




"""simple docstring"""





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


A   =       get_logger(__name__)



class      a__  (	enum.Enum   ):
			lowercase_    =	"""all_checks"""
			lowercase_    =	"""basic_checks"""
			lowercase_    =	"""no_checks"""






class      a__  (	__magic_name__   ):
			pass






class      a__  (	__magic_name__   ):
			pass






class      a__  (	__magic_name__   ):
			pass






class      a__  (	__magic_name__   ):
			pass






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=None							)			->			Dict:


			"""simple docstring"""






			if expected_checksums is None:
						logger.info("Unable to verify checksums."							)
						return
			if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
			if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
						raise UnexpectedDownloadedFile(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
			__UpperCAmelCase	: Any		=					[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__UpperCAmelCase	: Dict		=					" for " + verification_name if verification_name is not None else ""
			if len(_UpperCamelCase							) > 0:
						raise NonMatchingChecksumError(
						    f"Checksums didn't match{for_verification_name}:\n"
						    f"{bad_urls}\n"
						    "Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error"							)
			logger.info("All the checksums matched successfully" + for_verification_name							)



class      a__  (	__magic_name__   ):
			pass






class      a__  (	__magic_name__   ):
			pass






class      a__  (	__magic_name__   ):
			pass






class      a__  (	__magic_name__   ):
			pass






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""






			if expected_splits is None:
						logger.info("Unable to verify splits sizes."							)
						return
			if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
						raise ExpectedMoreSplits(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
			if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
						raise UnexpectedSplits(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
			__UpperCAmelCase	: Optional[Any]		=					[
			    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(_UpperCamelCase							) > 0:
						raise NonMatchingSplitsSizesError(str(_UpperCamelCase							)							)
			logger.info("All the splits matched successfully."							)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = True							)			->			dict:


			"""simple docstring"""






			if record_checksum:
						__UpperCAmelCase	: str		=					shaaaa()
						with open(_UpperCamelCase				,       "rb"							) as f:
									for chunk in iter(lambda: f.read(1 << 20							)				,       B""							):
												m.update(_UpperCamelCase							)
									__UpperCAmelCase	: Dict		=					m.hexdigest()
			else:
						__UpperCAmelCase	: Union[str, Any]		=					None
			return {"num_bytes": os.path.getsize(_UpperCamelCase							), "checksum": checksum}





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""






			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False








"""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   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					"laion/clap-htsat-unfused"
						__UpperCAmelCase	: List[str]		=					tempfile.mkdtemp()


			def       a_     (      self      :    Optional[Any]      ,  **UpperCamelCase_      :    Union[str, Any]):


						"""simple docstring"""




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


			def       a_     (      self      :    Union[str, Any]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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

						__UpperCAmelCase	: List[Any]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Union[str, Any]		=					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       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor())
						processor.save_pretrained(self.tmpdirname)

						__UpperCAmelCase	: int		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")
						__UpperCAmelCase	: Any		=					self.get_feature_extractor(do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						__UpperCAmelCase	: Tuple		=					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       a_     (      self      :    Any):


						"""simple docstring"""




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

						__UpperCAmelCase	: Any		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					floats_list((3, 1000))

						__UpperCAmelCase	: Tuple		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np")
						__UpperCAmelCase	: Any		=					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       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_feature_extractor()
						__UpperCAmelCase	: Optional[int]		=					self.get_tokenizer()

						__UpperCAmelCase	: str		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					"This is a test string"

						__UpperCAmelCase	: List[Any]		=					processor(text=UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					tokenizer(UpperCamelCase_)

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


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.get_feature_extractor()
						__UpperCAmelCase	: List[str]		=					self.get_tokenizer()

						__UpperCAmelCase	: List[str]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__UpperCAmelCase	: int		=					processor.batch_decode(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					tokenizer.batch_decode(UpperCamelCase_)

						self.assertListEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: int		=					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"      ,  )


