="<unk>"     ,_lowerCamelCase="<pad>"     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=_lowerCamelCase     ,eos_token=_lowerCamelCase     ,unk_token=_lowerCamelCase     ,pad_token=_lowerCamelCase     ,sp_model_kwargs=self.sp_model_kwargs     ,**_lowerCamelCase     ,)

				__lowercase										=							vocab_file

				__lowercase										=							spm.SentencePieceProcessor(**self.sp_model_kwargs )
				self.sp_model.Load(_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				return self.sp_model.get_piece_size()


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							{self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
				vocab.update(self.added_tokens_encoder )
				return vocab


	def __getstate__(self )							->							int:







				'''simple docstring'''





				__lowercase										=							self.__dict__.copy()
				__lowercase										=							None
				return state


	def __setstate__(self     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							d

				# for backward compatibility
				if not hasattr(self     ,'''sp_model_kwargs''' ):
							__lowercase										=							{}

				__lowercase										=							spm.SentencePieceProcessor(**self.sp_model_kwargs )
				self.sp_model.Load(self.vocab_file )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				return self.sp_model.encode(_lowerCamelCase     ,out_type=_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				return self.sp_model.piece_to_id(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.sp_model.IdToPiece(_lowerCamelCase )
				return token


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				__lowercase										=							[]
				__lowercase										=							''''''
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										out_string += self.sp_model.decode(_lowerCamelCase ) + token
										__lowercase										=							[]
							else:
										current_sub_tokens.append(_lowerCamelCase )
				out_string += self.sp_model.decode(_lowerCamelCase )
				return out_string.strip()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=None )							->							List[int]:







				'''simple docstring'''





				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = False )							->							List[int]:







				'''simple docstring'''





				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=_lowerCamelCase     ,token_ids_a=_lowerCamelCase     ,already_has_special_tokens=_lowerCamelCase )

				__lowercase										=							[1]
				if token_ids_a is None:
							return ([0] * len(_lowerCamelCase )) + suffix_ones
				return ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							Tuple[str]:







				'''simple docstring'''





				if not os.path.isdir(_lowerCamelCase ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
							return
				__lowercase										=							os.path.join(
				    _lowerCamelCase     ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

				if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
							copyfile(self.vocab_file     ,_lowerCamelCase )
				elif not os.path.isfile(self.vocab_file ):
							with open(_lowerCamelCase     ,'''wb''' ) as fi:
										__lowercase										=							self.sp_model.serialized_model_proto()
										fi.write(_lowerCamelCase )

				return (out_vocab_file,)












'''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


_SCREAMING_SNAKE_CASE					=    '''<<<<<<< This should probably be modified because it mentions: '''

_SCREAMING_SNAKE_CASE					=    '''=======
>>>>>>>
'''

_SCREAMING_SNAKE_CASE					=    [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

_SCREAMING_SNAKE_CASE					=    [
    # (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						(				lowerCamelCase_			:  Namespace				):
			return ConvertCommand(args.tfds_path		,       args.datasets_directory				)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	@staticmethod
	def 						_UpperCAmelCase     (_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							parser.add_parser(
				    '''convert'''     ,help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''     ,)
				train_parser.add_argument(
				    '''--tfds_path'''     ,type=_lowerCamelCase     ,required=_lowerCamelCase     ,help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''     ,)
				train_parser.add_argument(
				    '''--datasets_directory'''     ,type=_lowerCamelCase     ,required=_lowerCamelCase     ,help='''Path to the HuggingFace Datasets folder.''' )
				train_parser.set_defaults(func=_lowerCamelCase )


	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,*_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							get_logger('''datasets-cli/converting''' )

				__lowercase										=							tfds_path
				__lowercase										=							datasets_directory


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





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

				__lowercase										=							os.path.abspath(self._datasets_directory )

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

				__lowercase										=							[]
				__lowercase										=							[]
				__lowercase										=							{}

				if os.path.isdir(self._tfds_path ):
							__lowercase										=							os.listdir(_lowerCamelCase )
				else:
							__lowercase										=							[os.path.basename(self._tfds_path )]

				for f_name in file_names:
							self._logger.info(f"Looking at file {f_name}" )
							__lowercase										=							os.path.join(_lowerCamelCase     ,_lowerCamelCase )
							__lowercase										=							os.path.join(_lowerCamelCase     ,_lowerCamelCase )

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

							with open(_lowerCamelCase     ,encoding='''utf-8''' ) as f:
										__lowercase										=							f.readlines()

							__lowercase										=							[]
							__lowercase										=							False
							__lowercase										=							False
							__lowercase										=							[]
							for line in lines:
										__lowercase										=							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										=							'''import datasets\n'''
										elif "import tensorflow" in out_line:
													# order is important here
													__lowercase										=							''''''
													continue
										elif "from absl import logging" in out_line:
													__lowercase										=							'''from datasets import logging\n'''
										elif "getLogger" in out_line:
													__lowercase										=							out_line.replace('''getLogger'''     ,'''get_logger''' )
										elif any(expression in out_line for expression in TO_HIGHLIGHT ):
													__lowercase										=							True
													__lowercase										=							list(filter(lambda _lowerCamelCase : e in out_line     ,_lowerCamelCase ) )
													out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_lowerCamelCase ) + '''\n''' )
													out_lines.append(_lowerCamelCase )
													out_lines.append(_lowerCamelCase )
													continue
										else:
													for pattern, replacement in TO_CONVERT:
																__lowercase										=							re.sub(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )

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

							if is_builder or "wmt" in f_name:
										# We create a new directory for each dataset
										__lowercase										=							f_name.replace('''.py'''     ,'''''' )
										__lowercase										=							os.path.join(_lowerCamelCase     ,_lowerCamelCase )
										__lowercase										=							os.path.join(_lowerCamelCase     ,_lowerCamelCase )
										os.makedirs(_lowerCamelCase     ,exist_ok=_lowerCamelCase )
										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(_lowerCamelCase )

							if needs_manual_update:
										with_manual_update.append(_lowerCamelCase )

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

				for utils_file in utils_files:
							try:
										__lowercase										=							os.path.basename(_lowerCamelCase )
										__lowercase										=							imports_to_builder_map[f_name.replace('''.py'''     ,'''''' )]
										self._logger.info(f"Moving {dest_folder} to {utils_file}" )
										shutil.copy(_lowerCamelCase     ,_lowerCamelCase )
							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'." )





