


'''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  lowerCAmelCase				(				unittest.TestCase							):



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



	def 				lowercase     (					self ,					**snake_case__    ):
		return RobertaTokenizer.from_pretrained(self.checkpoint ,					**snake_case__    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return ClapFeatureExtractor.from_pretrained(self.checkpoint ,					**snake_case__    )



	def 				lowercase     (					self    ):
		shutil.rmtree(self.tmpdirname    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.get_tokenizer()
		lowerCAmelCase					:    str      							= self.get_feature_extractor()

		lowerCAmelCase					:    Optional[int]      							= ClapProcessor(tokenizer=snake_case__ ,					feature_extractor=snake_case__    )

		processor.save_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    str      							= ClapProcessor.from_pretrained(self.tmpdirname    )

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

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= ClapProcessor(tokenizer=self.get_tokenizer() ,					feature_extractor=self.get_feature_extractor()    )
		processor.save_pretrained(self.tmpdirname    )

		lowerCAmelCase					:    int      							= self.get_tokenizer(bos_token='(BOS)' ,					eos_token='(EOS)'    )
		lowerCAmelCase					:    List[Any]      							= self.get_feature_extractor(do_normalize=snake_case__ ,					padding_value=1.0    )

		lowerCAmelCase					:    Optional[int]      							= ClapProcessor.from_pretrained(
		    self.tmpdirname ,					bos_token='(BOS)' ,					eos_token='(EOS)' ,					do_normalize=snake_case__ ,					padding_value=1.0    )

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

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.get_feature_extractor()
		lowerCAmelCase					:    str      							= self.get_tokenizer()

		lowerCAmelCase					:    Optional[Any]      							= ClapProcessor(tokenizer=snake_case__ ,					feature_extractor=snake_case__    )

		lowerCAmelCase					:    Union[str, Any]      							= floats_list((3, 1000)    )

		lowerCAmelCase					:    List[Any]      							= feature_extractor(snake_case__ ,					return_tensors='np'    )
		lowerCAmelCase					:    Tuple      							= processor(audios=snake_case__ ,					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 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.get_feature_extractor()
		lowerCAmelCase					:    Any      							= self.get_tokenizer()

		lowerCAmelCase					:    Optional[Any]      							= ClapProcessor(tokenizer=snake_case__ ,					feature_extractor=snake_case__    )

		lowerCAmelCase					:    int      							= 'This is a test string'

		lowerCAmelCase					:    Optional[Any]      							= processor(text=snake_case__    )

		lowerCAmelCase					:    int      							= tokenizer(snake_case__    )

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.get_feature_extractor()
		lowerCAmelCase					:    List[Any]      							= self.get_tokenizer()

		lowerCAmelCase					:    Dict      							= ClapProcessor(tokenizer=snake_case__ ,					feature_extractor=snake_case__    )

		lowerCAmelCase					:    int      							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		lowerCAmelCase					:    List[str]      							= processor.batch_decode(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= tokenizer.batch_decode(snake_case__    )

		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.get_feature_extractor()
		lowerCAmelCase					:    List[str]      							= self.get_tokenizer()

		lowerCAmelCase					:    Union[str, Any]      							= ClapProcessor(tokenizer=snake_case__ ,					feature_extractor=snake_case__    )

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




'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    'files'					,   [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,   )
def 			__UpperCamelCase    (    _A    :				str					,   _A    :				List[Any]				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= tmp_path_factory.mktemp('dset_infos_dir'				)
	if "full:README.md" in files:
		with open(dataset_infos_dir / 'README.md'					,   'w'				) as f:
			f.write('---\ndataset_info:\n  dataset_size: 42\n---'				)
	if "empty:README.md" in files:
		with open(dataset_infos_dir / 'README.md'					,   'w'				) as f:
			f.write(''				)
    # we want to support dataset_infos.json for backward compatibility
	if "dataset_infos.json" in files:
		with open(dataset_infos_dir / 'dataset_infos.json'					,   'w'				) as f:
			f.write('{"default": {"dataset_size": 42}}'				)
	lowerCAmelCase					:    Union[str, Any]      							= DatasetInfosDict.from_directory(_A				)
	assert dataset_infos
	assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    'dataset_info'					,   [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,   features=Features({'a': Value('int32'				)}				)					,   builder_name='builder'					,   config_name='config'					,   version='1.0.0'					,   splits=[{'name': 'train'}]					,   download_size=42					,   ),
    ]					,   )
def 			__UpperCamelCase    (    _A    :				str					,   _A    :				DatasetInfo				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= str(_A				)
	dataset_info.write_to_directory(_A				)
	lowerCAmelCase					:    List[str]      							= DatasetInfo.from_directory(_A				)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(_A					,   'dataset_info.json'				)				)

def 			__UpperCamelCase    (    )    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= DatasetInfo(
	    description='foo'					,   citation='bar'					,   homepage='https://foo.bar'					,   license='CC0'					,   features=Features({'a': Value('int32'				)}				)					,   post_processed={}					,   supervised_keys=()					,   task_templates=[]					,   builder_name='builder'					,   config_name='config'					,   version='1.0.0'					,   splits=[{'name': 'train', 'num_examples': 42}]					,   download_checksums={}					,   download_size=13_37					,   post_processing_size=4_42					,   dataset_size=12_34					,   size_in_bytes=13_37 + 4_42 + 12_34					,   )
	lowerCAmelCase					:    Optional[int]      							= dataset_info._to_yaml_dict()
	assert sorted(_A				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		assert key in dataset_info_yaml_dict
		assert isinstance(dataset_info_yaml_dict[key]					,   (list, dict, int, str)				)
	lowerCAmelCase					:    Any      							= yaml.safe_dump(_A				)
	lowerCAmelCase					:    int      							= yaml.safe_load(_A				)
	assert dataset_info_yaml_dict == reloaded

def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= DatasetInfo()
	lowerCAmelCase					:    List[Any]      							= dataset_info._to_yaml_dict()
	assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    'dataset_infos_dict'					,   [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}				),
        DatasetInfosDict({'my_config_name': DatasetInfo()}				),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,   features=Features({'a': Value('int32'				)}				)					,   builder_name='builder'					,   config_name='config'					,   version='1.0.0'					,   splits=[{'name': 'train'}]					,   download_size=42					,   )
            }				),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42				),
                'v2': DatasetInfo(dataset_size=13_37				),
            }				),
    ]					,   )
def 			__UpperCamelCase    (    _A    :				Tuple					,   _A    :				DatasetInfosDict				)    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= str(_A				)
	dataset_infos_dict.write_to_directory(_A				)
	lowerCAmelCase					:    List[str]      							= DatasetInfosDict.from_directory(_A				)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		lowerCAmelCase					:    Tuple      							= config_name
		# the yaml representation doesn't include fields like description or citation
		# so we just test that we can recover what we can from the yaml
		lowerCAmelCase					:    Optional[Any]      							= DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
	assert dataset_infos_dict == reloaded

	if dataset_infos_dict:
		assert os.path.exists(os.path.join(_A					,   'README.md'				)				)
