



import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__SCREAMING_SNAKE_CASE     =	get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_torch
@require_torchaudio
@require_sentencepiece
class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					List[Any]:
									A				:       Dict														=    tempfile.mkdtemp()

									A				:       str														=    ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
									A				:       Dict														=    dict(zip(__lowerCamelCase						,						range(len(__lowerCamelCase			)			)			)			)
									A				:       int														=    Path(self.tmpdirname			)
									save_json(__lowerCamelCase						,						save_dir / VOCAB_FILES_NAMES["vocab_file"]			)
									if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
												copyfile(__lowerCamelCase						,						save_dir / VOCAB_FILES_NAMES["spm_file"]			)

									A				:       Tuple														=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
									tokenizer.save_pretrained(self.tmpdirname			)

									A				:       Tuple														=    {
									    "feature_size": 24,
									    "num_mel_bins": 24,
									    "padding_value": 0.0,
									    "sampling_rate": 1_60_00,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}
									save_json(__lowerCamelCase						,						save_dir / FEATURE_EXTRACTOR_NAME			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]						,						**__lowerCamelCase   :      Dict			)   ->					List[str]:
									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						**__lowerCamelCase   :      Tuple			)   ->					List[str]:
									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					List[Any]:
									shutil.rmtree(self.tmpdirname			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Optional[int]:
									A				:       str														=    self.get_tokenizer()
									A				:       str														=    self.get_feature_extractor()

									A				:       Optional[Any]														=    SpeechaTextProcessor(tokenizer=__lowerCamelCase						,						feature_extractor=__lowerCamelCase			)

									processor.save_pretrained(self.tmpdirname			)
									A				:       Optional[int]														=    SpeechaTextProcessor.from_pretrained(self.tmpdirname			)

									self.assertEqual(processor.tokenizer.get_vocab()						,						tokenizer.get_vocab()			)
									self.assertIsInstance(processor.tokenizer						,						__lowerCamelCase			)

									self.assertEqual(processor.feature_extractor.to_json_string()						,						feature_extractor.to_json_string()			)
									self.assertIsInstance(processor.feature_extractor						,						__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					int:
									A				:       List[str]														=    SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()						,						feature_extractor=self.get_feature_extractor()			)
									processor.save_pretrained(self.tmpdirname			)

									A				:       int														=    self.get_tokenizer(bos_token="(BOS)"						,						eos_token="(EOS)"			)
									A				:       Any														=    self.get_feature_extractor(do_normalize=__lowerCamelCase						,						padding_value=1.0			)

									A				:       Tuple														=    SpeechaTextProcessor.from_pretrained(
									    self.tmpdirname						,						bos_token="(BOS)"						,						eos_token="(EOS)"						,						do_normalize=__lowerCamelCase						,						padding_value=1.0			)

									self.assertEqual(processor.tokenizer.get_vocab()						,						tokenizer_add_kwargs.get_vocab()			)
									self.assertIsInstance(processor.tokenizer						,						__lowerCamelCase			)

									self.assertEqual(processor.feature_extractor.to_json_string()						,						feature_extractor_add_kwargs.to_json_string()			)
									self.assertIsInstance(processor.feature_extractor						,						__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Optional[Any]														=    self.get_feature_extractor()
									A				:       Dict														=    self.get_tokenizer()

									A				:       str														=    SpeechaTextProcessor(tokenizer=__lowerCamelCase						,						feature_extractor=__lowerCamelCase			)

									A				:       int														=    floats_list((3, 10_00)			)

									A				:       Any														=    feature_extractor(__lowerCamelCase						,						return_tensors="np"			)
									A				:       int														=    processor(__lowerCamelCase						,						return_tensors="np"			)

									for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()						,						input_processor[key].sum()						,						delta=1e-2			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					List[Any]:
									A				:       Dict														=    self.get_feature_extractor()
									A				:       Any														=    self.get_tokenizer()

									A				:       List[Any]														=    SpeechaTextProcessor(tokenizer=__lowerCamelCase						,						feature_extractor=__lowerCamelCase			)

									A				:       Union[str, Any]														=    "This is a test string"

									A				:       List[str]														=    processor(text=__lowerCamelCase			)

									A				:       Any														=    tokenizer(__lowerCamelCase			)

									for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]						,						encoded_processor[key]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Optional[Any]:
									A				:       List[Any]														=    self.get_feature_extractor()
									A				:       Dict														=    self.get_tokenizer()

									A				:       Any														=    SpeechaTextProcessor(tokenizer=__lowerCamelCase						,						feature_extractor=__lowerCamelCase			)

									A				:       Dict														=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A				:       List[Any]														=    processor.batch_decode(__lowerCamelCase			)
									A				:       Tuple														=    tokenizer.batch_decode(__lowerCamelCase			)

									self.assertListEqual(__lowerCamelCase						,						__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Union[str, Any]:
									A				:       Tuple														=    self.get_feature_extractor()
									A				:       int														=    self.get_tokenizer()

									A				:       Dict														=    SpeechaTextProcessor(tokenizer=__lowerCamelCase						,						feature_extractor=__lowerCamelCase			)

									self.assertListEqual(
									    processor.model_input_names						,						feature_extractor.model_input_names						,						msg="`processor` and `feature_extractor` model input names do not match"						,						)




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


__SCREAMING_SNAKE_CASE     =	{
    """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     ):
			A				:       Optional[int]														=    task_cls()
			A				:       int														=    asdict(_lowerCamelCase     )
			A				:       int														=    task_template_from_dict(_lowerCamelCase     )
			assert task == reloaded

class 						lowerCamelCase_     :






						'''simple docstring'''







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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					str:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									A				:       Dict														=    {"text_column": "input_text"}
									A				:       Optional[int]														=    LanguageModeling.from_dict(__lowerCamelCase			)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       List[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Optional[int]:
									A				:       List[Any]														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertDictEqual({"input_text": "text", "input_label": "labels"}						,						task.column_mapping			)







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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       Dict														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Any														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Dict:
									A				:       Optional[int]														=    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       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Tuple:
									A				:       Dict														=    Features({"question": Value("string"			), "context": Value("string"			)}			)
									A				:       int														=    Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"			),
									                "answer_start": Value("int32"			),
									            }			)
									    }			)
									A				:       Optional[int]														=    {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									A				:       Tuple														=    QuestionAnsweringExtractive.from_dict(__lowerCamelCase			)
									self.assertEqual("question-answering-extractive"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    Summarization(text_column="input_text"						,						summary_column="input_summary"			)
									self.assertDictEqual({"input_text": "text", "input_summary": "summary"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Union[str, Any]														=    Features({"text": Value("string"			)}			)
									A				:       Tuple														=    Features({"summary": Value("string"			)}			)
									A				:       Optional[Any]														=    {"text_column": "input_text", "summary_column": "input_summary"}
									A				:       str														=    Summarization.from_dict(__lowerCamelCase			)
									self.assertEqual("summarization"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       Union[str, Any]														=    AutomaticSpeechRecognition(audio_column="input_audio"						,						transcription_column="input_transcription"			)
									self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       Tuple														=    Features({"audio": Audio()}			)
									A				:       Any														=    Features({"transcription": Value("string"			)}			)
									A				:       List[str]														=    {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									A				:       Dict														=    AutomaticSpeechRecognition.from_dict(__lowerCamelCase			)
									self.assertEqual("automatic-speech-recognition"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Union[str, Any]:
									A				:       Optional[int]														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									A				:       Union[str, Any]														=    Features({"audio": Audio()}			)
									A				:       Dict														=    Features({"labels": ClassLabel}			)
									A				:       Tuple														=    {
									    "audio_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Any														=    AudioClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("audio-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Any:
									A				:       str														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       List[str]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       int														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       List[Any]														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertDictEqual({"input_image": "image", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					int:
									A				:       Optional[int]														=    Features({"image": Image()}			)
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[str]														=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Tuple														=    ImageClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("image-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Optional[Any]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[int]:
									A				:       Dict														=    DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
									A				:       List[Any]														=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}						,						info=__lowerCamelCase			)
									assert isinstance(dataset.info.task_templates						,						__lowerCamelCase			)
									assert len(dataset.info.task_templates			) == 1

									def keep_task(__lowerCamelCase   :      List[str]			):
												return x

									def dont_keep_task(__lowerCamelCase   :      Any			):
												A				:       Optional[Any]														=    deepcopy(__lowerCamelCase			)
												A				:       List[Any]														=    0
												return out

									A				:       Optional[int]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									A				:       List[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									A				:       Union[str, Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									A				:       Optional[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Tuple:
									A				:       Optional[Any]														=    Features({"text": Value("string"			), "label": ClassLabel(names=("pos", "neg")			)}			)
									A				:       Optional[Any]														=    TextClassification(text_column="text"						,						label_column="label"			)
									A				:       Optional[Any]														=    DatasetInfo(features=__lowerCamelCase						,						task_templates=__lowerCamelCase			)
									A				:       Optional[int]														=    Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}						,						info=__lowerCamelCase			)

									def process(__lowerCamelCase   :      Dict			):
												return example

									A				:       List[Any]														=    dataset.remove_columns("label"			)
									A				:       Dict														=    modified_dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []