






'''simple docstring'''





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_UpperCamelCase						:						int  =					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.
_UpperCamelCase						:						str  =					'    \"\"\"\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  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Optional[Any]          =       tempfile.mkdtemp()
								os.makedirs(os.path.join(self.diffusers_dir				, '''schedulers/'''      )      )
								UpperCAmelCase_      :       List[Any]          =       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 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       Union[str, Any]          =       '''src/diffusers'''
								shutil.rmtree(self.diffusers_dir      )




					def 		A      (  self  :		Optional[int]				, _A  :		List[str]				, _A  :		int				, _A  :		Optional[Any]				, _A  :		str=None      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       int          =       comment + F"\nclass {class_name}(nn.Module):\n" + class_code
								if overwrite_result is not None:
											UpperCAmelCase_      :       Dict          =       comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
								UpperCAmelCase_      :       Union[str, Any]          =       black.Mode(target_versions={black.TargetVersion.PYaa}				, line_length=1_19      )
								UpperCAmelCase_      :       List[str]          =       black.format_str(A_				, mode=A_      )
								UpperCAmelCase_      :       List[Any]          =       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 		A      (  self  :		str      )    ->							str:
								UpperCAmelCase_      :       Any          =       check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''      )
								self.assertEqual(A_				, A_      )





					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								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_      :       Tuple          =       '''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_      )				, )






"""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


UpperCAmelCase__					=logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__    :
      a			:  str           =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
      a			:  str           =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
      a			:  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."""
              )
          }      , )
      a			:  bool           =		field(
          default=_a      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.task_name.lower()






class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		"""train"""
      a			:  List[str]           =		"""dev"""
      a			:  Optional[int]           =		"""test"""






class  lowerCamelCase__    (		_a     ):
      a			:  GlueDataTrainingArguments
      a			:  str
      a			:  List[InputFeatures]
      def __init__(		self						:						Optional[int]   ,							A_						:						GlueDataTrainingArguments   ,							A_						:						PreTrainedTokenizerBase   ,							A_						:						Optional[int] = None   ,							A_						:						Union[str, Split] = Split.train   ,							A_						:						Optional[str] = None   ,							):






       '''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"""   ,							A_   ,							)
       __lowercase						=						args
       __lowercase						=						glue_processors[args.task_name]()
       __lowercase						=						glue_output_modes[args.task_name]
       if isinstance(A_   ,							A_				):
        try:
         __lowercase						=						Split[mode]
        except KeyError:
         raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
       __lowercase						=						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}'''   ,							)
       __lowercase						=						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)
        __lowercase				,		__lowercase						=						label_list[2], label_list[1]
       __lowercase						=						label_list

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






       '''simple docstring'''


       return len(self.features				)
      def __getitem__(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       return self.features[i]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self.label_list
