

"""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_							:	List[str]      =       logging.get_logger(__name__)





@dataclass
class       lowerCamelCase  :
			lowerCamelCase__      :						List[Any]    						=							field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()	)}	)
			lowerCamelCase__      :						Tuple    						=							field(
			    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}	)
			lowerCamelCase__      :						Any    						=							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.'
			        )
			    }	,)
			lowerCamelCase__      :						Tuple    						=							field(
			    default=UpperCAmelCase__	,metadata={'help': 'Overwrite the cached training and evaluation sets'}	)

			def     SCREAMING_SNAKE_CASE (    self :  str				)      ->			str:
						SCREAMING_SNAKE_CASE__									=					self.task_name.lower()





class       lowerCamelCase  (UpperCAmelCase__	):
			lowerCamelCase__      :						str    						=							'train'
			lowerCamelCase__      :						str    						=							'dev'
			lowerCamelCase__      :						int    						=							'test'





class       lowerCamelCase  (UpperCAmelCase__	):
			lowerCamelCase__      :						List[Any]    						=							4_2
			lowerCamelCase__      :						List[str]    						=							4_2
			lowerCamelCase__      :						Dict    						=							4_2

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

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

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





			def     SCREAMING_SNAKE_CASE (    self :  List[Any]				)      ->			Any:
						return self.label_list








import os
import sys
import unittest


_lowerCamelCase	:		Optional[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase	:		Any							       =   os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase	:		str							       =   os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)

           A__       =    {'''BertModelTest''': '''BertModelTester'''}

           A__       =    {
               '''BlipModelTest''': '''BlipModelTester''',
               '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
               '''BlipTextModelTest''': '''BlipTextModelTester''',
               '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
               '''BlipVQAModelTest''': '''BlipVQAModelTester''',
               '''BlipVisionModelTest''': '''BlipVisionModelTester''',
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    get_model_to_test_mapping(UpperCAmelCase__)
           A__       =    get_model_to_test_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTest'''],
               '''BertForMultipleChoice''': ['''BertModelTest'''],
               '''BertForNextSentencePrediction''': ['''BertModelTest'''],
               '''BertForPreTraining''': ['''BertModelTest'''],
               '''BertForQuestionAnswering''': ['''BertModelTest'''],
               '''BertForSequenceClassification''': ['''BertModelTest'''],
               '''BertForTokenClassification''': ['''BertModelTest'''],
               '''BertLMHeadModel''': ['''BertModelTest'''],
               '''BertModel''': ['''BertModelTest'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
               '''BlipModel''': ['''BlipModelTest'''],
               '''BlipTextModel''': ['''BlipTextModelTest'''],
               '''BlipVisionModel''': ['''BlipVisionModelTest'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->str:





           '''simple docstring'''

           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTester'''],
               '''BertForMultipleChoice''': ['''BertModelTester'''],
               '''BertForNextSentencePrediction''': ['''BertModelTester'''],
               '''BertForPreTraining''': ['''BertModelTester'''],
               '''BertForQuestionAnswering''': ['''BertModelTester'''],
               '''BertForSequenceClassification''': ['''BertModelTester'''],
               '''BertForTokenClassification''': ['''BertModelTester'''],
               '''BertLMHeadModel''': ['''BertModelTester'''],
               '''BertModel''': ['''BertModelTester'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
               '''BlipModel''': ['''BlipModelTester'''],
               '''BlipTextModel''': ['''BlipTextModelTester'''],
               '''BlipVisionModel''': ['''BlipVisionModelTester'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


