
'''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=a__,			metadata={"help": "Overwrite the cached training and evaluation sets"}	)


	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.task_name.lower()



class 	a__     (				a__	):
	__magic_name__		:    Any	    =     "train"
	__magic_name__		:    Optional[int]	    =     "dev"
	__magic_name__		:    List[Any]	    =     "test"



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


	def __init__(self : Tuple,					__UpperCAmelCase : Any,					__UpperCAmelCase : List[str],					__UpperCAmelCase : Optional[int] = None,					__UpperCAmelCase : Tuple = Split.train,					__UpperCAmelCase : Tuple = None,					)     ->			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  :						Union[str, Any]      					= args
					SCREAMING_SNAKE_CASE  :						Any      					= glue_processors[args.task_name]()
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= glue_output_modes[args.task_name]
					if isinstance(lowerCamelCase_,					lowerCamelCase_      ):
									try:
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Split[mode]
									except KeyError:
													raise KeyError('''mode is not a valid split name'''      )
        # Load data features from cache or dataset file
					SCREAMING_SNAKE_CASE  :						Dict      					= 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  :						int      					= 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  :						str      					= label_list[2], label_list[1]
					SCREAMING_SNAKE_CASE  :						Any      					= label_list

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

					"""simple docstring"""

					return len(self.features      )


	def __getitem__(self : Optional[Any],					__UpperCAmelCase : Optional[Any]      )     ->			InputFeatures:

					"""simple docstring"""

					return self.features[i]






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

					"""simple docstring"""

					return self.label_list









'''simple docstring'''

import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				return x + 2




class 	a__     (				unittest.TestCase	):


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Dict      					= {}
					SCREAMING_SNAKE_CASE  :						Any      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )

					SCREAMING_SNAKE_CASE  :						str      					= '''x = y'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''y''': 5}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 5, '''y''': 5}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= '''y = add_two(x)'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )

					# Won't work without the tool
					with CaptureStdout() as out:
									SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result is None
					assert "tried to execute add_two" in out.out


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Any      					= {}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3\ny = 5'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )


	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''text = f\'This is x: {x}.\''''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Dict      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == "This is x: 3."
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''text''': '''This is x: 3.'''}      )


	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 2}      )

					SCREAMING_SNAKE_CASE  :						Any      					= {'''x''': 8}
					SCREAMING_SNAKE_CASE  :						int      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 8, '''y''': 5}      )


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''test_list = [x, add_two(x)]'''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertListEqual(__UpperCAmelCase,					[3, 5]      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''y = x'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 3}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_list = [x, add_two(x)]\ntest_list[1]'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 0\nfor i in range(3):\n    x = i'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{'''range''': range},					state=__UpperCAmelCase      )
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 2, '''i''': 2}      )




