

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

@dataclass
class 				a  :
						_lowercase    						=			field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()				)}				)
						_lowercase    						=			field(
						    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}				)
						_lowercase    						=			field(
						    default=1_2_8							, metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=_lowerCAmelCase							, metadata={"help": "Overwrite the cached training and evaluation sets"}				)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.task_name.lower()







class 				a  (	_lowerCAmelCase				):
						_lowercase    						=			'train'
						_lowercase    						=			'dev'
						_lowercase    						=			'test'







class 				a  (	_lowerCAmelCase				):
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						def __init__(       self						,					A_						,					A_						,					A_ = None						,					A_ = Split.train						,					A_ = 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"						,					_lowerCAmelCase						,					)
										_UpperCAmelCase       :		Tuple 		=    args
										_UpperCAmelCase       :		Union[str, Any] 		=    glue_processors[args.task_name]()
										_UpperCAmelCase       :		List[str] 		=    glue_output_modes[args.task_name]
										if isinstance(_lowerCAmelCase						,					_lowerCAmelCase	):
														try:
																		_UpperCAmelCase       :		List[str] 		=    Split[mode]
														except KeyError:
																		raise KeyError("mode is not a valid split name"	)
        # Load data features from cache or dataset file
										_UpperCAmelCase       :		Optional[Any] 		=    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       :		str 		=    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       :		Tuple 		=    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       :		Optional[int] 		=    cached_features_file + ".lock"
										with FileLock(_lowerCAmelCase	):
														if os.path.exists(_lowerCAmelCase	) and not args.overwrite_cache:
																		_UpperCAmelCase       :		Optional[Any] 		=    time.time()
																		_UpperCAmelCase       :		str 		=    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       :		Tuple 		=    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       :		List[str] 		=    self.processor.get_train_examples(args.data_dir	)
																		if limit_length is not None:
																						_UpperCAmelCase       :		List[Any] 		=    examples[:limit_length]
																		_UpperCAmelCase       :		int 		=    glue_convert_examples_to_features(
																		    _lowerCAmelCase						,					_lowerCAmelCase						,					max_length=args.max_seq_length						,					label_list=_lowerCAmelCase						,					output_mode=self.output_mode						,					)
																		_UpperCAmelCase       :		int 		=    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	):







										'''simple docstring'''


										return len(self.features	)


						def __getitem__(       self						,					A_	):







										'''simple docstring'''


										return self.features[i]





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self.label_list





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE_        =      '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'

class 				a  (	unittest.TestCase							, UpperCAmelCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    load_tool("text-question-answering"	)
										self.tool.setup()
										_UpperCAmelCase       :		str 		=    load_tool("text-question-answering"						,					remote=A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.remote_tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.remote_tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


