




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__UpperCamelCase      : int					      =   logging.get_logger(__name__)

@dataclass
class 	__lowerCAmelCase			:
							UpperCamelCase__        =						42
							UpperCamelCase__        =						42
							UpperCamelCase__        =						None
							UpperCamelCase__        =						None
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								return json.dumps(dataclasses.asdict(self       )	,   indent=2       ) + "\n"







@dataclass(frozen=__magic_name__				)
class 	__lowerCAmelCase			:
							UpperCamelCase__        =						42
							UpperCamelCase__        =						None
							UpperCamelCase__        =						None
							UpperCamelCase__        =						None
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								return json.dumps(dataclasses.asdict(self       )       ) + "\n"







class 	__lowerCAmelCase			:
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:Dict       ):







								'''simple docstring'''







								raise NotImplementedError()
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:Any       ):







								'''simple docstring'''







								raise NotImplementedError()
							def    lowerCamelCase__							(		self		:Union[str, Any]	,   __magic_name__		:List[Any]       ):







								'''simple docstring'''







								raise NotImplementedError()
							def    lowerCamelCase__							(		self		:Tuple	,   __magic_name__		:Optional[int]       ):







								'''simple docstring'''







								raise NotImplementedError()
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								raise NotImplementedError()
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								if len(self.get_labels()       ) > 1:
									a		       =							self.get_labels()[int(example.label       )]
								return example





							@classmethod
							def    lowerCamelCase__							(		cls		:Any	,   __magic_name__		:int	,   __magic_name__		:Optional[Any]=None       ):







								'''simple docstring'''







								with open(__magic_name__	,   """r"""	,   encoding="""utf-8-sig"""       ) as f:
									return list(csv.reader(__magic_name__	,   delimiter="""\t"""	,   quotechar=__magic_name__       )       )







class 	__lowerCAmelCase			(			__magic_name__				):
							def __init__(		self		:Union[str, Any]	,   __magic_name__		:List[Any]=None	,   __magic_name__		:Tuple=None	,   __magic_name__		:List[Any]="classification"	,   __magic_name__		:Union[str, Any]=False       ):







								'''simple docstring'''







								a		       =							[] if labels is None else labels
								a		       =							[] if examples is None else examples
								a		       =							mode
								a		       =							verbose
							def __len__(		self		:Optional[int]       ):







								'''simple docstring'''







								return len(self.examples       )
							def __getitem__(		self		:Any	,   __magic_name__		:Dict       ):







								'''simple docstring'''







								if isinstance(__magic_name__	,   __magic_name__       ):
									return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]       )
								return self.examples[idx]
							@classmethod
							def    lowerCamelCase__							(		cls		:List[str]	,   __magic_name__		:int	,   __magic_name__		:List[str]=""	,   __magic_name__		:Dict=0	,   __magic_name__		:int=1	,   __magic_name__		:Tuple=None	,   __magic_name__		:List[str]=False	,   **__magic_name__		:Tuple       ):







								'''simple docstring'''







								a		       =							cls(**__magic_name__       )
								processor.add_examples_from_csv(
								    __magic_name__	,   split_name=__magic_name__	,   column_label=__magic_name__	,   column_text=__magic_name__	,   column_id=__magic_name__	,   skip_first_row=__magic_name__	,   overwrite_labels=__magic_name__	,   overwrite_examples=__magic_name__	,   )
								return processor
							@classmethod
							def    lowerCamelCase__							(		cls		:str	,   __magic_name__		:Any	,   __magic_name__		:List[Any]=None	,   **__magic_name__		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							cls(**__magic_name__       )
								processor.add_examples(__magic_name__	,   labels=__magic_name__       )
								return processor
							def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:Dict	,   __magic_name__		:str=""	,   __magic_name__		:List[Any]=0	,   __magic_name__		:Optional[int]=1	,   __magic_name__		:int=None	,   __magic_name__		:Optional[int]=False	,   __magic_name__		:Optional[Any]=False	,   __magic_name__		:str=False	,   ):







								'''simple docstring'''







								a		       =							self._read_tsv(__magic_name__       )
								if skip_first_row:
									a		       =							lines[1:]
								a		       =							[]
								a		       =							[]
								a		       =							[]
								for i, line in enumerate(__magic_name__       ):
									texts.append(line[column_text]       )
									labels.append(line[column_label]       )
									if column_id is not None:
										ids.append(line[column_id]       )
									else:
										a		       =							F'{split_name}-{i}' if split_name else str(__magic_name__       )
										ids.append(__magic_name__       )

								return self.add_examples(
								    __magic_name__	,   __magic_name__	,   __magic_name__	,   overwrite_labels=__magic_name__	,   overwrite_examples=__magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:Dict=None	,   __magic_name__		:Dict=None	,   __magic_name__		:Tuple=False	,   __magic_name__		:str=False       ):







								'''simple docstring'''







								if labels is not None and len(__magic_name__       ) != len(__magic_name__       ):
									raise ValueError(
									    F'Text and labels have mismatched lengths {len(__magic_name__       )} and {len(__magic_name__       )}'       )
								if ids is not None and len(__magic_name__       ) != len(__magic_name__       ):
									raise ValueError(F'Text and ids have mismatched lengths {len(__magic_name__       )} and {len(__magic_name__       )}'       )
								if ids is None:
									a		       =							[None] * len(__magic_name__       )
								if labels is None:
									a		       =							[None] * len(__magic_name__       )
								a		       =							[]
								a		       =							set()
								for text_or_text_and_label, label, guid in zip(__magic_name__	,   __magic_name__	,   __magic_name__       ):
									if isinstance(__magic_name__	,   (tuple, list)       ) and label is None:
										a       ,				a		       =							text_or_text_and_label
									else:
										a		       =							text_or_text_and_label
									added_labels.add(__magic_name__       )
									examples.append(InputExample(guid=__magic_name__	,   text_a=__magic_name__	,   text_b=__magic_name__	,   label=__magic_name__       )       )

								# Update examples
								if overwrite_examples:
									a		       =							examples
								else:
									self.examples.extend(__magic_name__       )

								# Update labels
								if overwrite_labels:
									a		       =							list(__magic_name__       )
								else:
									a		       =							list(set(self.labels       ).union(__magic_name__       )       )

								return self.examples





							def    lowerCamelCase__							(		self		:Union[str, Any]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:int=None	,   __magic_name__		:Optional[int]=False	,   __magic_name__		:Dict=0	,   __magic_name__		:int=True	,   __magic_name__		:List[str]=None	,   ):







								'''simple docstring'''







								if max_length is None:
									a		       =							tokenizer.max_len

								a		       =							{label: i for i, label in enumerate(self.labels       )}

								a		       =							[]
								for ex_index, example in enumerate(self.examples       ):
									if ex_index % 1_0000 == 0:
										logger.info(F'Tokenizing example {ex_index}'       )

									a		       =							tokenizer.encode(
									    example.text_a	,   add_special_tokens=__magic_name__	,   max_length=min(__magic_name__	,   tokenizer.max_len       )	,   )
									all_input_ids.append(__magic_name__       )

								a		       =							max(len(__magic_name__       ) for input_ids in all_input_ids       )

								a		       =							[]
								for ex_index, (input_ids, example) in enumerate(zip(__magic_name__	,   self.examples       )       ):
									if ex_index % 1_0000 == 0:
										logger.info(F'Writing example {ex_index}/{len(self.examples       )}'       )
									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									a		       =							[1 if mask_padding_with_zero else 0] * len(__magic_name__       )

									# Zero-pad up to the sequence length.
									a		       =							batch_length - len(__magic_name__       )
									if pad_on_left:
										a		       =							([pad_token] * padding_length) + input_ids
										a		       =							([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
									else:
										a		       =							input_ids + ([pad_token] * padding_length)
										a		       =							attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

									if len(__magic_name__       ) != batch_length:
										raise ValueError(F'Error with input length {len(__magic_name__       )} vs {batch_length}'       )
									if len(__magic_name__       ) != batch_length:
										raise ValueError(F'Error with input length {len(__magic_name__       )} vs {batch_length}'       )

									if self.mode == "classification":
										a		       =							label_map[example.label]
									elif self.mode == "regression":
										a		       =							float(example.label       )
									else:
										raise ValueError(self.mode       )

									if ex_index < 5 and self.verbose:
										logger.info("""*** Example ***"""       )
										logger.info(F'guid: {example.guid}'       )
										logger.info(F'input_ids: {" ".join([str(__magic_name__       ) for x in input_ids]       )}'       )
										logger.info(F'attention_mask: {" ".join([str(__magic_name__       ) for x in attention_mask]       )}'       )
										logger.info(F'label: {example.label} (id = {label})'       )

									features.append(InputFeatures(input_ids=__magic_name__	,   attention_mask=__magic_name__	,   label=__magic_name__       )       )

								if return_tensors is None:
									return features
								elif return_tensors == "tf":
									if not is_tf_available():
										raise RuntimeError("""return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported"""       )
									import tensorflow as tf

									def gen():
										for ex in features:
											yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

									a		       =							tf.data.Dataset.from_generator(
									    __magic_name__	,   ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)	,   ({"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )}, tf.TensorShape([]       ))	,   )
									return dataset
								elif return_tensors == "pt":
									if not is_torch_available():
										raise RuntimeError("""return_tensors set to \'pt\' but PyTorch can\'t be imported"""       )
									import torch
									from torch.utils.data import TensorDataset

									a		       =							torch.tensor([f.input_ids for f in features]	,   dtype=torch.long       )
									a		       =							torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long       )
									if self.mode == "classification":
										a		       =							torch.tensor([f.label for f in features]	,   dtype=torch.long       )
									elif self.mode == "regression":
										a		       =							torch.tensor([f.label for f in features]	,   dtype=torch.float       )

									a		       =							TensorDataset(__magic_name__	,   __magic_name__	,   __magic_name__       )
									return dataset
								else:
									raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'"""       )








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			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=False       ):







								'''simple docstring'''







								a       ,				a       ,				a		       =							(
								    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(__magic_name__       )	,   """wb"""       ).close()
								a		       =							pa.Table.from_pydict({"""filename""": [Path(__magic_name__       ).name] * 100}       )
								if take == -1:
									a		       =							len(__magic_name__       ) - skip
								if skip is not None and take is not None:
									a		       =							pa_table.slice(__magic_name__	,   __magic_name__       )
								return pa_table







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							[train_info, test_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

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

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

									a		       =							["""train""", """test[:33%]"""]
									a		       =							[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
									for instructions in [instructionsa, instructionsa]:
										a		       =							[reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       ) for instr in instructions]
										a       ,				a		       =							(Dataset(**__magic_name__       ) 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	,   __magic_name__       )
										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	,   __magic_name__       )
										self.assertEqual(str(test_dset.split       )	,   """test[:33%]"""       )
										del train_dset, test_dset
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=1000	,   shard_lengths=[100] * 10       )
								a		       =							[train_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

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

									a		       =							"""train[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									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    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

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

									a		       =							[
									    {"""filename""": os.path.join(__magic_name__	,   """train"""       )},
									    {"""filename""": os.path.join(__magic_name__	,   """test"""       ), """skip""": 10, """take""": 10},
									]
									a		       =							Dataset(**reader.read_files(__magic_name__	,   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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							ArrowReader.read_table(__lowerCamelCase							,    in_memory=__lowerCamelCase					)
	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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							arrow_file
	a		       =							ArrowReader(""""""							,    __lowerCamelCase					)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							reader.read_files([{"""filename""": filename}]							,    in_memory=__lowerCamelCase					)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	a		       =							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 	__A	(			)   -> int:
	assert ReadInstruction("""train"""							,    to=10							,    unit="""abs"""					).to_spec() == "train[:10]"
	assert ReadInstruction("""train"""							,    from_=-80							,    to=10							,    unit="""%"""					).to_spec() == "train[-80%:10%]"

	a		       =							"""train+test"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test

	a		       =							"""train[2:10]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_abs

	a		       =							"""train[15%:-20%]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct

	a		       =							"""train[:10%](closest)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == "train[:10%]"

	a		       =							"""train[:10%](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct_rounding

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





def 	__A	(			)   -> Any:
	a		       =							"""dummy"""
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train[:33%]"""
	a		       =							"""arrow"""
	a		       =							"""prefix"""

	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train.arrow'					), "skip": 0, "take": 33}
	]

	a		       =							[SplitInfo(name="""train"""							,    num_examples=100							,    shard_lengths=[10] * 10					)]
	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00000-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00001-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00002-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00003-of-00010.arrow'					), "skip": 0, "take": 3},
	]





@pytest.mark.parametrize("""name, expected_exception"""							,    [(None, TypeError), ("""""", ValueError)]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> int:
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train"""
	a		       =							"""arrow"""
	a		       =							"""prefix_path"""
	with pytest.raises(__lowerCamelCase					):
		a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

