

'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
							import torch


_lowerCamelCase    =  random.Random()






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=1.0							,       snake_case_=None							,       snake_case_=None	):
	if rng is None:
		_lowercase							=     global_rng

	_lowercase							=     []
	for batch_idx in range(shape[0]	):
		values.append([]	)
		for _ in range(shape[1]	):
			values[-1].append(rng.random() * scale	)

	return values
@require_torch
class 					__a   (		unittest.TestCase  ):

						def __init__(		self	:						int    ,			lowercase__	:						Optional[Any]    ,			lowercase__	:						Dict=7    ,			lowercase__	:						int=4_00    ,			lowercase__	:						int=20_00    ,			lowercase__	:						Dict=1    ,			lowercase__	:						Optional[int]=0.0    ,			lowercase__	:						List[Any]=2_40_00    ,			lowercase__	:						str=True    ,			)						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     parent
							_lowercase							=     batch_size
							_lowercase							=     min_seq_length
							_lowercase							=     max_seq_length
							_lowercase							=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							_lowercase							=     feature_size
							_lowercase							=     padding_value
							_lowercase							=     sampling_rate
							_lowercase							=     return_attention_mask

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

							"""simple docstring"""



							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}


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

							"""simple docstring"""



							def _flatten(lowercase__	:						List[Any]):
								return list(itertools.chain(*lowercase__))

							if equal_length:
								_lowercase							=     floats_list((self.batch_size, self.max_seq_length))
							else:
								# make sure that inputs increase in size
								_lowercase							=     [
								    _flatten(floats_list((x, self.feature_size)))
								    for x in range(self.min_seq_length    ,			self.max_seq_length    ,			self.seq_length_diff)
								]

							if numpify:
								_lowercase							=     [np.asarray(lowercase__) for x in audio_inputs]

							return audio_inputs






@require_torch
class 					__a   (		_snake_case							,unittest.TestCase  ):
						__SCREAMING_SNAKE_CASE     :					Any  =      EncodecFeatureExtractor

						def 	_UpperCAmelCase							(		self	:						int)						->int:

							"""simple docstring"""



							_lowercase							=     EnCodecFeatureExtractionTester(self)

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

							"""simple docstring"""



							_lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							# create three inputs of length 800, 1000, and 1200
							_lowercase							=     [floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
							_lowercase							=     [np.asarray(lowercase__) for audio_input in audio_inputs]

							# Test not batched input
							_lowercase							=     feat_extract(audio_inputs[0]    ,			return_tensors="""np""").input_values
							_lowercase							=     feat_extract(np_audio_inputs[0]    ,			return_tensors="""np""").input_values
							self.assertTrue(np.allclose(lowercase__    ,			lowercase__    ,			atol=1e-3))

							# Test batched
							_lowercase							=     feat_extract(lowercase__    ,			padding=lowercase__    ,			return_tensors="""np""").input_values
							_lowercase							=     feat_extract(lowercase__    ,			padding=lowercase__    ,			return_tensors="""np""").input_values
							for enc_seq_a, enc_seq_a in zip(lowercase__    ,			lowercase__):
								self.assertTrue(np.allclose(lowercase__    ,			lowercase__    ,			atol=1e-3))

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

							"""simple docstring"""



							_lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							_lowercase							=     np.random.rand(1_00).astype(np.floataa)
							_lowercase							=     np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
								_lowercase							=     feature_extractor.pad([{"""input_values""": inputs}]    ,			return_tensors="""np""")
								self.assertTrue(np_processed.input_values.dtype == np.floataa)
								_lowercase							=     feature_extractor.pad([{"""input_values""": inputs}]    ,			return_tensors="""pt""")
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa)

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						List[str])						->Optional[int]:

							"""simple docstring"""



							from datasets import load_dataset

							_lowercase							=     load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,			"""clean"""    ,			split="""validation""")
							# automatic decoding with librispeech
							_lowercase							=     ds.sort("""id""").select(range(lowercase__))[:num_samples]["""audio"""]

							return [x["array"] for x in audio_samples]

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

							"""simple docstring"""



							_lowercase							=     torch.tensor(
							    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
							     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
							     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
							     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
							     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
							     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03])
							# fmt: on
							_lowercase							=     self._load_datasamples(1)
							_lowercase							=     EncodecFeatureExtractor()
							_lowercase							=     feature_extractor(lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(input_values.shape    ,			(1, 1, 9_36_80))
							self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,			lowercase__    ,			atol=1e-6))

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

							"""simple docstring"""



							_lowercase							=     torch.tensor(
							    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
							     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
							     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
							     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
							     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
							     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03])
							# fmt: on
							_lowercase							=     self._load_datasamples(1)
							_lowercase							=     [np.tile(input_audio[0][None]    ,			reps=(2, 1))]
							input_audio[0][1] *= 0.5
							_lowercase							=     EncodecFeatureExtractor(feature_size=2)
							_lowercase							=     feature_extractor(lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(input_values.shape    ,			(1, 2, 9_36_80))
							self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,			lowercase__    ,			atol=1e-6))
							self.assertTrue(torch.allclose(input_values[0, 1, :30]    ,			EXPECTED_INPUT_VALUES * 0.5    ,			atol=1e-6))


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

							"""simple docstring"""



							_lowercase							=     self._load_datasamples(2)
							# would be easier if the stride was like
							_lowercase							=     EncodecFeatureExtractor(feature_size=1    ,			chunk_length_s=1    ,			overlap=0.01)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    lowercase__    ,			"""^Both padding and truncation were set. Make sure you only set one.$"""    ,			):
								_lowercase							=     feature_extractor(
								    lowercase__    ,			padding="""max_length"""    ,			truncation=lowercase__    ,			return_tensors="""pt""").input_values

							# truncate to chunk
							_lowercase							=     feature_extractor(lowercase__    ,			truncation=lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(2, 1, 7_15_20))  # 2 chunks

							# force truncate to max_length
							_lowercase							=     feature_extractor(
							    lowercase__    ,			truncation=lowercase__    ,			max_length=4_80_00    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(2, 1, 4_80_00))

							# pad to chunk
							_lowercase							=     feature_extractor(lowercase__    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(padded_outputs.shape    ,			(2, 1, 9_52_80))

							# pad to chunk
							_lowercase							=     feature_extractor(lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(2, 1, 9_52_80))

							# force pad to max length
							_lowercase							=     feature_extractor(
							    lowercase__    ,			padding="""max_length"""    ,			max_length=10_00_00    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(2, 1, 10_00_00))

							# force no pad
							with self.assertRaisesRegex(
							    lowercase__    ,			"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,			):
								_lowercase							=     feature_extractor(lowercase__    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values

							_lowercase							=     feature_extractor(input_audio[0]    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(1, 1, 9_36_80))

							# no pad if no chunk_length_s
							_lowercase							=     None
							with self.assertRaisesRegex(
							    lowercase__    ,			"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,			):
								_lowercase							=     feature_extractor(lowercase__    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values

							_lowercase							=     feature_extractor(input_audio[0]    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(1, 1, 9_36_80))

							# no pad if no overlap
							_lowercase							=     2
							_lowercase							=     None
							with self.assertRaisesRegex(
							    lowercase__    ,			"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,			):
								_lowercase							=     feature_extractor(lowercase__    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values

							_lowercase							=     feature_extractor(input_audio[0]    ,			padding=lowercase__    ,			return_tensors="""pt""").input_values
							self.assertEquals(truncated_outputs.shape    ,			(1, 1, 9_36_80))







'''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()




