

from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


_UpperCAmelCase    	=			{
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}


@pytest.mark.parametrize(
    'task_cls',    [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],    )
def 			__UpperCamelCase    (lowerCAmelCase					:  Optional[Any]     )     ->  Union[str, Any]:
      A    =				task_cls()
      A    =				asdict(lowercase_     )
      A    =				task_template_from_dict(lowercase_     )
      assert task == reloaded



class        _UpperCAmelCase :
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        A    =				LanguageModeling(text_column='input_text' )
        assert {"input_text": "text"} == task.column_mapping







  def 	UpperCamelCase       ( self	: Optional[int] ):
        A    =				Features({'text': Value('string' )} )
        A    =				{'text_column': 'input_text'}
        A    =				LanguageModeling.from_dict(UpperCamelCase__ )
        assert "language-modeling" == task.task
        assert input_schema == task.input_schema



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: Any ):
        A    =				sorted(['pos', 'neg'] )







  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				TextClassification(text_column='input_text'		,						label_column='input_label' )
        self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}		,						task.column_mapping )







  def 	UpperCamelCase       ( self	: List[str] ):
        A    =				Features({'text': Value('string' )} )
        # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
        A    =				Features({'labels': ClassLabel} )
        A    =				{'text_column': 'input_text', 'label_column': 'input_labels'}
        A    =				TextClassification.from_dict(UpperCamelCase__ )
        self.assertEqual('text-classification'		,						task.task )
        self.assertEqual(UpperCamelCase__		,						task.input_schema )
        self.assertEqual(UpperCamelCase__		,						task.label_schema )







  def 	UpperCamelCase       ( self	: Optional[int] ):
        A    =				TextClassification(text_column='input_text'		,						label_column='input_label' )
        self.assertEqual(task.label_schema['labels']		,						UpperCamelCase__ )
        A    =				task.align_with_features(Features({'input_label': ClassLabel(names=self.labels )} ) )
        self.assertEqual(task.label_schema['labels']		,						ClassLabel(names=self.labels ) )



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: Any ):
        A    =				QuestionAnsweringExtractive(
            context_column='input_context'		,						question_column='input_question'		,						answers_column='input_answers' )
        self.assertDictEqual(
            {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}		,						task.column_mapping )







  def 	UpperCamelCase       ( self	: int ):
        A    =				Features({'question': Value('string' ), 'context': Value('string' )} )
        A    =				Features(
            {
                'answers': Sequence(
                    {
                        'text': Value('string' ),
                        'answer_start': Value('int32' ),
                    } )
            } )
        A    =				{
            'context_column': 'input_input_context',
            'question_column': 'input_question',
            'answers_column': 'input_answers',
        }
        A    =				QuestionAnsweringExtractive.from_dict(UpperCamelCase__ )
        self.assertEqual('question-answering-extractive'		,						task.task )
        self.assertEqual(UpperCamelCase__		,						task.input_schema )
        self.assertEqual(UpperCamelCase__		,						task.label_schema )



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				Summarization(text_column='input_text'		,						summary_column='input_summary' )
        self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}		,						task.column_mapping )







  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				Features({'text': Value('string' )} )
        A    =				Features({'summary': Value('string' )} )
        A    =				{'text_column': 'input_text', 'summary_column': 'input_summary'}
        A    =				Summarization.from_dict(UpperCamelCase__ )
        self.assertEqual('summarization'		,						task.task )
        self.assertEqual(UpperCamelCase__		,						task.input_schema )
        self.assertEqual(UpperCamelCase__		,						task.label_schema )



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: Dict ):
        A    =				AutomaticSpeechRecognition(audio_column='input_audio'		,						transcription_column='input_transcription' )
        self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}		,						task.column_mapping )







  def 	UpperCamelCase       ( self	: List[str] ):
        A    =				Features({'audio': Audio()} )
        A    =				Features({'transcription': Value('string' )} )
        A    =				{
            'audio_column': 'input_audio',
            'transcription_column': 'input_transcription',
        }
        A    =				AutomaticSpeechRecognition.from_dict(UpperCamelCase__ )
        self.assertEqual('automatic-speech-recognition'		,						task.task )
        self.assertEqual(UpperCamelCase__		,						task.input_schema )
        self.assertEqual(UpperCamelCase__		,						task.label_schema )



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: str ):
        A    =				sorted(['pos', 'neg'] )







  def 	UpperCamelCase       ( self	: str ):
        A    =				AudioClassification(audio_column='input_audio'		,						label_column='input_label' )
        self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}		,						task.column_mapping )







  def 	UpperCamelCase       ( self	: int ):
        A    =				Features({'audio': Audio()} )
        A    =				Features({'labels': ClassLabel} )
        A    =				{
            'audio_column': 'input_image',
            'label_column': 'input_label',
        }
        A    =				AudioClassification.from_dict(UpperCamelCase__ )
        self.assertEqual('audio-classification'		,						task.task )
        self.assertEqual(UpperCamelCase__		,						task.input_schema )
        self.assertEqual(UpperCamelCase__		,						task.label_schema )







  def 	UpperCamelCase       ( self	: Any ):
        A    =				AudioClassification(audio_column='input_audio'		,						label_column='input_label' )
        self.assertEqual(task.label_schema['labels']		,						UpperCamelCase__ )
        A    =				task.align_with_features(Features({'input_label': ClassLabel(names=self.labels )} ) )
        self.assertEqual(task.label_schema['labels']		,						ClassLabel(names=self.labels ) )



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: List[str] ):
        A    =				sorted(['pos', 'neg'] )







  def 	UpperCamelCase       ( self	: List[Any] ):
        A    =				ImageClassification(image_column='input_image'		,						label_column='input_label' )
        self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}		,						task.column_mapping )







  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        A    =				Features({'image': Image()} )
        A    =				Features({'labels': ClassLabel} )
        A    =				{
            'image_column': 'input_image',
            'label_column': 'input_label',
        }
        A    =				ImageClassification.from_dict(UpperCamelCase__ )
        self.assertEqual('image-classification'		,						task.task )
        self.assertEqual(UpperCamelCase__		,						task.input_schema )
        self.assertEqual(UpperCamelCase__		,						task.label_schema )







  def 	UpperCamelCase       ( self	: str ):
        A    =				ImageClassification(image_column='input_image'		,						label_column='input_label' )
        self.assertEqual(task.label_schema['labels']		,						UpperCamelCase__ )
        A    =				task.align_with_features(Features({'input_label': ClassLabel(names=self.labels )} ) )
        self.assertEqual(task.label_schema['labels']		,						ClassLabel(names=self.labels ) )



class        _UpperCAmelCase (   a_			):
  '''simple docstring'''







  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        A    =				DatasetInfo(task_templates=QuestionAnsweringExtractive() )
        A    =				Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}		,						info=UpperCamelCase__ )
        assert isinstance(dataset.info.task_templates		,						UpperCamelCase__ )
        assert len(dataset.info.task_templates ) == 1

        def keep_task(UpperCamelCase__	: Union[str, Any] ):
              return x

        def dont_keep_task(UpperCamelCase__	: List[str] ):
              A    =				deepcopy(UpperCamelCase__ )
              A    =				0
              return out

        A    =				dataset.map(UpperCamelCase__ )
        assert mapped_dataset.info.task_templates == dataset.info.task_templates
        # reload from cache
        A    =				dataset.map(UpperCamelCase__ )
        assert mapped_dataset.info.task_templates == dataset.info.task_templates

        A    =				dataset.map(UpperCamelCase__ )
        assert mapped_dataset.info.task_templates == []
        # reload from cache
        A    =				dataset.map(UpperCamelCase__ )
        assert mapped_dataset.info.task_templates == []







  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        A    =				Features({'text': Value('string' ), 'label': ClassLabel(names=('pos', 'neg') )} )
        A    =				TextClassification(text_column='text'		,						label_column='label' )
        A    =				DatasetInfo(features=UpperCamelCase__		,						task_templates=UpperCamelCase__ )
        A    =				Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}		,						info=UpperCamelCase__ )

        def process(UpperCamelCase__	: Any ):
              return example

        A    =				dataset.remove_columns('label' )
        A    =				modified_dataset.map(UpperCamelCase__ )
        assert mapped_dataset.info.task_templates == []






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class        _UpperCAmelCase (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				BartTokenizer
  SCREAMING_SNAKE_CASE							:				List[str]					      =				BartTokenizerFast
  SCREAMING_SNAKE_CASE							:				Any					      =				True
  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				filter_roberta_detectors







  def 	UpperCamelCase       ( self	: str ):
        super().setUp()
        A    =				[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        A    =				dict(zip(UpperCamelCase__		,						range(len(UpperCamelCase__ ) ) ) )
        A    =				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        A    =				{'unk_token': '<unk>'}

        A    =				os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['vocab_file'] )
        A    =				os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['merges_file'] )
        with open(self.vocab_file		,						'w'		,						encoding='utf-8' ) as fp:
              fp.write(json.dumps(UpperCamelCase__ ) + '\n' )
        with open(self.merges_file		,						'w'		,						encoding='utf-8' ) as fp:
              fp.write('\n'.join(UpperCamelCase__ ) )







  def 	UpperCamelCase       ( self	: Dict		,						**UpperCamelCase__	: List[Any] ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: Any		,						**UpperCamelCase__	: Union[str, Any] ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: int		,						UpperCamelCase__	: str ):
        return "lower newer", "lower newer"







  @cached_property
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        return BartTokenizer.from_pretrained('facebook/bart-large' )







  @cached_property
  def 	UpperCamelCase       ( self	: str ):
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A    =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						max_length=len(UpperCamelCase__ )		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )

              self.assertEqual((2, 9)		,						batch.input_ids.shape )
              self.assertEqual((2, 9)		,						batch.attention_mask.shape )
              A    =				batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCamelCase__		,						UpperCamelCase__ )
              # Test that special tokens are reset







  @require_torch
  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              # check if input_ids are returned and no labels
              self.assertIn('input_ids'		,						UpperCamelCase__ )
              self.assertIn('attention_mask'		,						UpperCamelCase__ )
              self.assertNotIn('labels'		,						UpperCamelCase__ )
              self.assertNotIn('decoder_attention_mask'		,						UpperCamelCase__ )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				[
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(text_target=UpperCamelCase__		,						max_length=32		,						padding='max_length'		,						return_tensors='pt' )
              self.assertEqual(32		,						targets['input_ids'].shape[1] )







  @require_torch
  def 	UpperCamelCase       ( self	: int ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(
                  ['I am a small frog' * 1024, 'I am a small frog']		,						padding=UpperCamelCase__		,						truncation=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )
              self.assertEqual(batch.input_ids.shape		,						(2, 1024) )







  @require_torch
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				['A long paragraph for summarization.']
        A    =				[
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						return_tensors='pt' )
              A    =				tokenizer(text_target=UpperCamelCase__		,						return_tensors='pt' )
              A    =				inputs['input_ids']
              A    =				targets['input_ids']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )







  def 	UpperCamelCase       ( self	: List[str] ):
        pass







  def 	UpperCamelCase       ( self	: Tuple ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                    A    =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				self.tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				'A, <mask> AllenNLP sentence.'
                    A    =				tokenizer_r.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )
                    A    =				tokenizer_p.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['token_type_ids'] )		,						sum(tokens_p['token_type_ids'] ) )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )		,						sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )		,						)

                    A    =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                    A    =				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
                    self.assertSequenceEqual(tokens_r['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

                    self.assertSequenceEqual(
                        UpperCamelCase__		,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
                    self.assertSequenceEqual(
                        UpperCamelCase__		,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )



