

'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
							from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

							from .test_feature_extraction_speechta import floats_list


_lowerCamelCase    =  get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_torch
class 					__a   (		unittest.TestCase  ):

						def 	_UpperCAmelCase							(		self	:						Dict)						->str:

							"""simple docstring"""



							_lowercase							=     tempfile.mkdtemp()

							_lowercase							=     SpeechTaTokenizer(lowercase__)
							tokenizer.save_pretrained(self.tmpdirname)

							_lowercase							=     {
							    """feature_size""": 1,
							    """padding_value""": 0.0,
							    """sampling_rate""": 1_60_00,
							    """do_normalize""": False,
							    """num_mel_bins""": 80,
							    """hop_length""": 16,
							    """win_length""": 64,
							    """win_function""": """hann_window""",
							    """fmin""": 80,
							    """fmax""": 76_00,
							    """mel_floor""": 1e-10,
							    """reduction_factor""": 2,
							    """return_attention_mask""": True,
							}

							_lowercase							=     os.path.join(self.tmpdirname    ,			lowercase__)
							with open(self.feature_extraction_file    ,			"""w"""    ,			encoding="""utf-8""") as fp:
								fp.write(json.dumps(lowercase__) + """\n""")

						def 	_UpperCAmelCase							(		self	:						Optional[int]    ,			**lowercase__	:						List[Any])						->Optional[int]:

							"""simple docstring"""



							return SpeechTaTokenizer.from_pretrained(self.tmpdirname    ,			**lowercase__)

						def 	_UpperCAmelCase							(		self	:						Dict    ,			**lowercase__	:						Optional[Any])						->str:

							"""simple docstring"""



							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname    ,			**lowercase__)

						def 	_UpperCAmelCase							(		self	:						Tuple)						->Any:

							"""simple docstring"""



							shutil.rmtree(self.tmpdirname)

						def 	_UpperCAmelCase							(		self	:						Union[str, Any])						->Dict:

							"""simple docstring"""



							_lowercase							=     self.get_tokenizer()
							_lowercase							=     self.get_feature_extractor()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

							processor.save_pretrained(self.tmpdirname)
							_lowercase							=     SpeechTaProcessor.from_pretrained(self.tmpdirname)

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

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

						def 	_UpperCAmelCase							(		self	:						Dict)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     SpeechTaProcessor(tokenizer=self.get_tokenizer()    ,			feature_extractor=self.get_feature_extractor())
							processor.save_pretrained(self.tmpdirname)

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

							_lowercase							=     SpeechTaProcessor.from_pretrained(
							    self.tmpdirname    ,			bos_token="""(BOS)"""    ,			eos_token="""(EOS)"""    ,			do_normalize=lowercase__    ,			padding_value=1.0)

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

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

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

							"""simple docstring"""



							_lowercase							=     self.get_feature_extractor()
							_lowercase							=     self.get_tokenizer()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

							_lowercase							=     floats_list((3, 10_00))

							_lowercase							=     feature_extractor(audio=lowercase__    ,			return_tensors="""np""")
							_lowercase							=     processor(audio=lowercase__    ,			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 	_UpperCAmelCase							(		self	:						Any)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     self.get_feature_extractor()
							_lowercase							=     self.get_tokenizer()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

							_lowercase							=     floats_list((3, 10_00))

							_lowercase							=     feature_extractor(audio_target=lowercase__    ,			return_tensors="""np""")
							_lowercase							=     processor(audio_target=lowercase__    ,			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 	_UpperCAmelCase							(		self	:						str)						->Dict:

							"""simple docstring"""



							_lowercase							=     self.get_feature_extractor()
							_lowercase							=     self.get_tokenizer()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

							_lowercase							=     """This is a test string"""

							_lowercase							=     processor(text=lowercase__)
							_lowercase							=     tokenizer(lowercase__)

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

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Optional[Any]:

							"""simple docstring"""



							_lowercase							=     self.get_feature_extractor()
							_lowercase							=     self.get_tokenizer()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

							_lowercase							=     """This is a test string"""

							_lowercase							=     processor(text_target=lowercase__)
							_lowercase							=     tokenizer(lowercase__)

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

						def 	_UpperCAmelCase							(		self	:						Any)						->Dict:

							"""simple docstring"""



							_lowercase							=     self.get_feature_extractor()
							_lowercase							=     self.get_tokenizer()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

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

							_lowercase							=     processor.batch_decode(lowercase__)
							_lowercase							=     tokenizer.batch_decode(lowercase__)

							self.assertListEqual(lowercase__    ,			lowercase__)


						def 	_UpperCAmelCase							(		self	:						Dict)						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     self.get_feature_extractor()
							_lowercase							=     self.get_tokenizer()

							_lowercase							=     SpeechTaProcessor(tokenizer=lowercase__    ,			feature_extractor=lowercase__)

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







'''simple docstring'''







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     HfArgumentParser(snake_case_	)
	try:
		_lowercase							=     parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		_lowercase							=     """Arg --no_{0} is no longer used, please use --no-{0} instead."""
		_lowercase							=     """ """.join(str(snake_case_	).split(""" """	)[:-1]	)
		_lowercase							=     """"""
		_lowercase							=     eval(str(snake_case_	).split(""" """	)[-1]	)
		_lowercase							=     []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(snake_case_	)
		if len(snake_case_	) > 0:
			_lowercase							=     full_error_msg + begin_error_msg + str(snake_case_	)
		raise ValueError(snake_case_	)

	_lowercase							=     PyTorchBenchmark(args=snake_case_	)
	benchmark.run()


if __name__ == "__main__":
							main()




