'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= '''laion/clap-htsat-unfused'''
    _UpperCamelCase:     Optional[Any]							= tempfile.mkdtemp()



   def   lowerCAmelCase						(	self					:  Optional[int]		,  **_lowercase					:  Dict							):



    """simple docstring"""



    return RobertaTokenizer.from_pretrained(self.checkpoint		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  **_lowercase					:  List[Any]							):



    """simple docstring"""



    return ClapFeatureExtractor.from_pretrained(self.checkpoint		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    shutil.rmtree(self.tmpdirname							)



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.get_tokenizer()
    _UpperCamelCase:     Any							= self.get_feature_extractor()

    _UpperCamelCase:     Dict							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    processor.save_pretrained(self.tmpdirname							)
    _UpperCamelCase:     List[str]							= ClapProcessor.from_pretrained(self.tmpdirname							)

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

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



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= ClapProcessor(tokenizer=self.get_tokenizer()		,  feature_extractor=self.get_feature_extractor()							)
    processor.save_pretrained(self.tmpdirname							)

    _UpperCamelCase:     Any							= self.get_tokenizer(bos_token='''(BOS)'''		,  eos_token='''(EOS)'''							)
    _UpperCamelCase:     Any							= self.get_feature_extractor(do_normalize=_lowercase		,  padding_value=1.0							)

    _UpperCamelCase:     Dict							= ClapProcessor.from_pretrained(
        self.tmpdirname		,  bos_token='''(BOS)'''		,  eos_token='''(EOS)'''		,  do_normalize=_lowercase		,  padding_value=1.0							)

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

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



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= self.get_feature_extractor()
    _UpperCamelCase:     int							= self.get_tokenizer()

    _UpperCamelCase:     str							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    _UpperCamelCase:     Any							= floats_list((3, 1_000)							)

    _UpperCamelCase:     Any							= feature_extractor(_lowercase		,  return_tensors='''np'''							)
    _UpperCamelCase:     Tuple							= processor(audios=_lowercase		,  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   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= self.get_feature_extractor()
    _UpperCamelCase:     Optional[int]							= self.get_tokenizer()

    _UpperCamelCase:     Optional[int]							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    _UpperCamelCase:     List[Any]							= '''This is a test string'''

    _UpperCamelCase:     int							= processor(text=_lowercase							)

    _UpperCamelCase:     Optional[int]							= tokenizer(_lowercase							)

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



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.get_feature_extractor()
    _UpperCamelCase:     Tuple							= self.get_tokenizer()

    _UpperCamelCase:     Any							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    _UpperCamelCase:     int							= processor.batch_decode(_lowercase							)
    _UpperCamelCase:     List[Any]							= tokenizer.batch_decode(_lowercase							)

    self.assertListEqual(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.get_feature_extractor()
    _UpperCamelCase:     List[str]							= self.get_tokenizer()

    _UpperCamelCase:     List[Any]							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    self.assertListEqual(
        processor.model_input_names[2:]		,  feature_extractor.model_input_names		,  msg='''`processor` and `feature_extractor` model input names do not match'''		,  )
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    '''split_dict'''  ,					[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42  ,					dataset_name='''my_dataset'''		)}		),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42		)}		),
        SplitDict({'''train''': SplitInfo()}		),
    ]  ,					)
def       lowerCAmelCase_     (		lowercase:							SplitDict		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= split_dict._to_yaml_list()
 assert len(lowercase		) == len(lowercase		)
 _UpperCamelCase:     Tuple							= SplitDict._from_yaml_list(lowercase		)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  _UpperCamelCase:     Optional[Any]							= None
  # the split name of split_dict takes over the name of the split info object
  _UpperCamelCase:     Any							= split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''  ,					[SplitInfo(), SplitInfo(dataset_name=lowercase		), SplitInfo(dataset_name='''my_dataset'''		)]		)
def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
 # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
 _UpperCamelCase:     Optional[Any]							= asdict(SplitDict({'''train''': split_info}		)		)
 assert "dataset_name" in split_dict_asdict["train"]
 assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name