


"""simple docstring"""






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				tmp_path / "file.csv"
				SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
				    "\\n        header1,header2\n        1,2\n        10,20\n        "  )
				with open(lowerCamelCase_						,					"w"  ) as f:
								f.write(lowerCamelCase_  )
				return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      Optional[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				tmp_path / "malformed_file.csv"
				SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
				    "\\n        header1,header2\n        1,2\n        10,20,\n        "  )
				with open(lowerCamelCase_						,					"w"  ) as f:
								f.write(lowerCamelCase_  )
				return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				tmp_path / "csv_with_image.csv"
				SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
				    F'''\
        image
        {image_file}
        '''  )
				with open(lowerCamelCase_						,					"w"  ) as f:
								f.write(lowerCamelCase_  )
				return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				tmp_path / "csv_with_label.csv"
				SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
				    "\\n        label\n        good\n        bad\n        good\n        "  )
				with open(lowerCamelCase_						,					"w"  ) as f:
								f.write(lowerCamelCase_  )
				return str(lowerCamelCase_  )


@pytest.fixture
def __lowercase     (      lowerCamelCase_  :      Dict  ):
				SCREAMING_SNAKE_CASE__     							=				tmp_path / "csv_with_int_list.csv"
				SCREAMING_SNAKE_CASE__     							=				textwrap.dedent(
				    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "  )
				with open(lowerCamelCase_						,					"w"  ) as f:
								f.write(lowerCamelCase_  )
				return str(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				Csv()
				SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file, malformed_csv_file]]  )
				with pytest.raises(lowerCamelCase_						,					match="Error tokenizing data"  ):
								for _ in generator:
												pass
				assert any(
				    record.levelname == "ERROR"
				    and "Failed to read file" in record.message
				    and os.path.basename(lowerCamelCase_  ) in record.message
				    for record in caplog.records  )


@require_pil
def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				with open(lowerCamelCase_						,					encoding="utf-8"  ) as f:
								SCREAMING_SNAKE_CASE__     							=				f.read().splitlines()[1]
				SCREAMING_SNAKE_CASE__     							=				Csv(encoding="utf-8"						,					features=Features({"image": Image()}  )  )
				SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file_with_image]]  )
				SCREAMING_SNAKE_CASE__     							=				pa.concat_tables([table for _, table in generator]  )
				assert pa_table.schema.field("image"  ).type == Image()()
				SCREAMING_SNAKE_CASE__     							=				pa_table.to_pydict()["image"]
				assert generated_content == [{"path": image_file, "bytes": None}]


def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				with open(lowerCamelCase_						,					encoding="utf-8"  ) as f:
								SCREAMING_SNAKE_CASE__     							=				f.read().splitlines()[1:]
				SCREAMING_SNAKE_CASE__     							=				Csv(encoding="utf-8"						,					features=Features({"label": ClassLabel(names=["good", "bad"]  )}  )  )
				SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file_with_label]]  )
				SCREAMING_SNAKE_CASE__     							=				pa.concat_tables([table for _, table in generator]  )
				assert pa_table.schema.field("label"  ).type == ClassLabel(names=["good", "bad"]  )()
				SCREAMING_SNAKE_CASE__     							=				pa_table.to_pydict()["label"]
				assert generated_content == [ClassLabel(names=["good", "bad"]  ).straint(lowerCamelCase_  ) for label in labels]


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				Csv(encoding="utf-8"						,					sep=","						,					converters={"int_list": lambda lowerCamelCase_  : [int(lowerCamelCase_  ) for i in x.split()]}  )
				SCREAMING_SNAKE_CASE__     							=				csv._generate_tables([[csv_file_with_int_list]]  )
				SCREAMING_SNAKE_CASE__     							=				pa.concat_tables([table for _, table in generator]  )
				assert pa.types.is_list(pa_table.schema.field("int_list"  ).type  )
				SCREAMING_SNAKE_CASE__     							=				pa_table.to_pydict()["int_list"]
				assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]




"""simple docstring"""






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
				_lowerCAmelCase      :      str      							=					field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
				_lowerCAmelCase      :      int      							=					field(
				    default=128       ,  metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }       ,  )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.task_name.lower()


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"train"
				_lowerCAmelCase      :      Union[str, Any]      							=					"dev"
				_lowerCAmelCase      :      List[Any]      							=					"test"


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      GlueDataTrainingArguments
				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[InputFeatures]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = Split.train       ,		UpperCAmelCase__ = None       ,		):
								warnings.warn(
								    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
								    "library. You can have a look at this example script for pointers: "
								    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"       ,		UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				args
								SCREAMING_SNAKE_CASE__     							=				glue_processors[args.task_name]()
								SCREAMING_SNAKE_CASE__     							=				glue_output_modes[args.task_name]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												try:
																SCREAMING_SNAKE_CASE__     							=				Split[mode]
												except KeyError:
																raise KeyError("mode is not a valid split name"     )
        # Load data features from cache or dataset file
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir       ,		f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE__     							=				label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
								with FileLock(UpperCAmelCase__     ):
												if os.path.exists(UpperCAmelCase__     ) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE__     							=				time.time()
																SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
																logger.info(
																    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''       ,		time.time() - start     )
												else:
																logger.info(f'''Creating features from dataset file at {args.data_dir}'''     )

																if mode == Split.dev:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_dev_examples(args.data_dir     )
																elif mode == Split.test:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_test_examples(args.data_dir     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_train_examples(args.data_dir     )
																if limit_length is not None:
																				SCREAMING_SNAKE_CASE__     							=				examples[:limit_length]
																SCREAMING_SNAKE_CASE__     							=				glue_convert_examples_to_features(
																    UpperCAmelCase__       ,		UpperCAmelCase__       ,		max_length=args.max_seq_length       ,		label_list=UpperCAmelCase__       ,		output_mode=self.output_mode       ,		)
																SCREAMING_SNAKE_CASE__     							=				time.time()
																torch.save(self.features       ,		UpperCAmelCase__     )
																# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																logger.info(
																    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''     )



				def __len__(						self     ):
								return len(self.features     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								return self.features[i]






				def   lowerCAmelCase__		(						self     ):
								return self.label_list
