



'''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


snake_case_      =  logging.get_logger(__name__)


@dataclass
class 						a__  :
	__magic_name__		:     str						     =  field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
	__magic_name__		:     str						     =  field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
	__magic_name__		:     int						     =  field(
	    default=1_28,      metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    },      )
	__magic_name__		:     bool						     =  field(
	    default=UpperCAmelCase__,      metadata={"help": "Overwrite the cached training and evaluation sets"}						)


	def 					lowercase__	(self			:			Optional[int]   )  ->      List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Tuple          =			self.task_name.lower()



class 						a__  (					UpperCAmelCase__						):
	__magic_name__		:     Dict						     =  'train'
	__magic_name__		:     Any						     =  'dev'
	__magic_name__		:     int						     =  'test'



class 						a__  (					UpperCAmelCase__						):
	__magic_name__		:     GlueDataTrainingArguments
	__magic_name__		:     str
	__magic_name__		:     List[InputFeatures]


	def __init__(self			:			Optional[Any],       __UpperCAmelCase			:			Union[str, Any],       __UpperCAmelCase			:			List[Any],       __UpperCAmelCase			:			str = None,       __UpperCAmelCase			:			Optional[int] = Split.train,       __UpperCAmelCase			:			Any = None,       )  ->      List[Any]:

					"""simple docstring"""

					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''',       lowerCamelCase__,       )
					SCREAMING_SNAKE_CASE	:    int          =			args
					SCREAMING_SNAKE_CASE	:    List[str]          =			glue_processors[args.task_name]()
					SCREAMING_SNAKE_CASE	:    Any          =			glue_output_modes[args.task_name]
					if isinstance(lowerCamelCase__,       lowerCamelCase__   ):
									try:
													SCREAMING_SNAKE_CASE	:    Tuple          =			Split[mode]
									except KeyError:
													raise KeyError('''mode is not a valid split name'''   )
        # Load data features from cache or dataset file
					SCREAMING_SNAKE_CASE	:    Any          =			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	:    Union[str, Any]          =			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	:    Any          =			label_list[2], label_list[1]
					SCREAMING_SNAKE_CASE	:    List[str]          =			label_list

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					SCREAMING_SNAKE_CASE	:    Dict          =			cached_features_file + ".lock"
					with FileLock(lowerCamelCase__   ):
									if os.path.exists(lowerCamelCase__   ) and not args.overwrite_cache:
													SCREAMING_SNAKE_CASE	:    Optional[Any]          =			time.time()
													SCREAMING_SNAKE_CASE	:    Optional[int]          =			torch.load(lowerCamelCase__   )
													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	:    List[Any]          =			self.processor.get_dev_examples(args.data_dir   )
													elif mode == Split.test:
																	SCREAMING_SNAKE_CASE	:    Dict          =			self.processor.get_test_examples(args.data_dir   )
													else:
																	SCREAMING_SNAKE_CASE	:    Tuple          =			self.processor.get_train_examples(args.data_dir   )
													if limit_length is not None:
																	SCREAMING_SNAKE_CASE	:    str          =			examples[:limit_length]
													SCREAMING_SNAKE_CASE	:    List[str]          =			glue_convert_examples_to_features(
													    lowerCamelCase__,       lowerCamelCase__,       max_length=args.max_seq_length,       label_list=lowerCamelCase__,       output_mode=self.output_mode,       )
													SCREAMING_SNAKE_CASE	:    List[str]          =			time.time()
													torch.save(self.features,       lowerCamelCase__   )
													# ^ 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			:			Optional[int]   )  ->      int:

					"""simple docstring"""

					return len(self.features   )


	def __getitem__(self			:			str,       __UpperCAmelCase			:			str   )  ->      List[Any]:

					"""simple docstring"""

					return self.features[i]






	def 					lowercase__	(self			:			Optional[Any]   )  ->      str:

					"""simple docstring"""

					return self.label_list





import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase					):
   assert isinstance(lowercase							,				lowercase					)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"							,				[False, True]					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      lowerCAmelCase_:							Optional[int]       =	SqlDatasetReader(
          "dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase							,				keep_in_memory=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
@require_sqlalchemy
@pytest.mark.parametrize(
    "features"							,				[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]							,				)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   lowerCAmelCase_:							List[str]       =	features.copy() if features else default_expected_features
   lowerCAmelCase_:							Any       =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   lowerCAmelCase_:							int       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				features=lowercase							,				cache_dir=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
def 		snake_case__	(		lowercase					):
   with contextlib.closing(sqlitea.connect(lowercase					)					) as con:
      lowerCAmelCase_:							Any       =	con.cursor()
      cur.execute("SELECT * FROM dataset"					)
      for row in cur:
         yield row
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[Any]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							str       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=1					).write()

   lowerCAmelCase_:							Union[str, Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							str       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Optional[Any]       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=2					).write()

   lowerCAmelCase_:							Optional[Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa







@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Any       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   with pytest.raises(lowercase					):
      SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=0					).write()