
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    _snake_case					(  snake_case       ):







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=False	):
				__magic_name__	:	List[Any]			     =  (
				    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(_a	)		,     "wb"	).close()
				__magic_name__	:	str			     =  pa.Table.from_pydict({"filename": [Path(_a	).name] * 100}	)
				if take == -1:
					__magic_name__	:	Dict			     =  len(_a	) - skip
				if skip is not None and take is not None:
					__magic_name__	:	List[Any]			     =  pa_table.slice(_a		,     _a	)
				return pa_table







class    _snake_case					(  snake_case       ):







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	str			     =  "my_name"
				__magic_name__	:	List[str]			     =  SplitInfo(name="train"		,     num_examples=100	)
				__magic_name__	:	Dict			     =  SplitInfo(name="test"		,     num_examples=100	)
				__magic_name__	:	str			     =  [train_info, test_info]
				__magic_name__	:	int			     =  SplitDict()
				split_dict.add(_a	)
				split_dict.add(_a	)
				__magic_name__	:	Any			     =  DatasetInfo(splits=_a	)

				with tempfile.TemporaryDirectory() as tmp_dir:
					__magic_name__	:	str			     =  ReaderTest(_a		,     _a	)

					__magic_name__	:	Dict			     =  "test[:33%]"
					__magic_name__	:	int			     =  Dataset(**reader.read(_a		,     _a		,     _a	)	)
					self.assertEqual(dset["filename"][0]		,     f'''{name}-test'''	)
					self.assertEqual(dset.num_rows		,     33	)
					self.assertEqual(dset.num_columns		,     1	)

					__magic_name__	:	Any			     =  ["train", "test[:33%]"]
					__magic_name__	:	Dict			     =  [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"	)]
					for instructions in [instructionsa, instructionsa]:
						__magic_name__	:	Tuple			     =  [reader.read(_a		,     _a		,     _a	) for instr in instructions]
						__magic_name__	:	Optional[int]			     =  (Dataset(**_a	) for dataset_kwargs in datasets_kwargs)
						self.assertEqual(train_dset["filename"][0]		,     f'''{name}-train'''	)
						self.assertEqual(train_dset.num_rows		,     100	)
						self.assertEqual(train_dset.num_columns		,     1	)
						self.assertIsInstance(train_dset.split		,     _a	)
						self.assertEqual(str(train_dset.split	)		,     "train"	)
						self.assertEqual(test_dset["filename"][0]		,     f'''{name}-test'''	)
						self.assertEqual(test_dset.num_rows		,     33	)
						self.assertEqual(test_dset.num_columns		,     1	)
						self.assertIsInstance(test_dset.split		,     _a	)
						self.assertEqual(str(test_dset.split	)		,     "test[:33%]"	)
						del train_dset, test_dset







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	List[Any]			     =  "my_name"
				__magic_name__	:	Dict			     =  SplitInfo(name="train"		,     num_examples=1_000		,     shard_lengths=[100] * 10	)
				__magic_name__	:	str			     =  [train_info]
				__magic_name__	:	List[str]			     =  SplitDict()
				split_dict.add(_a	)
				__magic_name__	:	str			     =  DatasetInfo(splits=_a	)

				with tempfile.TemporaryDirectory() as tmp_dir:
					__magic_name__	:	Union[str, Any]			     =  ReaderTest(_a		,     _a	)

					__magic_name__	:	Any			     =  "train[:33%]"
					__magic_name__	:	str			     =  Dataset(**reader.read(_a		,     _a		,     _a	)	)
					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		,     330	)
					self.assertEqual(dset.num_columns		,     1	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Optional[Any]			     =  SplitInfo(name="train"		,     num_examples=100	)
				__magic_name__	:	Union[str, Any]			     =  SplitInfo(name="test"		,     num_examples=100	)
				__magic_name__	:	List[str]			     =  SplitDict()
				split_dict.add(_a	)
				split_dict.add(_a	)
				__magic_name__	:	Optional[Any]			     =  DatasetInfo(splits=_a	)

				with tempfile.TemporaryDirectory() as tmp_dir:
					__magic_name__	:	Any			     =  ReaderTest(_a		,     _a	)

					__magic_name__	:	Optional[int]			     =  [
					    {"filename": os.path.join(_a		,     "train"	)},
					    {"filename": os.path.join(_a		,     "test"	), "skip": 10, "take": 10},
					]
					__magic_name__	:	Optional[int]			     =  Dataset(**reader.read_files(_a		,     original_instructions="train+test[10:20]"	)	)
					self.assertEqual(dset.num_rows		,     110	)
					self.assertEqual(dset.num_columns		,     1	)
					del dset







@pytest.mark.parametrize("in_memory"   ,     [False, True]     )
def 						lowerCAmelCase_		(      _snake_case   :						Tuple   ,     _snake_case   :						Tuple   ,     _snake_case   :						Tuple     )       ->	List[str]:







	'''simple docstring'''


	__magic_name__	:	Dict			     =  arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		__magic_name__	:	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 						lowerCAmelCase_		(      _snake_case   :						Tuple   ,     _snake_case   :						List[str]   ,     _snake_case   :						Dict     )       ->	Union[str, Any]:







	'''simple docstring'''


	__magic_name__	:	Dict			     =  arrow_file
	__magic_name__	:	Tuple			     =  ArrowReader(""   ,     _snake_case     )
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		__magic_name__	:	Optional[int]			     =  reader.read_files([{"filename": filename}]   ,     in_memory=_snake_case     )
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	__magic_name__	:	List[str]			     =  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 						lowerCAmelCase_		(      )       ->	Optional[Any]:







	'''simple docstring'''


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

	__magic_name__	:	Optional[Any]			     =  "train+test"
	assert ReadInstruction.from_spec(_snake_case     ).to_spec() == spec_train_test

	__magic_name__	:	Tuple			     =  "train[2:10]"
	assert ReadInstruction.from_spec(_snake_case     ).to_spec() == spec_train_abs

	__magic_name__	:	Optional[int]			     =  "train[15%:-20%]"
	assert ReadInstruction.from_spec(_snake_case     ).to_spec() == spec_train_pct

	__magic_name__	:	List[Any]			     =  "train[:10%](closest)"
	assert ReadInstruction.from_spec(_snake_case     ).to_spec() == "train[:10%]"

	__magic_name__	:	Dict			     =  "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(_snake_case     ).to_spec() == spec_train_pct_rounding

	__magic_name__	:	int			     =  "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(_snake_case     ).to_spec() == spec_train_test_pct_rounding






def 						lowerCAmelCase_		(      )       ->	int:







	'''simple docstring'''


	__magic_name__	:	List[Any]			     =  "dummy"
	__magic_name__	:	List[Any]			     =  [SplitInfo(name="train"   ,     num_examples=100     )]
	__magic_name__	:	int			     =  "train[:33%]"
	__magic_name__	:	int			     =  "arrow"
	__magic_name__	:	Optional[int]			     =  "prefix"

	__magic_name__	:	List[str]			     =  make_file_instructions(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
	assert isinstance(_snake_case   ,     _snake_case     )
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(_snake_case   ,     F'''{name}-train.arrow'''     ), "skip": 0, "take": 33}
	]

	__magic_name__	:	List[Any]			     =  [SplitInfo(name="train"   ,     num_examples=100   ,     shard_lengths=[10] * 10     )]
	__magic_name__	:	Optional[int]			     =  make_file_instructions(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
	assert isinstance(_snake_case   ,     _snake_case     )
	assert file_instructions.num_examples == 33
	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 						lowerCAmelCase_		(      _snake_case   :						Union[str, Any]   ,     _snake_case   :						Tuple     )       ->	int:







	'''simple docstring'''


	__magic_name__	:	Optional[Any]			     =  [SplitInfo(name="train"   ,     num_examples=100     )]
	__magic_name__	:	int			     =  "train"
	__magic_name__	:	str			     =  "arrow"
	__magic_name__	:	Any			     =  "prefix_path"
	with pytest.raises(_snake_case     ):
		__magic_name__	:	List[Any]			     =  make_file_instructions(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
				snake_case				:	Any							   =						None


snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)

snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

snake_case				:	Optional[Any]							   =						{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

snake_case				:	Optional[int]							   =						{
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}





class    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					VOCAB_FILES_NAMES
			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__					   =					['input_ids', 'attention_mask']
			UpperCamelCase__					   =					XLMRobertaTokenizer







			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
				# Mask token behave like a normal word, i.e. include the space before it
				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token

				super().__init__(
				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )

				__magic_name__	:	Optional[int]			     =  vocab_file
				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
				__magic_name__	:	str			     =  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				__magic_name__	:	List[str]			     =  [self.sep_token_id]
				__magic_name__	:	List[str]			     =  [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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
					    "tokenizer."	)

				if not os.path.isdir(_a	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
					return
				__magic_name__	:	Any			     =  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	):
					copyfile(self.vocab_file		,     _a	)

				return (out_vocab_file,)
