


"""simple docstring"""
import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Dict:
							snake_case_          =						test_file.split(os.path.sep			)
							if components[0:2] != ["tests", "models"]:
														raise ValueError(
														    """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
														    f"""{test_file} instead."""			)
							snake_case_          =						components[-1]
							if not test_fn.endswith("""py"""			):
														raise ValueError(f"""`test_file` should be a python file. Got {test_fn} instead."""			)
							if not test_fn.startswith("""test_modeling_"""			):
														raise ValueError(
														    f"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""			)

							snake_case_          =						components[:-1] + [test_fn.replace(""".py"""			,	""""""			)]
							snake_case_          =						""".""".join(_SCREAMING_SNAKE_CASE			)

							return test_module_path







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						get_module_path(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						importlib.import_module(_SCREAMING_SNAKE_CASE			)

							return test_module







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						[]
							snake_case_          =						get_test_module(_SCREAMING_SNAKE_CASE			)
							for attr in dir(_SCREAMING_SNAKE_CASE			):
														if attr.endswith("""ModelTester"""			):
																					tester_classes.append(getattr(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)			)

    # sort with class names
							return sorted(_SCREAMING_SNAKE_CASE			,	key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						[]
							snake_case_          =						get_test_module(_SCREAMING_SNAKE_CASE			)
							for attr in dir(_SCREAMING_SNAKE_CASE			):
														snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
														# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
														# `all_model_classes` is not empty (which also excludes other special classes).
														snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""all_model_classes"""			,	[]			)
														if len(_SCREAMING_SNAKE_CASE			) > 0:
																					test_classes.append(_SCREAMING_SNAKE_CASE			)

    # sort with class names
							return sorted(_SCREAMING_SNAKE_CASE			,	key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						get_test_classes(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						set()
							for test_class in test_classes:
														model_classes.update(test_class.all_model_classes			)

							# sort with class names
							return sorted(_SCREAMING_SNAKE_CASE			,	key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Any:
							snake_case_          =						test_class()
							if hasattr(_SCREAMING_SNAKE_CASE			,	"""setUp"""			):
														test.setUp()

							snake_case_          =						None
							if hasattr(_SCREAMING_SNAKE_CASE			,	"""model_tester"""			):
														# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
														if test.model_tester is not None:
																					snake_case_          =						test.model_tester.__class__

							return model_tester







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						get_test_classes(_SCREAMING_SNAKE_CASE			)

							snake_case_          =						[]
							for test_class in test_classes:
														if model_class in test_class.all_model_classes:
																					target_test_classes.append(_SCREAMING_SNAKE_CASE			)

    # sort with class names
							return sorted(_SCREAMING_SNAKE_CASE			,	key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							snake_case_          =						get_test_classes_for_model(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							snake_case_          =						[]
							for test_class in test_classes:
														snake_case_          =						get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE			)
														if tester_class is not None:
																					tester_classes.append(_SCREAMING_SNAKE_CASE			)

    # sort with class names
							return sorted(_SCREAMING_SNAKE_CASE			,	key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							snake_case_          =						get_test_classes(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						{test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE			) for test_class in test_classes}
							return test_tester_mapping







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						get_model_classes(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						{
							    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) for model_class in model_classes
							}
							return model_test_mapping







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						get_model_classes(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						{
							    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) for model_class in model_classes
							}
							return model_to_tester_mapping







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			str:
							if isinstance(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														return o
							elif isinstance(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														return o.__name__
							elif isinstance(_SCREAMING_SNAKE_CASE			,	(list, tuple)			):
														return [to_json(_SCREAMING_SNAKE_CASE			) for x in o]
							elif isinstance(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														return {to_json(_SCREAMING_SNAKE_CASE			): to_json(_SCREAMING_SNAKE_CASE			) for k, v in o.items()}
							else:
														return o







"""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        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Dict    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""laion/clap-htsat-unfused"""
														snake_case_          =						tempfile.mkdtemp()



							def 						lowerCAmelCase    (			self							: List[str]						,    **UpperCAmelCase_							: Tuple    )   ->str:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    **UpperCAmelCase_							: Any    )   ->Optional[Any]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: str    )   ->Any:



														"""simple docstring"""



														shutil.rmtree(self.tmpdirname    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_tokenizer()
														snake_case_          =						self.get_feature_extractor()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														processor.save_pretrained(self.tmpdirname    )
														snake_case_          =						ClapProcessor.from_pretrained(self.tmpdirname    )

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

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



							def 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()    )
														processor.save_pretrained(self.tmpdirname    )

														snake_case_          =						self.get_tokenizer(bos_token="""(BOS)"""						,    eos_token="""(EOS)"""    )
														snake_case_          =						self.get_feature_extractor(do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

														snake_case_          =						ClapProcessor.from_pretrained(
														    self.tmpdirname						,    bos_token="""(BOS)"""						,    eos_token="""(EOS)"""						,    do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

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

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



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						floats_list((3, 1_000)    )

														snake_case_          =						feature_extractor(UpperCAmelCase_						,    return_tensors="""np"""    )
														snake_case_          =						processor(audios=UpperCAmelCase_						,    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							: Union[str, Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						"""This is a test string"""

														snake_case_          =						processor(text=UpperCAmelCase_    )

														snake_case_          =						tokenizer(UpperCAmelCase_    )

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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														snake_case_          =						processor.batch_decode(UpperCAmelCase_    )
														snake_case_          =						tokenizer.batch_decode(UpperCAmelCase_    )

														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

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



