




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 		__A      (			unittest.TestCase			):

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	int								="""laion/clap-htsat-unfused"""
										__magic_name__	:	Any								=tempfile.mkdtemp()

						def     A__						(		self   :List[str]    , **__snake_case   :int ):
										'''simple docstring'''

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

						def     A__						(		self   :Optional[int]    , **__snake_case   :List[Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										shutil.rmtree(self.tmpdirname )

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	str								=self.get_tokenizer()
										__magic_name__	:	Dict								=self.get_feature_extractor()

										__magic_name__	:	List[str]								=ClapProcessor(tokenizer=__snake_case    , feature_extractor=__snake_case )

										processor.save_pretrained(self.tmpdirname )
										__magic_name__	:	Optional[int]								=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     A__						(		self   :Tuple ):
										'''simple docstring'''

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

										__magic_name__	:	Optional[Any]								=self.get_tokenizer(bos_token="""(BOS)"""    , eos_token="""(EOS)""" )
										__magic_name__	:	Optional[int]								=self.get_feature_extractor(do_normalize=__snake_case    , padding_value=1.0 )

										__magic_name__	:	str								=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     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.get_feature_extractor()
										__magic_name__	:	Any								=self.get_tokenizer()

										__magic_name__	:	Optional[Any]								=ClapProcessor(tokenizer=__snake_case    , feature_extractor=__snake_case )

										__magic_name__	:	Any								=floats_list((3, 10_00) )

										__magic_name__	:	str								=feature_extractor(__snake_case    , return_tensors="""np""" )
										__magic_name__	:	List[Any]								=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     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.get_feature_extractor()
										__magic_name__	:	List[Any]								=self.get_tokenizer()

										__magic_name__	:	List[Any]								=ClapProcessor(tokenizer=__snake_case    , feature_extractor=__snake_case )

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

										__magic_name__	:	Union[str, Any]								=processor(text=__snake_case )

										__magic_name__	:	Union[str, Any]								=tokenizer(__snake_case )

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

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.get_feature_extractor()
										__magic_name__	:	Union[str, Any]								=self.get_tokenizer()

										__magic_name__	:	int								=ClapProcessor(tokenizer=__snake_case    , feature_extractor=__snake_case )

										__magic_name__	:	List[Any]								=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__magic_name__	:	List[str]								=processor.batch_decode(__snake_case )
										__magic_name__	:	int								=tokenizer.batch_decode(__snake_case )

										self.assertListEqual(__snake_case    , __snake_case )


						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.get_feature_extractor()
										__magic_name__	:	Optional[int]								=self.get_tokenizer()

										__magic_name__	:	int								=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"""    , )










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




