

"""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


__A     :    str    =		random.Random()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__=1.0			,							lowercase__=None			,							lowercase__=None      ):
					"""simple docstring"""
					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 			__UpperCamelCase (   unittest.TestCase						):


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str=7     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=4_0_0     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_0_0_0     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1     ,     __SCREAMING_SNAKE_CASE :		Any=0.0     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_4_0_0_0     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=True     ,     ):
							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 :		Dict):
							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 :		Tuple     ,     __SCREAMING_SNAKE_CASE :		List[Any]=False     ,     __SCREAMING_SNAKE_CASE :		Any=False):
							def _flatten(__SCREAMING_SNAKE_CASE :		Any):
												return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

							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(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

							return audio_inputs





@require_torch
class 			__UpperCamelCase (   _A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    EncodecFeatureExtractor


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					EnCodecFeatureExtractionTester(self)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							# 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(8_0_0     ,     1_4_0_0     ,     2_0_0)]
							A			=					[np.asarray(__SCREAMING_SNAKE_CASE) 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(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-3))

							# Test batched
							A			=					feat_extract(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="np").input_values
							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							A			=					np.random.rand(1_0_0).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 :		Any     ,     __SCREAMING_SNAKE_CASE :		Optional[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(__SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							# fmt: off
							A			=					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
							A			=					self._load_datasamples(1)
							A			=					EncodecFeatureExtractor()
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(input_values.shape     ,     (1, 1, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-6))


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							# fmt: off
							A			=					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
							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(__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(input_values.shape     ,     (1, 2, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-6))
							self.assertTrue(torch.allclose(input_values[0, 1, :3_0]     ,     EXPECTED_INPUT_VALUES * 0.5     ,     atol=1E-6))


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					self._load_datasamples(2)
							# would be easier if the stride was like
							A			=					EncodecFeatureExtractor(feature_size=1     ,     chunk_length_s=1     ,     overlap=0.0_1)

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

							# truncate to chunk
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 7_1_5_2_0))  # 2 chunks

							# force truncate to max_length
							A			=					feature_extractor(
							    __SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE     ,     max_length=4_8_0_0_0     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 4_8_0_0_0))

							# pad to chunk
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(padded_outputs.shape     ,     (2, 1, 9_5_2_8_0))

							# pad to chunk
							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 9_5_2_8_0))

							# force pad to max length
							A			=					feature_extractor(
							    __SCREAMING_SNAKE_CASE     ,     padding="max_length"     ,     max_length=1_0_0_0_0_0     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (2, 1, 1_0_0_0_0_0))

							# 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.$"     ,     ):
												A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							A			=					feature_extractor(input_audio[0]     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (1, 1, 9_3_6_8_0))

							# no pad if no chunk_length_s
							A			=					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.$"     ,     ):
												A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							A			=					feature_extractor(input_audio[0]     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (1, 1, 9_3_6_8_0))

							# no pad if no overlap
							A			=					2
							A			=					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.$"     ,     ):
												A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values

							A			=					feature_extractor(input_audio[0]     ,     padding=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape     ,     (1, 1, 9_3_6_8_0))









from __future__ import annotations






def 		a__       (							A__							):
			if len(A__							) == 0:
						raise ValueError('find_max() arg is an empty sequence'							)
			SCREAMING_SNAKE_CASE_						:			str       						= nums[0]
			for x in nums:
						if x > max_num:
									SCREAMING_SNAKE_CASE_						:			Optional[int]       						= x
			return max_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)





