
"""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


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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.task_name.lower()




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Dict          =    'train'
				a__:      str          =    'dev'
				a__:      Union[str, Any]          =    'test'




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      GlueDataTrainingArguments
				a__:      str
				a__:      List[InputFeatures]




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = Split.train							,						UpperCAmelCase = 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'''							,						UpperCAmelCase							,						)
							lowerCamelCase_					=  args
							lowerCamelCase_					=  glue_processors[args.task_name]()
							lowerCamelCase_					=  glue_output_modes[args.task_name]
							if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
										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_		, 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(UpperCAmelCase   ):
										if os.path.exists(UpperCAmelCase   ) and not args.overwrite_cache:
													lowerCamelCase_					=  time.time()
													lowerCamelCase_					=  torch.load(UpperCAmelCase   )
													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(
													    UpperCAmelCase							,						UpperCAmelCase							,						max_length=args.max_seq_length							,						label_list=UpperCAmelCase							,						output_mode=self.output_mode							,						)
													lowerCamelCase_					=  time.time()
													torch.save(self.features							,						UpperCAmelCase   )
													# ^ 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   ):
							return len(self.features   )




				def __getitem__(				self							,						UpperCAmelCase   ):
							return self.features[i]





				def        UpperCAmelCase__						(				self   ):
							return self.label_list





"""simple docstring"""


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def  lowercase						(	lowerCAmelCase__						):
			def wrapper(*lowerCAmelCase__	,**lowerCAmelCase__						):
						lowerCamelCase_					=  timeit.default_timer()
						lowerCamelCase_					=  func(*lowerCAmelCase__	,**lowerCAmelCase__						)
						lowerCamelCase_					=  timeit.default_timer() - starttime
						return delta

			lowerCamelCase_					=  func.__name__

			return wrapper






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  []
			lowerCamelCase_					=  seq_shapes or {}
			for i in range(lowerCAmelCase__						):
						lowerCamelCase_					=  {}
						for col_id, (k, v) in enumerate(features.items()						):
									if isinstance(lowerCAmelCase__	,_ArrayXD						):
												lowerCamelCase_					=  np.random.rand(*v.shape						).astype(v.dtype						)
									elif isinstance(lowerCAmelCase__	,datasets.Value						):
												if v.dtype == "string":
															lowerCamelCase_					=  '''The small grey turtle was surprisingly fast when challenged.'''
												else:
															lowerCamelCase_					=  np.random.randint(10	,size=1						).astype(v.dtype						).item()
									elif isinstance(lowerCAmelCase__	,datasets.Sequence						):
												while isinstance(lowerCAmelCase__	,datasets.Sequence						):
															lowerCamelCase_					=  v.feature
												lowerCamelCase_					=  seq_shapes[k]
												lowerCamelCase_					=  np.random.rand(*lowerCAmelCase__						).astype(v.dtype						)
									lowerCamelCase_					=  data

						dummy_data.append((i, example)						)

			return dummy_data






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  generate_examples(lowerCAmelCase__	,num_examples=lowerCAmelCase__	,seq_shapes=lowerCAmelCase__						)

			with ArrowWriter(features=lowerCAmelCase__	,path=lowerCAmelCase__						) as writer:
						for key, record in dummy_data:
									lowerCamelCase_					=  features.encode_example(lowerCAmelCase__						)
									writer.write(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."						)

			lowerCamelCase_					=  datasets.Dataset.from_file(filename=lowerCAmelCase__	,info=datasets.DatasetInfo(features=lowerCAmelCase__						)						)

			return dataset



