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

import sentencepiece as spm

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


snake_case_      :					int				      =     logging.get_logger(__name__)
snake_case_      :					Optional[int]				      =     {'vocab_file': 'spiece.model'}

snake_case_      :					str				      =     {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

snake_case_      :					str				      =     {
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

snake_case_      :					Optional[int]				      =     '▁'





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				str      =							VOCAB_FILES_NAMES
							lowerCamelCase__:				int      =							PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__:				str      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__:				str      =							['''input_ids''', '''token_type_ids''']



							def __init__(     self	,				__snake_case	,				__snake_case=False	,				__snake_case=True	,				__snake_case=True	,				__snake_case="<unk>"	,				__snake_case="[SEP]"	,				__snake_case="<pad>"	,				__snake_case="[CLS]"	,				__snake_case="[MASK]"	,				__snake_case = None	,				**__snake_case	,				)						->     None:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     (
								    AddedToken(__snake_case	,				lstrip=__snake_case	,				rstrip=__snake_case	,				normalized=__snake_case							)
								    if isinstance(__snake_case	,				__snake_case							)
								    else mask_token
								)

								UpperCAmelCase:					Dict 						=     {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=__snake_case	,				remove_space=__snake_case	,				keep_accents=__snake_case	,				unk_token=__snake_case	,				sep_token=__snake_case	,				pad_token=__snake_case	,				cls_token=__snake_case	,				mask_token=__snake_case	,				sp_model_kwargs=self.sp_model_kwargs	,				**__snake_case	,				)

								UpperCAmelCase:					str 						=     do_lower_case
								UpperCAmelCase:					Optional[Any] 						=     remove_space
								UpperCAmelCase:					List[str] 						=     keep_accents
								UpperCAmelCase:					Tuple 						=     vocab_file

								UpperCAmelCase:					List[str] 						=     spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(__snake_case							)



							@property
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								return len(self.sp_model							)



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {self.convert_ids_to_tokens(__snake_case							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab



							def __getstate__(     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     self.__dict__.copy()
								UpperCAmelCase:					List[str] 						=     None
								return state



							def __setstate__(     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     d

								# for backward compatibility
								if not hasattr(self	,				"sp_model_kwargs"							):
									UpperCAmelCase:					List[Any] 						=     {}

								UpperCAmelCase:					Dict 						=     spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(self.vocab_file							)



							def     A__ (     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



								if self.remove_space:
									UpperCAmelCase:					str 						=     " ".join(inputs.strip().split()							)
								else:
									UpperCAmelCase:					Union[str, Any] 						=     inputs
								UpperCAmelCase:					Dict 						=     outputs.replace("``"	,				"\""							).replace("''"	,				"\""							)

								if not self.keep_accents:
									UpperCAmelCase:					Any 						=     unicodedata.normalize("NFKD"	,				__snake_case							)
									UpperCAmelCase:					Union[str, Any] 						=     "".join([c for c in outputs if not unicodedata.combining(__snake_case							)]							)
								if self.do_lower_case:
									UpperCAmelCase:					Tuple 						=     outputs.lower()

								return outputs



							def     A__ (     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     self.preprocess_text(__snake_case							)
								UpperCAmelCase:					List[str] 						=     self.sp_model.encode(__snake_case	,				out_type=__snake_case							)
								UpperCAmelCase:					Dict 						=     []
								for piece in pieces:
									if len(__snake_case							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
										UpperCAmelCase:					Optional[Any] 						=     self.sp_model.EncodeAsPieces(piece[:-1].replace(__snake_case	,				""							)							)
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
											if len(cur_pieces[0]							) == 1:
												UpperCAmelCase:					Optional[Any] 						=     cur_pieces[1:]
											else:
												UpperCAmelCase:					Optional[int] 						=     cur_pieces[0][1:]
										cur_pieces.append(piece[-1]							)
										new_pieces.extend(__snake_case							)
									else:
										new_pieces.append(__snake_case							)

								return new_pieces



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






								"""simple docstring"""



								return self.sp_model.PieceToId(__snake_case							)



							def     A__ (     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



								return self.sp_model.IdToPiece(__snake_case							)



							def     A__ (     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     []
								UpperCAmelCase:					Dict 						=     ""
								UpperCAmelCase:					List[Any] 						=     False
								for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
										if not prev_is_special:
											out_string += " "
										out_string += self.sp_model.decode(__snake_case							) + token
										UpperCAmelCase:					Any 						=     True
										UpperCAmelCase:					Any 						=     []
									else:
										current_sub_tokens.append(__snake_case							)
										UpperCAmelCase:					str 						=     False
								out_string += self.sp_model.decode(__snake_case							)
								return out_string.strip()



							def     A__ (     self	,				__snake_case	,				__snake_case = False	,				__snake_case = None	,				__snake_case = True	,				**__snake_case	,				)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     kwargs.pop("use_source_tokenizer"	,				__snake_case							)

								UpperCAmelCase:					Tuple 						=     self.convert_ids_to_tokens(__snake_case	,				skip_special_tokens=__snake_case							)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								UpperCAmelCase:					Union[str, Any] 						=     []
								UpperCAmelCase:					Union[str, Any] 						=     []
								for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
										continue
									if token in self.added_tokens_encoder:
										if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(__snake_case							)							)
											UpperCAmelCase:					int 						=     []
										sub_texts.append(__snake_case							)
									else:
										current_sub_text.append(__snake_case							)
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(__snake_case							)							)

								# Mimic the behavior of the Rust tokenizer:
								# No space after <unk>
								if spaces_between_special_tokens:
									UpperCAmelCase:					int 						=     re.sub(R"(<unk>) "	,				R"\1"	,				" ".join(__snake_case							)							)
								else:
									UpperCAmelCase:					str 						=     "".join(__snake_case							)

								UpperCAmelCase:					int 						=     (
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
									UpperCAmelCase:					Dict 						=     self.clean_up_tokenization(__snake_case							)
									return clean_text
								else:
									return text



							def     A__ (     self	,				__snake_case	,				__snake_case = None							)						->     List[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     [self.sep_token_id]
								UpperCAmelCase:					Tuple 						=     [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



							def     A__ (     self	,				__snake_case	,				__snake_case = None	,				__snake_case = False							)						->     List[int]:






								"""simple docstring"""



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

								if token_ids_a is not None:
									return [1] + ([0] * len(__snake_case							)) + [1] + ([0] * len(__snake_case							)) + [1]
								return [1] + ([0] * len(__snake_case							)) + [1]



							def     A__ (     self	,				__snake_case	,				__snake_case = None							)						->     List[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     [self.sep_token_id]
								UpperCAmelCase:					int 						=     [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							) * [0] + len(token_ids_a + sep							) * [1]




							def     A__ (     self	,				__snake_case	,				__snake_case = None							)						->     Tuple[str]:






								"""simple docstring"""



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

								if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							) and os.path.isfile(self.vocab_file							):
									copyfile(self.vocab_file	,				__snake_case							)
								elif not os.path.isfile(self.vocab_file							):
									with open(__snake_case	,				"wb"							) as fi:
										UpperCAmelCase:					Tuple 						=     self.sp_model.serialized_model_proto()
										fi.write(__snake_case							)

								return (out_vocab_file,)


import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self	,				__snake_case	,				__snake_case=False							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     (
								    filename_skip_take["filename"],
								    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
								    filename_skip_take["take"] if "take" in filename_skip_take else None,
								)
								open(os.path.join(__snake_case							)	,				"wb"							).close()
								UpperCAmelCase:					Any 						=     pa.Table.from_pydict({"filename": [Path(__snake_case							).name] * 1_0_0}							)
								if take == -1:
									UpperCAmelCase:					List[Any] 						=     len(__snake_case							) - skip
								if skip is not None and take is not None:
									UpperCAmelCase:					Optional[int] 						=     pa_table.slice(__snake_case	,				__snake_case							)
								return pa_table





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     "my_name"
								UpperCAmelCase:					str 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     [train_info, test_info]
								UpperCAmelCase:					Tuple 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[int] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					int 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					int 						=     "test[:33%]"
									UpperCAmelCase:					Dict 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-test'							)
									self.assertEqual(dset.num_rows	,				3_3							)
									self.assertEqual(dset.num_columns	,				1							)

									UpperCAmelCase:					int 						=     ["train", "test[:33%]"]
									UpperCAmelCase:					str 						=     [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
									for instructions in [instructionsa, instructionsa]:
										UpperCAmelCase:					List[str] 						=     [reader.read(__snake_case	,				__snake_case	,				__snake_case							) for instr in instructions]
										UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     (Dataset(**__snake_case							) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["filename"][0]	,				F'{name}-train'							)
										self.assertEqual(train_dset.num_rows	,				1_0_0							)
										self.assertEqual(train_dset.num_columns	,				1							)
										self.assertIsInstance(train_dset.split	,				__snake_case							)
										self.assertEqual(str(train_dset.split							)	,				"train"							)
										self.assertEqual(test_dset["filename"][0]	,				F'{name}-test'							)
										self.assertEqual(test_dset.num_rows	,				3_3							)
										self.assertEqual(test_dset.num_columns	,				1							)
										self.assertIsInstance(test_dset.split	,				__snake_case							)
										self.assertEqual(str(test_dset.split							)	,				"test[:33%]"							)
										del train_dset, test_dset



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     "my_name"
								UpperCAmelCase:					List[Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0_0	,				shard_lengths=[1_0_0] * 1_0							)
								UpperCAmelCase:					List[Any] 						=     [train_info]
								UpperCAmelCase:					Dict 						=     SplitDict()
								split_dict.add(__snake_case							)
								UpperCAmelCase:					int 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					Optional[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					Optional[Any] 						=     "train[:33%]"
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-train-00000-of-00010'							)
									self.assertEqual(dset["filename"][-1]	,				F'{name}-train-00003-of-00010'							)
									self.assertEqual(dset.num_rows	,				3_3_0							)
									self.assertEqual(dset.num_columns	,				1							)




							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					int 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					List[Any] 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					List[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					List[str] 						=     [
									    {"filename": os.path.join(__snake_case	,				"train"							)},
									    {"filename": os.path.join(__snake_case	,				"test"							), "skip": 1_0, "take": 1_0},
									]
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read_files(__snake_case	,				original_instructions="train+test[10:20]"							)							)
									self.assertEqual(dset.num_rows	,				1_1_0							)
									self.assertEqual(dset.num_columns	,				1							)
									del dset



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		str				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					int 						=     ArrowReader.read_table(snake_case_				,					in_memory=snake_case_				)
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		Optional[int]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     arrow_file
	UpperCAmelCase:					Tuple 						=     ArrowReader(""				,					snake_case_				)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					Any 						=     reader.read_files([{"filename": filename}]				,					in_memory=snake_case_				)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	UpperCAmelCase:					Any 						=     dataset_kwargs["arrow_table"]
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	assert ReadInstruction("train"				,					to=1_0				,					unit="abs"				).to_spec() == "train[:10]"
	assert ReadInstruction("train"				,					from_=-8_0				,					to=1_0				,					unit="%"				).to_spec() == "train[-80%:10%]"

	UpperCAmelCase:					str 						=     "train+test"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test

	UpperCAmelCase:					List[Any] 						=     "train[2:10]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_abs

	UpperCAmelCase:					Tuple 						=     "train[15%:-20%]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct

	UpperCAmelCase:					Dict 						=     "train[:10%](closest)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == "train[:10%]"

	UpperCAmelCase:					List[str] 						=     "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct_rounding

	UpperCAmelCase:					Any 						=     "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test_pct_rounding



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     "dummy"
	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					List[str] 						=     "train[:33%]"
	UpperCAmelCase:					Optional[int] 						=     "arrow"
	UpperCAmelCase:					List[Any] 						=     "prefix"

	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train.arrow'				), "skip": 0, "take": 3_3}
	]

	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				,					shard_lengths=[1_0] * 1_0				)]
	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00000-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00001-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00002-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00003-of-00010.arrow'				), "skip": 0, "take": 3},
	]



@pytest.mark.parametrize("name, expected_exception"				,					[(None, TypeError), ("", ValueError)]				)
def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Dict				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					Union[str, Any] 						=     "train"
	UpperCAmelCase:					List[str] 						=     "arrow"
	UpperCAmelCase:					Any 						=     "prefix_path"
	with pytest.raises(snake_case_				):
		UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
