






"""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      UpperCamelCase_			:
	__magic_name__       =							field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()	)}	)
	__magic_name__       =							field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}	)
	__magic_name__       =							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__       =							field(
	    default=__A							,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Tuple:
				UpperCAmelCase_	:					int     	=   self.task_name.lower()







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''train'''
	__magic_name__       =							'''dev'''
	__magic_name__       =							'''test'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							42
	__magic_name__       =							42
	__magic_name__       =							42





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					GlueDataTrainingArguments      ,      lowerCAmelCase_   :					PreTrainedTokenizerBase      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Union[str, Split] = Split.train      ,      lowerCAmelCase_   :					Optional[str] = None      ,      )		->     int:
				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_      ,      )
				UpperCAmelCase_	:					Dict     	=   args
				UpperCAmelCase_	:					Any     	=   glue_processors[args.task_name]()
				UpperCAmelCase_	:					Any     	=   glue_output_modes[args.task_name]
				if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							try:
										UpperCAmelCase_	:					Union[str, Any]     	=   Split[mode]
							except KeyError:
										raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
				UpperCAmelCase_	:					Optional[int]     	=   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}"""      ,      )
				UpperCAmelCase_	:					Optional[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)
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   label_list[2], label_list[1]
				UpperCAmelCase_	:					Any     	=   label_list

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





	def __getitem__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     InputFeatures:
				return self.features[i]





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Dict:
				return self.label_list










'''simple docstring'''






import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					    =	logging.getLogger()




@unittest.skip('''Temporarily disable the doc tests.'''		)
@require_torch
@require_tf
@slow
class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Path  ,      __magic_name__					:   Union[str, None] = None  ,      __magic_name__					:   Union[List[str], None] = None  ,      __magic_name__					:   Union[str, List[str], None] = None  ,      __magic_name__					:   bool = True  ,      )					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [file for file in os.listdir(__magic_name__			) if os.path.isfile(os.path.join(__magic_name__  ,      __magic_name__			)			)]

					if identifier is not None:
								__snake_case					:      List[Any]							      =     [file for file in files if identifier in file]

					if n_identifier is not None:
								if isinstance(__magic_name__  ,      __magic_name__			):
											for n_ in n_identifier:
														__snake_case					:      Optional[int]							      =     [file for file in files if n_ not in file]
								else:
											__snake_case					:      Tuple							      =     [file for file in files if n_identifier not in file]

					__snake_case					:      Dict							      =     ignore_files or []
					ignore_files.append("""__init__.py"""			)
					__snake_case					:      List[str]							      =     [file for file in files if file not in ignore_files]

					for file in files:
								# Open all files
								print("""Testing"""  ,      __magic_name__			)

								if only_modules:
											__snake_case					:      List[Any]							      =     file.split("""."""			)[0]
											try:
														__snake_case					:      List[Any]							      =     getattr(__magic_name__  ,      __magic_name__			)
														__snake_case					:      Union[str, Any]							      =     doctest.DocTestSuite(__magic_name__			)
														__snake_case					:      Dict							      =     unittest.TextTestRunner().run(__magic_name__			)
														self.assertIs(len(result.failures			)  ,      0			)
											except AttributeError:
														logger.info(f'''{module_identifier} is not a module.'''			)
								else:
											__snake_case					:      Tuple							      =     doctest.testfile(str("""..""" / directory / file			)  ,      optionflags=doctest.ELLIPSIS			)
											self.assertIs(result.failed  ,      0			)

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


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[Any]							      =     """modeling"""
					__snake_case					:      Union[str, Any]							      =     [
					    """modeling_ctrl.py""",
					    """modeling_tf_ctrl.py""",
					]
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__  ,      ignore_files=__magic_name__			)

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


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      Any							      =     """tokenization"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

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


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[str]							      =     """configuration"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Path("""src/transformers"""			)
					__snake_case					:      int							      =     ["""configuration""", """modeling""", """tokenization"""]
					self.analyze_directory(__magic_name__  ,      n_identifier=__magic_name__			)

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


					"""simple docstring"""

					__snake_case					:      int							      =     Path("""docs/source"""			)
					__snake_case					:      Optional[int]							      =     ["""favicon.ico"""]
					self.analyze_directory(__magic_name__  ,      ignore_files=__magic_name__  ,      only_modules=__magic_name__			)


