





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


UpperCAmelCase_   				=  random.Random()





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							int=1.0							,     UpperCamelCase__      :							Dict=None							,     UpperCamelCase__      :							Any=None    )      ->			Tuple:



					'''simple docstring'''




					if rng is None:
										_snake_case						=					global_rng

					_snake_case						=					[]
					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       UpperCamelCase_		(	unittest.TestCase       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=7					,    lowerCAmelCase_=400					,    lowerCAmelCase_=2000					,    lowerCAmelCase_=1					,    lowerCAmelCase_=0.0					,    lowerCAmelCase_=2_4000					,    lowerCAmelCase_=True					,    )		->				str:
										_snake_case						=					parent
										_snake_case						=					batch_size
										_snake_case						=					min_seq_length
										_snake_case						=					max_seq_length
										_snake_case						=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										_snake_case						=					feature_size
										_snake_case						=					padding_value
										_snake_case						=					sampling_rate
										_snake_case						=					return_attention_mask




					def 						lowerCAmelCase      (		self	)		->				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 						lowerCAmelCase      (		self					,    lowerCAmelCase_=False					,    lowerCAmelCase_=False	)		->				Tuple:
										def _flatten(lowerCAmelCase_	):
															return list(itertools.chain(*SCREAMING_SNAKE_CASE_	)	)

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

										return audio_inputs





@require_torch
class       UpperCamelCase_		(	snake_case_				,	unittest.TestCase       ):
					lowerCAmelCase_				=						EncodecFeatureExtractor




					def 						lowerCAmelCase      (		self	)		->				Union[str, Any]:
										_snake_case						=					EnCodecFeatureExtractionTester(self	)




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

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

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




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

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




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										from datasets import load_dataset

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

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




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										# fmt: off
										_snake_case						=					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
										_snake_case						=					self._load_datasamples(1	)
										_snake_case						=					EncodecFeatureExtractor()
										_snake_case						=					feature_extractor(SCREAMING_SNAKE_CASE_					,    return_tensors='pt'	).input_values
										self.assertEquals(input_values.shape					,    (1, 1, 9_3680)	)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]					,    SCREAMING_SNAKE_CASE_					,    atol=1E-6	)	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										# fmt: off
										_snake_case						=					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
										_snake_case						=					self._load_datasamples(1	)
										_snake_case						=					[np.tile(input_audio[0][None]					,    reps=(2, 1)	)]
										input_audio[0][1] *= 0.5
										_snake_case						=					EncodecFeatureExtractor(feature_size=2	)
										_snake_case						=					feature_extractor(SCREAMING_SNAKE_CASE_					,    return_tensors='pt'	).input_values
										self.assertEquals(input_values.shape					,    (1, 2, 9_3680)	)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]					,    SCREAMING_SNAKE_CASE_					,    atol=1E-6	)	)
										self.assertTrue(torch.allclose(input_values[0, 1, :30]					,    EXPECTED_INPUT_VALUES * 0.5					,    atol=1E-6	)	)






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

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

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

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

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

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

										# force pad to max length
										_snake_case						=					feature_extractor(
										    SCREAMING_SNAKE_CASE_					,    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(
										    SCREAMING_SNAKE_CASE_					,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,    ):
															_snake_case						=					feature_extractor(SCREAMING_SNAKE_CASE_					,    padding=SCREAMING_SNAKE_CASE_					,    return_tensors='pt'	).input_values

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

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

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

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

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









"""simple docstring"""





_lowercase						   =     [0.4_85, 0.4_56, 0.4_06]
_lowercase						   =     [0.2_29, 0.2_24, 0.2_25]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
_lowercase						   =     [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]


