



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 __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_=1.0  ,     SCREAMING_SNAKE_CASE_=None  ,     SCREAMING_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  _snake_case	(    unittest.TestCase):




				def __init__(  self    :  List[str],    __lowercase    :  Union[str, Any],    __lowercase    :  List[str]=7,    __lowercase    :  Tuple=400,    __lowercase    :  Optional[int]=2000,    __lowercase    :  Dict=1,    __lowercase    :  List[str]=0.0,    __lowercase    :  List[str]=2_4000,    __lowercase    :  List[Any]=True,    ):
							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 							A__					(  self    :  str  ):
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}




				def 							A__					(  self    :  Any,    __lowercase    :  Union[str, Any]=False,    __lowercase    :  List[Any]=False  ):
							def _flatten(__lowercase    :  Optional[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  _snake_case	(    a_			,		unittest.TestCase):
				UpperCamelCase__ :    Union[str, Any]   						=EncodecFeatureExtractor




				def 							A__					(  self    :  List[Any]  ):
							lowercase__         = EnCodecFeatureExtractionTester(self  )




				def 							A__					(  self    :  Tuple  ):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							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(800,    1400,    200  )]
							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 							A__					(  self    :  Any  ):
							lowercase__         = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
							lowercase__         = np.random.rand(100  ).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 							A__					(  self    :  Any,    __lowercase    :  List[Any]  ):
							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 							A__					(  self    :  List[str]  ):
							# fmt: off
							lowercase__         = torch.tensor(
							    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
							     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
							     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
							     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
							     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
							     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]  )
							# 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_3680)  )
							self.assertTrue(torch.allclose(input_values[0, 0, :30],    __lowercase,    atol=1e-6  )  )




				def 							A__					(  self    :  Dict  ):
							# fmt: off
							lowercase__         = torch.tensor(
							    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
							     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
							     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
							     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
							     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
							     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]  )
							# 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_3680)  )
							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 							A__					(  self    :  Optional[Any]  ):
							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_1520)  )  # 2 chunks

							# force truncate to max_length
							lowercase__         = feature_extractor(
							    __lowercase,    truncation=__lowercase,    max_length=4_8000,    return_tensors="pt"  ).input_values
							self.assertEquals(truncated_outputs.shape,    (2, 1, 4_8000)  )

							# pad to chunk
							lowercase__         = feature_extractor(__lowercase,    padding=__lowercase,    return_tensors="pt"  ).input_values
							self.assertEquals(padded_outputs.shape,    (2, 1, 9_5280)  )

							# pad to chunk
							lowercase__         = feature_extractor(__lowercase,    return_tensors="pt"  ).input_values
							self.assertEquals(truncated_outputs.shape,    (2, 1, 9_5280)  )

							# force pad to max length
							lowercase__         = feature_extractor(
							    __lowercase,    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(
							    __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_3680)  )

							# 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_3680)  )

							# 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_3680)  )








'''simple docstring'''







from __future__ import annotations






def       lowerCAmelCase__       (				lowerCamelCase     :			list   ):
       _A     :	Union[str, Any]      =					sorted(lowerCamelCase   )
       _A     :	Tuple      =					len(lowerCamelCase   )
       _A     :	int      =					length >> 1
       return (
           (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
           if length % 2 == 0
           else sorted_list[mid_index]
       )






def       lowerCAmelCase__       (				):
       import doctest

       doctest.testmod()


if __name__ == "__main__":
   main()






