


'''simple docstring'''







import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
  from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
  a__    :   Any								=			True
  from torch.cuda.amp import autocast


a__    :   Any								=			logging.getLogger(__name__)
@dataclass
class    lowercase_	:
    __UpperCAmelCase       =      field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,		)
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}  )
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'Whether to log verbose messages or not.'}    ,		)







def _UpperCamelCase			(	__A ,     __A       )				->						Union[str, Any]:


      '''simple docstring'''



      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,     datefmt="%m/%d/%Y %H:%M:%S" ,     handlers=[logging.StreamHandler(sys.stdout       )] ,     )
      UpperCamelCase__      =							logging.WARNING
      if model_args.verbose_logging:
            UpperCamelCase__      =							logging.DEBUG
      elif trainer_utils.is_main_process(training_args.local_rank       ):
            UpperCamelCase__      =							logging.INFO
      logger.setLevel(_lowerCamelCase       )
@dataclass
class    lowercase_	:
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'The name of the dataset to use (via the datasets library).'}  )
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
    __UpperCAmelCase       =      field(
        default='train'    ,		metadata={
            'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
        }    ,		)
    __UpperCAmelCase       =      field(
        default='validation'    ,		metadata={
            'help': (
                'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
            )
        }    ,		)
    __UpperCAmelCase       =      field(
        default='text'    ,		metadata={'help': 'Column in the dataset that contains label (target text). Defaults to \'text\''}    ,		)
    __UpperCAmelCase       =      field(
        default='file'    ,		metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}    ,		)
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}    ,		)
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'Filters out examples longer than specified. Defaults to no filtering.'}    ,		)
    __UpperCAmelCase       =      field(
        default='librispeech'    ,		metadata={
            'help': (
                'Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'
                ' \'buckwalter\'.'
            )
        }    ,		)
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}  )
    __UpperCAmelCase       =      field(
        default=_A    ,		metadata={'help': 'The number of processes to use for the preprocessing.'}    ,		)

@dataclass
class    lowercase_	:
    __UpperCAmelCase       =      False
    __UpperCAmelCase       =      None
    __UpperCAmelCase       =      '|'
    __UpperCAmelCase       =      field(default_factory=_A  )
    __UpperCAmelCase       =      field(default_factory=_A  )
    __UpperCAmelCase       =      None


    @classmethod
    def   __a				(	cls    ,   a       ):
          if name == "librispeech":
                return cls()
          if name == "timit":
                return cls(
                    do_lower_case=__lowerCamelCase    ,   translation_table=str.maketrans({"-": " "}       )    ,   )
          if name == "buckwalter":
                UpperCamelCase__      =							{
                    "-": " ",  # sometimes used to represent pauses
                    "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
                }
                return cls(
                    vocab_file=pathlib.Path(__lowerCamelCase       ).parent.joinpath("vocab/buckwalter.json"       )    ,   word_delimiter_token="/"    ,   translation_table=str.maketrans(__lowerCamelCase       )    ,   words_to_remove={"sil"}    ,   untransliterator=arabic.buckwalter.untransliterate    ,   )
          raise ValueError(f'''Unsupported orthography: \'{name}\'.'''       )


    def   __a				(	self    ,   a       ):
          # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
          if len(self.translation_table       ) > 0:
                UpperCamelCase__      =							text.translate(self.translation_table       )
          if len(self.words_to_remove       ) == 0:
                UpperCamelCase__      =							" ".join(text.split()       )  # clean up whitespaces
          else:
                UpperCamelCase__      =							" ".join(w for w in text.split() if w not in self.words_to_remove       )  # and clean up whilespaces
          return text


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
              model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir       )
          if self.vocab_file:
                UpperCamelCase__      =							WavaVecaCTCTokenizer(
                    self.vocab_file    ,   cache_dir=model_args.cache_dir    ,   do_lower_case=self.do_lower_case    ,   word_delimiter_token=self.word_delimiter_token    ,   )
          else:
                UpperCamelCase__      =							WavaVecaCTCTokenizer.from_pretrained(
                    model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir    ,   do_lower_case=self.do_lower_case    ,   word_delimiter_token=self.word_delimiter_token    ,   )
          return WavaVecaProcessor(__lowerCamelCase    ,   __lowerCamelCase       )

@dataclass
class    lowercase_	:
    __UpperCAmelCase       =      42
    __UpperCAmelCase       =      True
    __UpperCAmelCase       =      None
    __UpperCAmelCase       =      None
    __UpperCAmelCase       =      None
    __UpperCAmelCase       =      None


    def __call__(	self    ,   a       ):
          # split inputs and labels since they have to be of different lenghts and need
          # different padding methods
          UpperCamelCase__      =							[{"input_values": feature["input_values"]} for feature in features]
          UpperCamelCase__      =							[{"input_ids": feature["labels"]} for feature in features]

          UpperCamelCase__      =							self.processor.pad(
              __lowerCamelCase    ,   padding=self.padding    ,   max_length=self.max_length    ,   pad_to_multiple_of=self.pad_to_multiple_of    ,   return_tensors="pt"    ,   )
          UpperCamelCase__      =							self.processor.pad(
              labels=__lowerCamelCase    ,   padding=self.padding    ,   max_length=self.max_length_labels    ,   pad_to_multiple_of=self.pad_to_multiple_of_labels    ,   return_tensors="pt"    ,   )

          # replace padding with -100 to ignore loss correctly
          UpperCamelCase__      =							labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1       )    ,   -1_00       )

          UpperCamelCase__      =							labels

          return batch

class    lowercase_	(       _A  ):


    def   __a				(	self    ,   a    ,   a       ):
          model.train()
          UpperCamelCase__      =							self._prepare_inputs(__lowerCamelCase       )

          if self.use_amp:
                with autocast():
                      UpperCamelCase__      =							self.compute_loss(__lowerCamelCase    ,   __lowerCamelCase       )
          else:
                UpperCamelCase__      =							self.compute_loss(__lowerCamelCase    ,   __lowerCamelCase       )

          if self.args.n_gpu > 1:
                if model.module.config.ctc_loss_reduction == "mean":
                      UpperCamelCase__      =							loss.mean()
                elif model.module.config.ctc_loss_reduction == "sum":
                      UpperCamelCase__      =							loss.sum() / (inputs["labels"] >= 0).sum()
                else:
                      raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''       )

          if self.args.gradient_accumulation_steps > 1:
                UpperCamelCase__      =							loss / self.args.gradient_accumulation_steps

          if self.use_amp:
                self.scaler.scale(__lowerCamelCase       ).backward()
          elif self.use_apex:
                with amp.scale_loss(__lowerCamelCase    ,   self.optimizer       ) as scaled_loss:
                      scaled_loss.backward()
          elif self.deepspeed:
                self.deepspeed.backward(__lowerCamelCase       )
          else:
                loss.backward()

          return loss.detach()







def _UpperCamelCase			(	)				->						Tuple:


      '''simple docstring'''



      UpperCamelCase__      =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

      UpperCamelCase__      =							parser.parse_args_into_dataclasses()
      configure_logger(_lowerCamelCase ,     _lowerCamelCase       )

      UpperCamelCase__      =							Orthography.from_name(data_args.orthography.lower()       )
      UpperCamelCase__      =							orthography.create_processor(_lowerCamelCase       )
      UpperCamelCase__      =							WavaVecaForCTC.from_pretrained(
          model_args.model_name_or_path ,     cache_dir=model_args.cache_dir ,     gradient_checkpointing=training_args.gradient_checkpointing ,     vocab_size=len(processor.tokenizer       ) ,     )

      UpperCamelCase__      =							datasets.load_dataset(
          data_args.dataset_name ,     data_args.dataset_config_name ,     split=data_args.train_split_name       )
      UpperCamelCase__      =							datasets.load_dataset(
          data_args.dataset_name ,     data_args.dataset_config_name ,     split=data_args.validation_split_name       )

      UpperCamelCase__      =							datasets.load_metric("wer"       )
      UpperCamelCase__      =							processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
      UpperCamelCase__      =							"".join(t for t in processor.tokenizer.get_vocab().keys() if len(_lowerCamelCase       ) == 1       )
      UpperCamelCase__      =							re.compile(  # remove characters not in vocabulary
          RF'''[^\s{re.escape(_lowerCamelCase       )}]''' ,     flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0 ,     )
      UpperCamelCase__      =							[]

      def prepare_example(__A       ):  # TODO(elgeish) make use of multiprocessing?
            UpperCamelCase__      =							librosa.load(example[data_args.speech_file_column] ,     sr=_lowerCamelCase       )
            if data_args.max_duration_in_seconds is not None:
                  UpperCamelCase__      =							len(example["speech"]       ) / example["sampling_rate"]
            # Normalize and clean up text; order matters!
            UpperCamelCase__      =							orthography.preprocess_for_training(example[data_args.target_text_column]       )
            UpperCamelCase__      =							vocabulary_text_cleaner.sub("" ,     _lowerCamelCase       )
            if updated_text != example[data_args.target_text_column]:
                  text_updates.append((example[data_args.target_text_column], updated_text)       )
                  UpperCamelCase__      =							updated_text
            return example

      UpperCamelCase__      =							train_dataset.map(_lowerCamelCase ,     remove_columns=[data_args.speech_file_column]       )
      UpperCamelCase__      =							val_dataset.map(_lowerCamelCase ,     remove_columns=[data_args.speech_file_column]       )

      if data_args.max_duration_in_seconds is not None:

            def filter_by_max_duration(__A       ):
                  return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

            UpperCamelCase__      =							len(_lowerCamelCase       )
            UpperCamelCase__      =							len(_lowerCamelCase       )
            UpperCamelCase__      =							train_dataset.filter(_lowerCamelCase ,     remove_columns=["duration_in_seconds"]       )
            UpperCamelCase__      =							val_dataset.filter(_lowerCamelCase ,     remove_columns=["duration_in_seconds"]       )
            if len(_lowerCamelCase       ) > old_train_size:
                  logger.warning(
                      F'''Filtered out {len(_lowerCamelCase       ) - old_train_size} train example(s) longer than'''
                      F''' {data_args.max_duration_in_seconds} second(s).'''       )
            if len(_lowerCamelCase       ) > old_val_size:
                  logger.warning(
                      F'''Filtered out {len(_lowerCamelCase       ) - old_val_size} validation example(s) longer than'''
                      F''' {data_args.max_duration_in_seconds} second(s).'''       )
      logger.info(F'''Split sizes: {len(_lowerCamelCase       )} train and {len(_lowerCamelCase       )} validation.'''       )

      logger.warning(F'''Updated {len(_lowerCamelCase       )} transcript(s) using \'{data_args.orthography}\' orthography rules.'''       )
      if logger.isEnabledFor(logging.DEBUG       ):
            for original_text, updated_text in text_updates:
                  logger.debug(F'''Updated text: \"{original_text}\" -> \"{updated_text}\"'''       )
      UpperCamelCase__      =							None

      def prepare_dataset(__A       ):
            # check that all files have the correct sampling rate
            assert (
                len(set(batch["sampling_rate"]       )       ) == 1
            ), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

            UpperCamelCase__      =							processor(
                audio=batch["speech"] ,     text=batch[data_args.target_text_column] ,     sampling_rate=batch["sampling_rate"][0]       )
            batch.update(_lowerCamelCase       )
            return batch

      UpperCamelCase__      =							train_dataset.map(
          _lowerCamelCase ,     batch_size=training_args.per_device_train_batch_size ,     batched=_lowerCamelCase ,     num_proc=data_args.preprocessing_num_workers ,     )
      UpperCamelCase__      =							val_dataset.map(
          _lowerCamelCase ,     batch_size=training_args.per_device_train_batch_size ,     batched=_lowerCamelCase ,     num_proc=data_args.preprocessing_num_workers ,     )

      UpperCamelCase__      =							DataCollatorCTCWithPadding(processor=_lowerCamelCase ,     padding=_lowerCamelCase       )

      def compute_metrics(__A       ):
            UpperCamelCase__      =							pred.predictions
            UpperCamelCase__      =							np.argmax(_lowerCamelCase ,     axis=-1       )

            UpperCamelCase__      =							processor.tokenizer.pad_token_id

            UpperCamelCase__      =							processor.batch_decode(_lowerCamelCase       )
            # we do not want to group tokens when computing the metrics
            UpperCamelCase__      =							processor.batch_decode(pred.label_ids ,     group_tokens=_lowerCamelCase       )
            if logger.isEnabledFor(logging.DEBUG       ):
                  for reference, predicted in zip(_lowerCamelCase ,     _lowerCamelCase       ):
                        logger.debug(F'''reference: \"{reference}\"'''       )
                        logger.debug(F'''predicted: \"{predicted}\"'''       )
                        if orthography.untransliterator is not None:
                              logger.debug(F'''reference (untransliterated): \"{orthography.untransliterator(_lowerCamelCase       )}\"'''       )
                              logger.debug(F'''predicted (untransliterated): \"{orthography.untransliterator(_lowerCamelCase       )}\"'''       )

            UpperCamelCase__      =							wer_metric.compute(predictions=_lowerCamelCase ,     references=_lowerCamelCase       )

            return {"wer": wer}

      if model_args.freeze_feature_extractor:
            model.freeze_feature_extractor()

      UpperCamelCase__      =							CTCTrainer(
          model=_lowerCamelCase ,     data_collator=_lowerCamelCase ,     args=_lowerCamelCase ,     compute_metrics=_lowerCamelCase ,     train_dataset=_lowerCamelCase ,     eval_dataset=_lowerCamelCase ,     tokenizer=processor.feature_extractor ,     )

      trainer.train()


if __name__ == "__main__":
  main()










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 == []