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 (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=1.0  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None	):
						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      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    Tuple=7							,       _UpperCamelCase			:    Any=4_0_0							,       _UpperCamelCase			:    List[Any]=2_0_0_0							,       _UpperCamelCase			:    Optional[int]=1							,       _UpperCamelCase			:    Optional[Any]=0.0							,       _UpperCamelCase			:    Any=2_4_0_0_0							,       _UpperCamelCase			:    int=True							,       )				->List[Any]:
									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     snake_case__(					self			:    Optional[int]							)				->Union[str, Any]:
									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									}







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    Any=False							,       _UpperCamelCase			:    Tuple=False							)				->str:
									def _flatten(_UpperCamelCase			:    Union[str, Any]							):
															return list(itertools.chain(*_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(_snake_case							) for x in audio_inputs]

									return audio_inputs






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







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       int						      =    EncodecFeatureExtractor







			def     snake_case__(					self			:    Optional[Any]							)				->Tuple:
									snake_case_       =     EnCodecFeatureExtractionTester(self							)







			def     snake_case__(					self			:    Union[str, Any]							)				->str:
									# 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(8_0_0							,       1_4_0_0							,       2_0_0							)]
									snake_case_       =     [np.asarray(_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(_snake_case							,       _snake_case							,       atol=1e-3							)							)

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







			def     snake_case__(					self			:    Optional[Any]							)				->Dict:
									snake_case_       =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
									snake_case_       =     np.random.rand(1_0_0							).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     snake_case__(					self			:    int							,       _UpperCamelCase			:    Optional[Any]							)				->Optional[int]:
									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(_snake_case							)							)[:num_samples]['audio']

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







			def     snake_case__(					self			:    Dict							)				->List[str]:
									# 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(_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]							,       _snake_case							,       atol=1e-6							)							)







			def     snake_case__(					self			:    Optional[int]							)				->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(_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]							,       _snake_case							,       atol=1e-6							)							)
									self.assertTrue(torch.allclose(input_values[0, 1, :3_0]							,       EXPECTED_INPUT_VALUES * 0.5							,       atol=1e-6							)							)







			def     snake_case__(					self			:    int							)				->Tuple:
									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(
									    _snake_case							,       '''^Both padding and truncation were set. Make sure you only set one.$'''							,       ):
															snake_case_       =     feature_extractor(
															    _snake_case							,       padding='''max_length'''							,       truncation=_snake_case							,       return_tensors='''pt'''							).input_values

									# truncate to chunk
									snake_case_       =     feature_extractor(_snake_case							,       truncation=_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
									snake_case_       =     feature_extractor(
									    _snake_case							,       truncation=_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
									snake_case_       =     feature_extractor(_snake_case							,       padding=_snake_case							,       return_tensors='''pt'''							).input_values
									self.assertEquals(padded_outputs.shape							,       (2, 1, 9_5_2_8_0)							)

									# pad to chunk
									snake_case_       =     feature_extractor(_snake_case							,       return_tensors='''pt'''							).input_values
									self.assertEquals(truncated_outputs.shape							,       (2, 1, 9_5_2_8_0)							)

									# force pad to max length
									snake_case_       =     feature_extractor(
									    _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(
									    _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(_snake_case							,       padding=_snake_case							,       return_tensors='''pt'''							).input_values

									snake_case_       =     feature_extractor(input_audio[0]							,       padding=_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
									snake_case_       =     None
									with self.assertRaisesRegex(
									    _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(_snake_case							,       padding=_snake_case							,       return_tensors='''pt'''							).input_values

									snake_case_       =     feature_extractor(input_audio[0]							,       padding=_snake_case							,       return_tensors='''pt'''							).input_values
									self.assertEquals(truncated_outputs.shape							,       (1, 1, 9_3_6_8_0)							)

									# no pad if no overlap
									snake_case_       =     2
									snake_case_       =     None
									with self.assertRaisesRegex(
									    _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(_snake_case							,       padding=_snake_case							,       return_tensors='''pt'''							).input_values

									snake_case_       =     feature_extractor(input_audio[0]							,       padding=_snake_case							,       return_tensors='''pt'''							).input_values
									self.assertEquals(truncated_outputs.shape							,       (1, 1, 9_3_6_8_0)							)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     5381
						for x in s:
												snake_case_       =     ((hash_value << 5) + hash_value) + ord(SCREAMING_SNAKE_CASE__	)
						return hash_value & 0xFF_FF_FF_FF