


"""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 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
					a					='''laion/clap-htsat-unfused'''
					a					=tempfile.mkdtemp()


	def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					int:
					return RobertaTokenizer.from_pretrained(self.checkpoint			,   **__A						)


	def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					List[Any]:
					return ClapFeatureExtractor.from_pretrained(self.checkpoint			,   **__A						)


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
					shutil.rmtree(self.tmpdirname						)


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					a					=self.get_tokenizer()
					a					=self.get_feature_extractor()

					a					=ClapProcessor(tokenizer=__A			,   feature_extractor=__A						)

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

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

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


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					a					=ClapProcessor(tokenizer=self.get_tokenizer()			,   feature_extractor=self.get_feature_extractor()						)
					processor.save_pretrained(self.tmpdirname						)

					a					=self.get_tokenizer(bos_token='''(BOS)'''			,   eos_token='''(EOS)'''						)
					a					=self.get_feature_extractor(do_normalize=__A			,   padding_value=1.0						)

					a					=ClapProcessor.from_pretrained(
					    self.tmpdirname			,   bos_token='''(BOS)'''			,   eos_token='''(EOS)'''			,   do_normalize=__A			,   padding_value=1.0						)

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

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


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					a					=self.get_feature_extractor()
					a					=self.get_tokenizer()

					a					=ClapProcessor(tokenizer=__A			,   feature_extractor=__A						)

					a					=floats_list((3, 1000)						)

					a					=feature_extractor(__A			,   return_tensors='''np'''						)
					a					=processor(audios=__A			,   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 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
					a					=self.get_feature_extractor()
					a					=self.get_tokenizer()

					a					=ClapProcessor(tokenizer=__A			,   feature_extractor=__A						)

					a					='''This is a test string'''

					a					=processor(text=__A						)

					a					=tokenizer(__A						)

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


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					a					=self.get_feature_extractor()
					a					=self.get_tokenizer()

					a					=ClapProcessor(tokenizer=__A			,   feature_extractor=__A						)

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

					a					=processor.batch_decode(__A						)
					a					=tokenizer.batch_decode(__A						)

					self.assertListEqual(__A			,   __A						)





	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					a					=self.get_feature_extractor()
					a					=self.get_tokenizer()

					a					=ClapProcessor(tokenizer=__A			,   feature_extractor=__A						)

					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"""







from __future__ import annotations
def _A						(						lowercase	,   lowercase	,   lowercase	,   ):







    """simple docstring"""
    if (electron_conc, hole_conc, intrinsic_conc).count(0		) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''		)
    elif electron_conc < 0:
        raise ValueError('''Electron concentration cannot be negative in a semiconductor'''		)
    elif hole_conc < 0:
        raise ValueError('''Hole concentration cannot be negative in a semiconductor'''		)
    elif intrinsic_conc < 0:
        raise ValueError(
            '''Intrinsic concentration cannot be negative in a semiconductor'''		)
    elif electron_conc == 0:
        return (
            "electron_conc",
            intrinsic_conc**2 / hole_conc,
        )
    elif hole_conc == 0:
        return (
            "hole_conc",
            intrinsic_conc**2 / electron_conc,
        )
    elif intrinsic_conc == 0:
        return (
            "intrinsic_conc",
            (electron_conc * hole_conc) ** 0.5,
        )
    else:
        return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()