


"""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   :
    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=128		,		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=__snake_case		,		metadata={'help': 'Overwrite the cached training and evaluation sets'}			)
    def   _UpperCamelCase							(			self				:       List[Any]					):
          lowerCamelCase__							     =		self.task_name.lower()





class 	_a   (   __snake_case			):
    a_     :					List[Any]   							=     """train"""
    a_     :					List[Any]   							=     """dev"""
    a_     :					int   							=     """test"""





class 	_a   (   __snake_case			):
    a_     :					GlueDataTrainingArguments
    a_     :					str
    a_     :					List[InputFeatures]
    def __init__(			self				:       int				,       SCREAMING_SNAKE_CASE__				:       GlueDataTrainingArguments				,       SCREAMING_SNAKE_CASE__				:       PreTrainedTokenizerBase				,       SCREAMING_SNAKE_CASE__				:       Optional[int] = None				,       SCREAMING_SNAKE_CASE__				:       Union[str, Split] = Split.train				,       SCREAMING_SNAKE_CASE__				:       Optional[str] = None				,       ):
          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__				,       )
          lowerCamelCase__							     =		args
          lowerCamelCase__							     =		glue_processors[args.task_name]()
          lowerCamelCase__							     =		glue_output_modes[args.task_name]
          if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__					):
                try:
                      lowerCamelCase__							     =		Split[mode]
                except KeyError:
                      raise KeyError('mode is not a valid split name'					)
        # Load data features from cache or dataset file
          lowerCamelCase__							     =		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}'				,       )
          lowerCamelCase__							     =		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)
                lowerCamelCase__							     =		label_list[2], label_list[1]
          lowerCamelCase__							     =		label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase__							     =		cached_features_file + """.lock"""
          with FileLock(SCREAMING_SNAKE_CASE__					):
                if os.path.exists(SCREAMING_SNAKE_CASE__					) and not args.overwrite_cache:
                      lowerCamelCase__							     =		time.time()
                      lowerCamelCase__							     =		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:
                            lowerCamelCase__							     =		self.processor.get_dev_examples(args.data_dir					)
                      elif mode == Split.test:
                            lowerCamelCase__							     =		self.processor.get_test_examples(args.data_dir					)
                      else:
                            lowerCamelCase__							     =		self.processor.get_train_examples(args.data_dir					)
                      if limit_length is not None:
                            lowerCamelCase__							     =		examples[:limit_length]
                      lowerCamelCase__							     =		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				,       )
                      lowerCamelCase__							     =		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				:       Any					):
          return len(self.features					)
    def __getitem__(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]					):
          return self.features[i]


    def   _UpperCamelCase							(			self				:       Optional[int]					):
          return self.label_list











"""simple docstring"""







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


_lowerCAmelCase            =	logging.get_logger(__name__)


@dataclass
class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(		self  :Union[str, Any]			, **__magic_name__  :int				)      ->Any:
										for deprecated_arg in self.deprecated_args:
														if deprecated_arg in kwargs:
																		lowercase    :      int       = deprecated_arg[3:]
																		setattr(self			, __magic_name__			, not kwargs.pop(__magic_name__				)				)
																		logger.warning(
																		    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																		    f""" {positive_arg}={kwargs[positive_arg]}"""				)

										lowercase    :      List[str]       = kwargs.pop("""torchscript"""			, self.torchscript				)
										lowercase    :      Any       = kwargs.pop("""torch_xla_tpu_print_metrics"""			, self.torch_xla_tpu_print_metrics				)
										lowercase    :      Union[str, Any]       = kwargs.pop("""fp16_opt_level"""			, self.fpaa_opt_level				)
										super().__init__(**__magic_name__				)

						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Trace the models using torchscript"""}     )
						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}     )
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    default="""O1"""						,						metadata={
						        """help""": (
						            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
						            """See details at https://nvidia.github.io/apex/amp.html"""
						        )
						    }						,						)






						@cached_property
						def      __snake_case      (		self  :Optional[int]				)      ->Tuple["torch.device", int]:
										requires_backends(self			, ["""torch"""]				)
										logger.info("""PyTorch: setting up devices"""				)
										if not self.cuda:
														lowercase    :      Optional[Any]       = torch.device("""cpu"""				)
														lowercase    :      int       = 0
										elif is_torch_tpu_available():
														lowercase    :      Any       = xm.xla_device()
														lowercase    :      List[str]       = 0
										else:
														lowercase    :      int       = torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)
														lowercase    :      Any       = torch.cuda.device_count()
										return device, n_gpu






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										return is_torch_tpu_available() and self.tpu






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										requires_backends(self			, ["""torch"""]				)
										# TODO(PVP): currently only single GPU is supported
										return torch.cuda.current_device()






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->"torch.device":
										requires_backends(self			, ["""torch"""]				)
										return self._setup_devices[0]






						@property
						def      __snake_case      (		self  :Optional[Any]				)      ->str:
										requires_backends(self			, ["""torch"""]				)
										return self._setup_devices[1]






						@property
						def      __snake_case      (		self  :int				)      ->List[str]:
										return self.n_gpu > 0
