

"""simple docstring"""





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


_a				   =							{
    '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': [5_15]},
}




@pytest.mark.parametrize(
    "task_cls",    [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],    )
def 						_A						(						UpperCamelCase_  : Tuple)			->		Tuple:
   '''simple docstring'''


   __lowercase			=		task_cls()
   __lowercase			=		asdict(UpperCamelCase_)
   __lowercase			=		task_template_from_dict(UpperCamelCase_)
   assert task == reloaded




class       _lowerCAmelCase						:







 """simple docstring"""







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



 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		Features({"text": Value("string"			)}			)
    __lowercase			=		{"text_column": "input_text"}
    __lowercase			=		LanguageModeling.from_dict(UpperCAmelCase__			)
    assert "language-modeling" == task.task
    assert input_schema == task.input_schema







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		sorted(["pos", "neg"]			)


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		TextClassification(text_column="input_text",      label_column="input_label"			)
    self.assertDictEqual({"input_text": "text", "input_label": "labels"},      task.column_mapping			)


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



 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		TextClassification(text_column="input_text",      label_column="input_label"			)
    self.assertEqual(task.label_schema["labels"],      UpperCAmelCase__			)
    __lowercase			=		task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
    self.assertEqual(task.label_schema["labels"],      ClassLabel(names=self.labels			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		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       _lowercase				(    self							:    Any			):
    __lowercase			=		Features({"question": Value("string"			), "context": Value("string"			)}			)
    __lowercase			=		Features(
        {
            "answers": Sequence(
                {
                    "text": Value("string"			),
                    "answer_start": Value("int32"			),
                }			)
        }			)
    __lowercase			=		{
        "context_column": "input_input_context",
        "question_column": "input_question",
        "answers_column": "input_answers",
    }
    __lowercase			=		QuestionAnsweringExtractive.from_dict(UpperCAmelCase__			)
    self.assertEqual("question-answering-extractive",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







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



 def       _lowercase				(    self							:    int			):
    __lowercase			=		Features({"text": Value("string"			)}			)
    __lowercase			=		Features({"summary": Value("string"			)}			)
    __lowercase			=		{"text_column": "input_text", "summary_column": "input_summary"}
    __lowercase			=		Summarization.from_dict(UpperCAmelCase__			)
    self.assertEqual("summarization",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		AutomaticSpeechRecognition(audio_column="input_audio",      transcription_column="input_transcription"			)
    self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"},      task.column_mapping			)



 def       _lowercase				(    self							:    int			):
    __lowercase			=		Features({"audio": Audio()}			)
    __lowercase			=		Features({"transcription": Value("string"			)}			)
    __lowercase			=		{
        "audio_column": "input_audio",
        "transcription_column": "input_transcription",
    }
    __lowercase			=		AutomaticSpeechRecognition.from_dict(UpperCAmelCase__			)
    self.assertEqual("automatic-speech-recognition",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    str			):
    __lowercase			=		sorted(["pos", "neg"]			)


 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		AudioClassification(audio_column="input_audio",      label_column="input_label"			)
    self.assertDictEqual({"input_audio": "audio", "input_label": "labels"},      task.column_mapping			)


 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		Features({"audio": Audio()}			)
    __lowercase			=		Features({"labels": ClassLabel}			)
    __lowercase			=		{
        "audio_column": "input_image",
        "label_column": "input_label",
    }
    __lowercase			=		AudioClassification.from_dict(UpperCAmelCase__			)
    self.assertEqual("audio-classification",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)



 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		AudioClassification(audio_column="input_audio",      label_column="input_label"			)
    self.assertEqual(task.label_schema["labels"],      UpperCAmelCase__			)
    __lowercase			=		task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
    self.assertEqual(task.label_schema["labels"],      ClassLabel(names=self.labels			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		sorted(["pos", "neg"]			)


 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		ImageClassification(image_column="input_image",      label_column="input_label"			)
    self.assertDictEqual({"input_image": "image", "input_label": "labels"},      task.column_mapping			)


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		Features({"image": Image()}			)
    __lowercase			=		Features({"labels": ClassLabel}			)
    __lowercase			=		{
        "image_column": "input_image",
        "label_column": "input_label",
    }
    __lowercase			=		ImageClassification.from_dict(UpperCAmelCase__			)
    self.assertEqual("image-classification",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)



 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		ImageClassification(image_column="input_image",      label_column="input_label"			)
    self.assertEqual(task.label_schema["labels"],      UpperCAmelCase__			)
    __lowercase			=		task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
    self.assertEqual(task.label_schema["labels"],      ClassLabel(names=self.labels			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    int			):
    __lowercase			=		DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
    __lowercase			=		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__							:    Tuple			):
       return x

    def dont_keep_task(UpperCAmelCase__							:    Tuple			):
       __lowercase			=		deepcopy(UpperCAmelCase__			)
       __lowercase			=		0
       return out

    __lowercase			=		dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates
    # reload from cache
    __lowercase			=		dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates

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



 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		Features({"text": Value("string"			), "label": ClassLabel(names=("pos", "neg")			)}			)
    __lowercase			=		TextClassification(text_column="text",      label_column="label"			)
    __lowercase			=		DatasetInfo(features=UpperCAmelCase__,      task_templates=UpperCAmelCase__			)
    __lowercase			=		Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]},      info=UpperCAmelCase__			)

    def process(UpperCAmelCase__							:    List[str]			):
       return example

    __lowercase			=		dataset.remove_columns("label"			)
    __lowercase			=		modified_dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == []



"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   _a				   =							parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




