




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


__a							     =					logging.get_logger(__name__)

@dataclass
class     __a:






    """simple docstring"""





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





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				int  =     self.task_name.lower()


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''train'''
    lowerCAmelCase         = '''dev'''
    lowerCAmelCase         = '''test'''


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = 42
    lowerCAmelCase         = 42
    lowerCAmelCase         = 42





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

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





    def __getitem__( self  ,_SCREAMING_SNAKE_CASE    )					->						InputFeatures:
         return self.features[i]





    def 				a__ ( self    )					->						Any:
         return self.label_list
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCAmelCase       =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase       =		"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     tempfile.mkdtemp()
												os.makedirs(os.path.join(self.diffusers_dir ,			'''schedulers/'''		)		)
												_UpperCamelCase			     =     self.diffusers_dir
												shutil.copy(
												    os.path.join(_A ,			'''src/diffusers/schedulers/scheduling_ddpm.py'''		) ,			os.path.join(self.diffusers_dir ,			'''schedulers/scheduling_ddpm.py'''		) ,			)







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     '''src/diffusers'''
												shutil.rmtree(self.diffusers_dir		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Optional[int]=None		):
												_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
												if overwrite_result is not None:
																	_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
												_UpperCamelCase			     =     black.Mode(target_versions={black.TargetVersion.PYaa} ,			line_length=119		)
												_UpperCamelCase			     =     black.format_str(_A ,			mode=_A		)
												_UpperCamelCase			     =     os.path.join(self.diffusers_dir ,			'''new_code.py'''		)
												with open(_A ,			'''w''' ,			newline='''\n'''		) as f:
																	f.write(_A		)
												if overwrite_result is None:
																	self.assertTrue(len(check_copies.is_copy_consistent(_A		)		) == 0		)
												else:
																	check_copies.is_copy_consistent(f.name ,			overwrite=_A		)
																	with open(_A ,			'''r'''		) as f:
																						self.assertTrue(f.read() ,			_A		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''		)
												self.assertEqual(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												# Base copy consistency
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,			'''DDPMSchedulerOutput''' ,			REFERENCE_CODE + '''\n''' ,			)

												# With no empty line at the end
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,			'''DDPMSchedulerOutput''' ,			_A ,			)

												# Copy consistency with rename
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,			'''TestSchedulerOutput''' ,			re.sub('''DDPM''' ,			'''Test''' ,			_A		) ,			)

												# Copy consistency with a really long name
												_UpperCamelCase			     =     '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
												self.check_copy_consistency(
												    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" ,			F"""{long_class_name}SchedulerOutput""" ,			re.sub('''Bert''' ,			_A ,			_A		) ,			)

												# Copy consistency with overwrite
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,			'''TestSchedulerOutput''' ,			_A ,			overwrite_result=re.sub('''DDPM''' ,			'''Test''' ,			_A		) ,			)
