


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							'▁'

lowerCAmelCase	=							{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase	=							{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCAmelCase	=							{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowerCAmelCase	=							['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			VOCAB_FILES_NAMES
							_lowercase     :      Union[str, Any]  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase     :      Union[str, Any]  				=			PRETRAINED_VOCAB_FILES_MAP
							_lowercase     :      Optional[int]  				=			['''input_ids''', '''attention_mask''']

							_lowercase     :      List[int]  				=			[]
							_lowercase     :      List[int]  				=			[]

							def __init__(							self:     List[str]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     List[str]="<s>"   ,						UpperCamelCase_:     str="</s>"   ,						UpperCamelCase_:     Union[str, Any]="</s>"   ,						UpperCamelCase_:     List[Any]="<s>"   ,						UpperCamelCase_:     Any="<unk>"   ,						UpperCamelCase_:     Optional[Any]="<pad>"   ,						UpperCamelCase_:     str="<mask>"   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     Any=None   ,						UpperCamelCase_:     Tuple=None   ,						UpperCamelCase_:     Optional[Dict[str, Any]] = None   ,						UpperCamelCase_:     str=None   ,						**UpperCamelCase_:     List[Any]   ,						)      ->       str:





									"""simple docstring"""







									lowercase__						=				AddedToken(UpperCamelCase_   ,						lstrip=UpperCamelCase_   ,						rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_   ,						UpperCamelCase_							) else mask_token

									lowercase__						=				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=UpperCamelCase_   ,						eos_token=UpperCamelCase_   ,						unk_token=UpperCamelCase_   ,						sep_token=UpperCamelCase_   ,						cls_token=UpperCamelCase_   ,						pad_token=UpperCamelCase_   ,						mask_token=UpperCamelCase_   ,						tokenizer_file=UpperCamelCase_   ,						src_lang=UpperCamelCase_   ,						tgt_lang=UpperCamelCase_   ,						additional_special_tokens=UpperCamelCase_   ,						sp_model_kwargs=self.sp_model_kwargs   ,						**UpperCamelCase_   ,						)

									lowercase__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(str(UpperCamelCase_							)							)
									lowercase__						=				vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									lowercase__						=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									lowercase__						=				1

									lowercase__						=				len(self.sp_model							)
									lowercase__						=				{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_							)
									}
									lowercase__						=				{v: k for k, v in self.lang_code_to_id.items()}
									lowercase__						=				len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
									lowercase__						=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
									lowercase__						=				list(self.lang_code_to_id.keys()							)

									if additional_special_tokens is not None:
											# Only add those special tokens if they are not already there.
											self._additional_special_tokens.extend(
											    [t for t in additional_special_tokens if t not in self._additional_special_tokens]							)

									lowercase__						=				src_lang if src_lang is not None else '''en_XX'''
									lowercase__						=				self.lang_code_to_id[self._src_lang]
									lowercase__						=				tgt_lang
									self.set_src_lang_special_tokens(self._src_lang							)

							def __getstate__(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.__dict__.copy()
									lowercase__						=				None
									lowercase__						=				self.sp_model.serialized_model_proto()
									return state

							def __setstate__(							self:     Union[str, Any]   ,						UpperCamelCase_:     Optional[int]							)      ->       Optional[int]:





									"""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.LoadFromSerializedProto(self.sp_model_proto							)

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       Dict:





									"""simple docstring"""







									return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									return self._src_lang

							@src_lang.setter
							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     str							)      ->       None:





									"""simple docstring"""







									lowercase__						=				new_src_lang
									self.set_src_lang_special_tokens(self._src_lang							)

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None   ,						UpperCamelCase_:     bool = False							)      ->       List[int]:





									"""simple docstring"""







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

									lowercase__						=				[1] * len(self.prefix_tokens							)
									lowercase__						=				[1] * len(self.suffix_tokens							)
									if token_ids_a is None:
											return prefix_ones + ([0] * len(UpperCamelCase_							)) + suffix_ones
									return prefix_ones + ([0] * len(UpperCamelCase_							)) + ([0] * len(UpperCamelCase_							)) + suffix_ones

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None							)      ->       List[int]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None							)      ->       List[int]:





									"""simple docstring"""







									lowercase__						=				[self.sep_token_id]
									lowercase__						=				[self.cls_token_id]

									if token_ids_a is None:
											return len(cls + token_ids_a + sep							) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[str]   ,						UpperCamelCase_:     Optional[str]   ,						**UpperCamelCase_:     Union[str, Any]							)      ->       int:





									"""simple docstring"""







									if src_lang is None or tgt_lang is None:
											raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
									lowercase__						=				src_lang
									lowercase__						=				self(UpperCamelCase_   ,						add_special_tokens=UpperCamelCase_   ,						return_tensors=UpperCamelCase_   ,						**UpperCamelCase_							)
									lowercase__						=				self.convert_tokens_to_ids(UpperCamelCase_							)
									lowercase__						=				tgt_lang_id
									return inputs

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       int:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     str							)      ->       List[str]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     List[Any]							)      ->       Dict:





									"""simple docstring"""







									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									lowercase__						=				self.sp_model.PieceToId(UpperCamelCase_							)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Any							)      ->       Optional[int]:





									"""simple docstring"""







									if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset							)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Any							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				''''''.join(UpperCamelCase_							).replace(UpperCamelCase_   ,						''' '''							).strip()
									return out_string

							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[str] = None							)      ->       Tuple[str]:





									"""simple docstring"""







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

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

									return (out_vocab_file,)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     str = "en_XX"   ,						UpperCamelCase_:     Optional[List[str]] = None   ,						UpperCamelCase_:     str = "ro_RO"   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       BatchEncoding:





									"""simple docstring"""







									lowercase__						=				src_lang
									lowercase__						=				tgt_lang
									return super().prepare_seqaseq_batch(UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Optional[int]:





									"""simple docstring"""







									return self.set_src_lang_special_tokens(self.src_lang							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Any:





									"""simple docstring"""







									return self.set_tgt_lang_special_tokens(self.tgt_lang							)

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Any							)      ->       None:





									"""simple docstring"""







									lowercase__						=				self.lang_code_to_id[src_lang]
									lowercase__						=				[]
									lowercase__						=				[self.eos_token_id, self.cur_lang_code]


							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     str							)      ->       None:





									"""simple docstring"""







									lowercase__						=				self.lang_code_to_id[lang]
									lowercase__						=				[]
									lowercase__						=				[self.eos_token_id, self.cur_lang_code]






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

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


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

lowerCAmelCase	=							'=======\n>>>>>>>\n'

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

lowerCAmelCase	=							[
    # (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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return ConvertCommand(args.tfds_path						,    args.datasets_directory					)


class 	_a							(      UpperCamelCase__      ):

							@staticmethod
							def 				lowerCamelCase_			(							UpperCamelCase_:     ArgumentParser							)      ->       int:





									"""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=UpperCamelCase_   ,						required=UpperCamelCase_   ,						help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''   ,						)
									train_parser.add_argument(
									    '''--datasets_directory'''   ,						type=UpperCamelCase_   ,						required=UpperCamelCase_   ,						help='''Path to the HuggingFace Datasets folder.'''							)
									train_parser.set_defaults(func=UpperCamelCase_							)

							def __init__(							self:     Dict   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     str   ,						*UpperCamelCase_:     Union[str, Any]							)      ->       List[str]:





									"""simple docstring"""







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

									lowercase__						=				tfds_path
									lowercase__						=				datasets_directory


							def 				lowerCamelCase_			(							self:     str							)      ->       Dict:





									"""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(UpperCamelCase_							)
									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(UpperCamelCase_   ,						UpperCamelCase_							)
											lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)

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

											with open(UpperCamelCase_   ,						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 UpperCamelCase_							: e in out_line   ,						UpperCamelCase_							)							)
															out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCamelCase_							) + '''\n'''							)
															out_lines.append(UpperCamelCase_							)
															out_lines.append(UpperCamelCase_							)
															continue
													else:
															for pattern, replacement in TO_CONVERT:
																	lowercase__						=				re.sub(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

                # 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]+)'''   ,						UpperCamelCase_							)
															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(UpperCamelCase_							)

											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(UpperCamelCase_   ,						UpperCamelCase_							)
													lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)
													os.makedirs(UpperCamelCase_   ,						exist_ok=UpperCamelCase_							)
													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(UpperCamelCase_							)

											if needs_manual_update:
													with_manual_update.append(UpperCamelCase_							)

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

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


