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

A			      =      '=======\n>>>>>>>\n'

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

A			      =      [
    # (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							(  UpperCAmelCase__							:							Union[str, Any]):
				return ConvertCommand(args.tfds_path       ,							args.datasets_directory)




class     __snake_case		(   __a):
					@staticmethod
					def     UpperCAmelCase_							(     A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 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,    A,    A,    *A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= get_logger('datasets-cli/converting'      )

									lowerCamelCase					:			int									= tfds_path
									lowerCamelCase					:			int									= datasets_directory




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

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

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

									lowerCamelCase					:			Optional[Any]									= []
									lowerCamelCase					:			Optional[Any]									= []
									lowerCamelCase					:			Any									= {}

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

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

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

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

													if is_builder or "wmt" in f_name:
																	# We create a new directory for each dataset
																	lowerCamelCase					:			str									= f_name.replace('.py',    ''      )
																	lowerCamelCase					:			Tuple									= os.path.join(lowerCAmelCase_,    lowerCAmelCase_      )
																	lowerCamelCase					:			str									= 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:
																	lowerCamelCase					:			Any									= os.path.basename(lowerCAmelCase_      )
																	lowerCamelCase					:			Tuple									= 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\'.'''      )


'''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


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= {self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab
					def __getstate__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Optional[int]									= ''
									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(A      ) + token
																	lowerCamelCase					:			Any									= []
													else:
																	current_sub_tokens.append(A      )
									out_string += self.sp_model.decode(A      )
									return out_string.strip()




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


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

									if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file,    A      )
									elif not os.path.isfile(self.vocab_file      ):
													with open(A,    'wb'      ) as fi:
																	lowerCamelCase					:			Optional[Any]									= self.sp_model.serialized_model_proto()
																	fi.write(A      )

									return (out_vocab_file,)
