
"""simple docstring"""


def  _snake_case			(						snake_case__				:       float		, snake_case__				:       float       ):
	return a + b


if __name__ == "__main__":
		_lowercase							     =			5
		_lowercase							     =			6
		print(F"""The sum of {a} + {b} is {add(a, b)}""")

"""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


_lowercase											=			random.Random()






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Union[str, Any]=1.0		, snake_case__				:       int=None		, snake_case__				:       Dict=None       ):
	if rng is None:
		A  				=				global_rng

	A  				=				[]
	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 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : int					,A_       : str					,A_       : List[str]=7					,A_       : Tuple=400					,A_       : Union[str, Any]=2000					,A_       : Optional[Any]=1					,A_       : Optional[int]=0.0					,A_       : str=2_4000					,A_       : Optional[int]=True					,)       ->				Dict:
							A  				=				parent
							A  				=				batch_size
							A  				=				min_seq_length
							A  				=				max_seq_length
							A  				=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A  				=				feature_size
							A  				=				padding_value
							A  				=				sampling_rate
							A  				=				return_attention_mask


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Optional[int]:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}




						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : List[str]=False					,A_       : str=False )       ->				Optional[Any]:
							def _flatten(A_       : int ):
								return list(itertools.chain(*A_ ) )

							if equal_length:
								A  				=				floats_list((self.batch_size, self.max_seq_length) )
							else:
								# make sure that inputs increase in size
								A  				=				[
								    _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:
								A  				=				[np.asarray(A_ ) for x in audio_inputs]

							return audio_inputs







@require_torch
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      EncodecFeatureExtractor


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[Any]:
							A  				=				EnCodecFeatureExtractionTester(self )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				str:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							# create three inputs of length 800, 1000, and 1200
							A  				=				[floats_list((1, x) )[0] for x in range(800					,1400					,200 )]
							A  				=				[np.asarray(A_ ) for audio_input in audio_inputs]

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

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


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							A  				=				np.random.rand(100 ).astype(np.floataa )
							A  				=				np_audio_inputs.tolist()

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


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : int )       ->				List[Any]:
							from datasets import load_dataset

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

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


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[int]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				EncodecFeatureExtractor()
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 1, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				List[Any]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				[np.tile(input_audio[0][None]					,reps=(2, 1) )]
							input_audio[0][1] *= 0.5
							A  				=				EncodecFeatureExtractor(feature_size=2 )
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 2, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]					,EXPECTED_INPUT_VALUES * 0.5					,atol=1e-6 ) )




						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				List[str]:
							A  				=				self._load_datasamples(2 )
							# would be easier if the stride was like
							A  				=				EncodecFeatureExtractor(feature_size=1					,chunk_length_s=1					,overlap=0.01 )

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

							# truncate to chunk
							A  				=				feature_extractor(A_					,truncation=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 7_1520) )  # 2 chunks

							# force truncate to max_length
							A  				=				feature_extractor(
							    A_					,truncation=A_					,max_length=4_8000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 4_8000) )

							# pad to chunk
							A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(padded_outputs.shape					,(2, 1, 9_5280) )

							# pad to chunk
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 9_5280) )

							# force pad to max length
							A  				=				feature_extractor(
							    A_					,padding='max_length'					,max_length=10_0000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 10_0000) )

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

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

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

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

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

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )